Sensors 2014, 14, 5074-5117; doi:10.3390/s140305074
Energy-Efficient Boarder Node Medium Access Control
Protocol for Wireless Sensor Networks
Abdul Razaque * and Khaled M. Elleithy
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604,
USA; E-Mail: email@example.com
* Author to whom correspondence should be addressed; E-Mail: firstname.lastname@example.org;
Tel.: +1-917-889-5975; Fax: +1-203-576-4766.
Received: 5 September 2013; in revised form: 3 March 2014 / Accepted: 4 March 2014 /
Published: 12 March 2014
Abstract: This paper introduces the design, implementation, and performance analysis of the
scalable and mobility-aware hybrid protocol named boarder node medium access control
(BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of
scheduled and contention-based MAC protocols. Like contention-based MAC protocols,
BN-MAC achieves high channel utilization, network adaptability under heavy traffic and
mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC
reduces idle listening time, emissions, and collision handling at low cost at one-hop
neighbor nodes and achieves high channel utilization under heavy network loads.
BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a
boarder node (BN), which is of paramount importance. The BN coordinates with the
remaining nodes within and beyond the region. Unlike other hybrid MAC protocols,
BN-MAC incorporates three promising models that further reduce the energy consumption,
idle listening time, overhearing, and congestion to improve the throughput and reduce the
latency. One of the models used with BN-MAC is automatic active and sleep (AAS),
which reduces the ideal listening time. When nodes finish their monitoring process, AAS
lets them automatically go into the sleep state to avoid the idle listening state. Another
model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the
nodes sense the nature of the environment. Based on the nature of the environment, the
nodes decide whether to use the active or passive mode. This decision power of the nodes
further reduces energy consumption because the nodes turn off the radio of the transceiver
in the passive mode. The third model is the least-distance smart neighboring search
(LDSNS), which determines the shortest efficient path to the one-hop neighbor and also
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provides cross-layering support to handle the mobility of the nodes. The BN-MAC also
incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for
reducing the latency and energy consumption for several WSN application areas to
improve the throughput. BN-MAC uses a unique window slot size to enhance the
contention resolution issue for improved throughput. BN-MAC also prefers to
communicate within a one-hop destination using Anycast, which maintains load balancing
to maintain network reliability. BN-MAC is introduced with the goal of supporting four
major application areas: monitoring and behavioral areas, controlling natural disasters,
human-centric applications, and tracking mobility and static home automation devices
from remote places. These application areas require a congestion-free mobility-supported
MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network
simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium
access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty
cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC).
The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that
outperforms other hybrid MAC protocols in the context of quality of service (QoS)
parameters, such as energy consumption, latency, throughput, channel access time,
successful delivery rate, coverage efficiency, and average duty cycle.
General Terms: design; experimentation; performance; algorithms
Keywords: sensor node; hybrid MAC protocols; BN-MAC protocol; mobility; intelligent
decision-making (IDM) model; automatic active and sleep (AAS) model; least-distance
smart neighboring search (LDSNS); wireless sensor network (WSN)
AAS Automatic Active and Sleep
ADC-SMAC Adaptive Duty Cycle SMAC
A-MAC Advertisement-based MAC
BDIF Broadcast Destinations Inter Frame
BN-MAC Boarder Node Medium Access Control
BNIS Boarder Node Indication Signal
BNVSP Boarder Node Volunteer Selection Process
BSIF Broadcast Source Inter Frame
BT node Bluetooth- enabled Node
Ch-S Channel Sampling
CD Clock Drift
CDMA Code Division Multiple Access
CP Check Period
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CSMA Carrier Sense Multiple Access
DAPS Dynamic Adjustment of Packet Size
EAP Energy Aware-Routing Protocol
EFB Election Flag Bit
G-MAC Gateway Medium Access Control
HRPs Hierarchal Routing Protocols
IDM Intelligence Decision Model
IE Indoor Environment
IOE Indoor and Outdoor Environment
LDSNS Least Distance Smart Neighboring Search
LEI Level of Energy Information
LPR-MAC Low Power Real Time Medium Access Control
MAC Medium Access Control
MPD Maximized Probability Detection
OE Outdoor Environment
ns2 Network Simulator-2
ROC Relative Operating Characteristics
SF Synchronized Frame
SP Short Preamble
SPIN Sensor Protocols for Information via Negotiation
SPIN-EC SPIN via Negotiation Energy-Conservation
SPIN-BC SPIN via Negotiation Broadcast Channel
SPIN-PP SPIN via Negotiation Point-to-Point
SPIN-RL SPIN via Negotiation Reliable Link
UE Unknown Environment
Z-MAC Zebra Medium Access Control
Wireless sensor networks (WSNs) have become an increasingly popular research topic in recent
years. WSNs have produced promising solutions for several applications, such as intrusion detection,
target detection, industrial automation, environmental monitoring, surveillance and military systems,
medical diagnosing systems, and tactical systems . WSNs consist of small sensor nodes
disseminated in a targeted area to monitor the events for collecting the data of interest. WSNs also
experience many challenging problems, including large energy consumption, network scalability,
mobility, coverage, and uniformity . These problems affect the lifetime of the network, increase the
latency, and reduce the throughput. The limited battery life and harsh operating conditions cause
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further complications, which can lead to node failure . Although significant research has been
conducted on WSNs to maintain high communication standards (especially coverage), the issue of
high power consumption remains unresolved . The radio is one of the major power-consuming
sections of the sensor in WSNs that can be handled using energy-efficient medium access control
(MAC) protocols. Several MAC protocols, introduced to reduce the energy consumption, improve the
lifetime of WSNs . Unfortunately, most of the application-dependent  MAC protocols for WSNs
are not energy efficient and thus do not effectively improve the lifetime of WSNs. The protocols
should be scalable to adjust to changes in the network, such as the insertion of new nodes and the
deletion of existing nodes [7,8]. The reduction in energy achieved by the MAC protocols increases the
latency, particularly in multi-hop data communication . These design constraints must be considered
when developing new MAC protocols.
MAC protocols are classified into different categories, such as schedule-based, contention-based,
mobility-aware, and hybrid protocols [10,11], however, many of the contention-based MAC protocols
are based on sensor-MAC (S-MAC), which are designed for specific WSN applications .
Contention-based protocols have free access to acquire the medium . The nodes, which follow
contention-based mechanisms, are not required to follow the cluster. These protocols are network
adaptable to allow for the insertion and removal of sensor nodes from the network. However, in
contention-based MAC protocols, when nodes are available on channel but do not know the activities
(schedule) of each other, nodes do not know when to turn on/off the radio, thus increasing the energy
consumption. Schedule-based MAC protocols are more suitable for reducing idle listening .
However, in such protocols, node problems occur due to the presence of a tight schedule; once a node
misses its schedule, then it must wait for the next turn, thus increasing the energy consumption.
Additionally, schedule-based MAC protocols are not adaptable due to changes in network topology .
Hybrid protocols leverage the characteristics of time division multiple access (TDMA) and carrier
sense multiple access (CSMA) . Existing hybrid MAC protocols are based on the clustering
approach [17,18], where time is divided into different time slots for each node in the cluster. Each
node is responsible for using its own allotted time slot. Clustering reduces the idle listening and
collisions. The transceiver also receives the sleep schedule without any additional overhead. However,
such a mechanism experiences several drawbacks, as discussed in . First, it is critical to determine
an effective time schedule in a scalable manner. A centralized node is often needed to determine a
collision-free schedule. It is extremely difficult to create an effective schedule with channel reuse or a
high degree of concurrency (the ideal solution is NP-hard) . Second, TDMA requires clock
synchronization, which is an important feature of several sensor applications. However, tight
synchronization results in energy overhead because it necessitates recurring message exchanges. Third,
issues may arise due to frequent topology changes resulting from time-fluctuating channel conditions,
such as battery outages, changes in the physical environment, and node failure. Controlling dynamic
topology changes is costly and may even require a global change. Fourth, it is difficult to determine
the intercession relation among neighboring nodes due to different communication and radio
interference ranges from each other and other interfering nodes that may not be involved with direct
communication (this situation is known as interference anomaly) . Fifth, during low contention,
TDMA results in lower channel utilization and increased delays. These problems with TDMA
demonstrate that TDMA is not a reasonable choice when used individually, even if an efficient TDMA
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schedule is used. CSMA is attractive due to its ﬂexibility, simplicity, and robustness. CSMA does not
need considerable setup support, such as clock synchronization and global topology information. The
dynamic joining and leaving of nodes is handled efficiently without additional operations. However,
these benefits may come at the cost of an increased amount of trial and error; a trial may face
collisions when more than two nodes attempt to access the channel simultaneously, causing signal
ﬁdelity to decay at the destination. Collisions can occur in any two-hop neighboring nodes. Although
collisions at a one-hop neighbor node can easily be reduced by using carrier sensing before
transmission, carrier sensing is not controlled beyond one hop. This issue, called the hidden terminal
problem, affects throughput, particularly in high-data-rate sensor applications. RTS/CTS is an
additional method to deploy with virtual carrier sensing in (CSMA/CA). The RTS frame consists of
five fields include frame control, receiver address, duration, FCS and transmitter address. The CTS
frame consists of four fields include frame control receiver address, FCS and duration. Although
RTS/CTS can reduce the hidden terminal problem, it creates high overhead (40%–75%) in channel
utilization due to control packets in WSNs [21,22].
Scalability and mobility are major issues whenever a node changes. Hybrid MAC protocols also
experience inter-cluster communications and require tight time synchronization. These hybrid MAC
protocols also use long preambles (signals used to synchronize transmission timing between two or
more nodes and systems) that consume bandwidth and increase channel utilization . To address
these issues, the BN-MAC mobility-aware hybrid protocol introduces cross-layering support to control
mobility and uses short preamble messages to reduce bandwidth consumption.
Combining CSMA and TDMA and including additional features, BN-MAC is a highly robust
mobility-aware protocol for controlling timing failures, slot allocation failures, time-varying channel
disorder, synchronization, and topological changes. In worst-case scenarios, the performance of
BN-MAC will not be reduced because this protocol needs local synchronization at one-hop
neighborhoods. Our analyses prove that the overall performance of BN-MAC will still be comparable
to other hybrid MAC protocols when clocks are unsynchronized and slot allocation failure occurs.
The remainder of this paper is organized as follows: in Section 2, we discuss the goals, challenges,
and contributions of this research. In Section 3, we present related work on hybrid MAC protocols. In
Section 4, the system model is discussed. In Section 5, the BN-MAC protocol design is presented. In
Section 6, the automatic active and sleep (AAS) model is presented. Section 7 presents the intelligent
decision-making (IDM) model to automatically place nodes into either active or passive mode.
Section 8 describes the simulation setup and analysis of the results. In Section 9, we discuss the results.
Finally, our conclusions are presented in Section 10.
2. Research Goals, Challenges, and Contributions
One of the key goals of introducing BN-MAC is to support the multiple application domains of
WSNs. We focus on several characteristics and factors that affect the performance of existing hybrid
MAC protocols and BN-MAC. Factors that affect energy consumption and scalability include idle
listening, overhearing, congestion, and mobility. The key challenge is determining how to integrate all
of the proposed models to work as a single unit. Mobility is also difficult to address due to limitations
and constraints at the MAC layer for maintaining scalability.
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BN-MAC is proposed as a hybrid protocol involving a contention part and a scheduled part. The
contention part is semi-synchronized  with a low duty cycle that helps to achieve faster access to
the medium and manages the synchronization among nodes. The semi-synchronous feature is
preferable for several application areas to reduce latency and energy consumption and maximize
throughput. Second, the schedule part works with a dual message mechanism. Whenever the sensor
node requires the schedule of its neighboring nodes, the sensor node uses the Anycast message
mechanism because the sensor node can send a control message to only the nearest node in the group
of potential receivers or may choose several nodes, depending on the situation. When the data are sent,
the node uses the unicast message mechanism to forward the same data to all possible destinations. In
addition, the neighbor discovery process consists of a short preamble message that consumes less
energy. The dual mechanism avoids network congestion and increases the lifetime of WSNs. Third,
BN-MAC discovers the presence and level of mobility of the sensor nodes within its neighbors using
the received signal strength indicator (RSSI) and link quality indicator (LQI), both of which are
obtained from the neighbor nodes at the time of synchronization.
BN-MAC performs localized reuse time slot allocation without changing the slots of the nodes that
already exist if the node intends to perform further communication. This feature reduces latency and
control messages and increases throughput. Fourth, new energy level information (ELI) algorithm is
used for the dynamic selection of the coordinator, known as the boarder node (BN). BN dynamically
works as a coordinator (head or leader) on a specific position. BN stays at the position as long as it
uses its sources ―energy‖ for performing some specific task for a definite period inside the network
region then it vacates the position when the energy is reduced for the next node to become BN. In
BN-MAC, the node with the highest energy level in its region will have a large probability of
becoming the BN. BN-MAC approach can handle diverse situations more effectively. Additionally,
three models are included in BN-MAC: AAS, LDSNS, and IDM. AAS is a simple yet efficient model
for solving an idle listening problem. With the AAS model, sensor nodes are forced to go into the sleep
state after performing the events that can prolong the lifetime of the network. This model significantly
outperforms the previous sleep-wake up approaches designed for controlling the idle listening time.
LDSNS is used to determine the shortest distance of the sensor node to one-hop neighbor nodes. The
sensor node does not have the ability to send data over long distances; thus, LDSNS finds a close
one-hop neighbor node to reduce energy consumption and improve the network lifetime.
The IDM model is used to sense the nature of the environment. This ability is critical because the
sensor node is capable of obtaining energy from the Sun, which allows the sensor node to preserve its
battery energy when automating the passive mode in an outdoor environment. The mode of the sensor
node is typically set manually at the time of installation according to the nature of the environment;
however, the IDM model automates the sensor node to reduce the energy consumption and expand the
3. Related Work
Although the deployment of WSNs has highly fascinated academia and industry, WSN platform has
been experiencing several kinds of challenges due to many limitations and constraints. The WSN
performance depends on an efficiency of the MAC protocol. The necessity of multi-featured MAC
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protocol is of paramount importance to handle mobility based scenarios for several real time WSN
applications. The salient features of most related work are discussed. We emphasize some of the
known hybrid MAC protocols. The hybrid protocol named Z-MAC is introduced that integrates the
features of both TDMA and CSMA techniques . In Z-MAC, CSMA is used as a baseline and
TDMA resolves conflicts by scheduling the channel access. The protocol is based on the owner slot
concept. Z-MAC uses novel flexible local time-frame synchronization without global synchronization.
But, it requires the global clock synchronization. Z-MAC also introduces node highest priority scheme.
If any node competes for accessing the channel, then the highest priority based node first gets the
access to the channel. In a highly competitive environment, the node priority scheme decreases the
network congestion. However, Z-MAC experiences latency issues due to the use of long preambles.
Further, Z-MAC has another network adaptability problem because the nodes are tightly scheduled
with each. As a result, Z-MAC decreases the throughput and increases excess energy consumption
during the mobility.
Advertisement-based MAC (A-MAC) hybrid protocol is introduced in  for controlling collision,
overhearing and marginally idle-listening issue. In A-MAC, TDMA is used as baseline while CSMA
improves the channel access. Each node is assigned certain number of time slots within the two-hop
destination. The assigned time slots are used to transmit the data without disturbing the other nodes.
A-MAC also uses an advertisement message that helps the sender to inform the neighboring nodes
regarding its transmission schedule. The major advantage of A-MAC protocol is to inform the nodes in
advance in order to make receiver and sender ready for data transmission. This inclusion avoids the
idle listening and overhearing. However, the overhead of control packets increases the latency and
consumes extra energy. Further, A-MAC is only designed for monitoring the surveillance applications,
but it does not have enough support for mobility and real time communication.
Speck MAC is a deviation of B-MAC protocol . The Speck MAC aimed to reduce energy
consumption and overhearing problem during heavy traffic. However, it consumes extra energy by
sending wake-up frames  and also experiences excess latency. Speck MAC does not support for
the real time and mobility based applications. ADC-SMAC  is an improved version of S-MAC that
adds two new features to S-MAC. First, the node is capable to calculate its energy consumption and an
average sleep time before sending synchronized packets. Second, the node adjusts the duty cycle based
on network conditions then announces its schedule by sending broadcast messages to neighbor nodes.
These two features reduce the energy consumption, but increase latency. Additionally, ADC-SMAC
behaves poorly in mobile environments.
Low-power real-time medium access control (LPRT) protocol is proposed for actuation and
wireless systems using star topology . The LPRT-MAC introduces the super frame concept that
uses mini slots for transmission to the base station. LPRT-MAC reduces the energy consumption when
coordinating with the channel. Star topology avoids the network overhead. However, the LPRT-MAC
performance is limited and not suitable for long multi-hop WSNs. Additionally, it is also not
compatible with other communication topologies. Based on the literature survey of hybrid MAC
protocols, we conclude that the reported hybrid MAC protocols are not good candidates for mobility
and real time applications under congested and heavy traffic network load. To support several mobility
and real time applications, we have introduced BN-MAC protocol that reduces energy consumption and
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improves scalability. BN-MAC also controls the congestion based on LDSNS and energy aware-routing
protocol (EAP)  to maximize the throughput, reduce the latency and prolongs the network lifetime.
4. System Model for BN-MAC
We adopt an ad hoc-based network architecture that comprises sensor nodes with limited power
resources and a BN with more dispensing capability and higher energy. The nodes are scattered to
monitor the different events and activities. The WSN is divided into different regions, with each region
controlled by a BN that coordinates within the given region and adjacent regions. Numerous
economical BT node rev3 sensors are deployed over the battlefield area to provide a high level of
coverage. The BT node rev3 is a self-directed prototyping platform based on a microcontroller, a
Bluetooth radio, and ZigBee. The Bluetooth-enabled sensors cover short-distance communication
among the troops deployed at the nearest positions, whereas ZigBee covers the long distances among
troops. A small number of fixed coordinators obtain accurate positions of their troops as well as the
enemy and their weapons. Each end sensor node is logically connected with a digital addressable
lighting interface controller (DALIC). A DALIC consists of a controller and supports single or
multiple lighting devices. The controller monitors and controls each light by using bi-directional data
exchange. The DALI protocol broadcasts messages simultaneously to the address multiple devices to
find their locations. The DALIC helps to monitor and locate the exact position of the enemy. To
determine the exact location, the DALIC requires an active bat location (ABL) system that
automatically determines the location of the objects.
We also assume that all of the sensor nodes use seismic modality, and each sensor senses different
events during every sampling period using a seismic frequency spectrum. We have considered multiple
issues when designing region-based WSNs for a military scenario. The first consideration is that we
have identified the area of the war and a possible solution. The second consideration is focused on the
deployment issues of the network, such as the location of the sensor nodes determined before
deployment. In this manner, the degree of coverage and connectivity is secured. The nodes are
randomly scattered in the disaster area. To save energy, the nodes typically use short-range and
one-hop communication rather than long-range communication. We use a one-hop destination search
to schedule and deliver data.
We have focused on a combined mobility and static scenario using the ns2 network simulator in the
scenario depicted in Figure 1. Each static and moving object is connected with a command node. The
command node is a heterogeneous node that obtains event information through homogenous nodes
fixed in the field. Similarly, the command node forwards the collected information using the
(homogenous) sensor nodes to the BN. In this scenario, the battlefield is dispersed into different
regions. Each region covers several command nodes that gather information from the events. The
message-forwarding process consists of intra- and intercommunication. Intra communication is used
within the region, whereas intercommunication is used outside of the region. The mode of
communication within the region is based on Anycast communication. Anycast is used to exploit the
knowledge of immediate channel condition in choosing the appropriate downstream neighbor on
smaller time scales. Additionally, the main notion behind MAC layer anycasting is to accomplish the
objectives of network layer, while invoking short-term improvement at the MAC layer, based on the
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local channel settings. Anycast also provides the option of specifying multiple downstream
destinations to the MAC protocol. Anycast allows for increased load balancing to minimize the work
load and complexity of the network for reliable data transfer. Unlike Anycast, multicast increases
latency. Thus, each node stores and forwards the packets to several nodes, resulting in increased
energy consumption. This battlefield scenario requires mobility and scalability. The cross-layering
support of BN-MAC successfully resolves this issue using the pheromone termite (PT) mobility
model. The PT model provides robust and faster routing over WSNs. This model is specially designed
to control the scalability of WSNs and the mobility of nodes. The PT analytical model monitors the
behavior of the WSN using the packet generation rate and the pheromone sensitivity over single and
multiple links . The PT routing model monitors the different activities of the troops and maintains a
faster recovery process using the packet generation rate and pheromone sensitivity. BN-MAC uses the
AAS model to address idle listening in nodes, as discussed in Section 6. The AAS model lets the nodes
go into the sleep state after monitoring and processing the collected information. This approach allows
the nodes to reduce the amount of energy consumed in idle listening. In this scenario, some of the
sensor nodes are deployed in the open battlefield area, whereas some are grounded or fixed to
buildings to monitor different processes, as such situations demand the sensor nodes to act differently.
BN-MAC uses the IDM model to sense the nature of the environment, which allows the mode of the
sensor node to be automatically switched either into the active or passive mode. The IDM model also
reduces WSNs’ energy consumption.
Figure 1. Proposed simulated WSN.
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5. BN-MAC Protocol Design
BN-MAC is proposed with the aim of supporting multiple applications, particularly military
applications, which require mobility-aware and static devices to be controlled from remote places.
MAC design in WSNs is ant involved process because WSNs are based on mechanisms that are
entirely different from the traditional networks. WSNs have limitations due to storage, computational
capability, and energy resources. Therefore, the MAC protocols should be well organized to distribute
the bandwidth fairly and be energy efficient, with appealing features that may stimulate the robust
design of the communication media. One of the key factors for introducing BN-MAC is to reduce
energy consumption while addressing idle listening, overhearing, mobility, and congestion concerns.
BN-MAC also shortens the latency while guaranteeing the reliability of the WSN.
BN-MAC improves the existing Z-MAC, A-MAC, Speck-MAC, ADC-SMAC, and LPRT-MAC
protocols by adding new features. The mechanism of BN-MAC supports the hybrid topology that
combines the features of TDMA and CSMA. The network is constructed as a flat single-hop topology.
The features of TDMA are used to improve the contention, whereas CSMA works as a baseline.
BN-MAC follows the concept of the owner slot. The node has complete access to its owner slot,
similar to TDMA-based approaches. The remaining slots are accessed through the CSMA approach.
The CSMA approach preserves energy and controls collisions. In addition, BN-MAC eliminates idle
listening in each region to achieve a considerable energy saving. Bi-directional traffic inside each
region of the WSN promotes smooth data exchange and efficient use of the bandwidth. Additionally,
BN-MAC uses dynamic contention free slot exchange, which increases network scalability under even
a heavy traffic load.
BN-MAC consists of the following phases: finding the list of one-hop neighbors, intra-semi-
synchronous transmission scheduling, inter-synchronous transmission scheduling, and selection of a
BN. These operations are performed once during the setup process and are not performed again until
the network topology is physically changed. In this approach, the initial costs for running these
operations are balanced while achieving a better throughput and reduced energy consumption during
intra- and inter-transmission.
5.1. Finding the List of One Hop-Neighbors
When a node intends to start communication with its neighbor node after accessing the channel,
the node sends an Anycast message to its one-hop neighbor nodes to obtain the details of neighboring
nodes. This process helps to reduce overhead and manage network load balancing. The process of
sending the Anycast ensures that the intended neighboring nodes are able to talk with each other,
even if they possess different sleeping and communication schedules. The neighbor discovery process
consists of short messages (short preambles), which consume less network bandwidth and improve
Each node randomly sends a short preamble for finding the list of intended neighbor nodes using
Anycast after two seconds for 15 s. This timing is used obtain maximum throughput; packet sending
intervals from 1 to 10 s were considered, but the time interval of 2 s provides the maximum
throughput. We have also set the packet sending time at 15 s to facilitate the successful completion of
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the packet sending process. If we set the time less than or higher than 15 s, then the node energy is
wasted. The node is unable to complete the packet sending cycle when the time is less than 15 s, and
when the time is greater than 15 s, the node comes into the idle situation because after finishing the
packet sending task and thus waits on the channel until the level of set time is reached. We present the
performance of the BN-MAC at different time intervals and packet sending durations in Figures 2 and 3.
A comparison of BN-MAC and Z-MAC, the nodes of which use 30 s for the neighbor discovery
process, indicates that Z-MAC has higher energy consumption.
Figure 2. Throughput at different time intervals.
Figure 3. Packet sending duration versus energy consumption.
The node discovery process in BN-MAC consists of a one-hop neighbor node, but nodes are able to
obtain two-hop neighbor information that is helpful for expanding cross-layering support. The two-hop
information that has been obtained is also used for slot allocation, which enables the node to increase
40 80 120 160 200
TIME INTERVAL (SECONDS)
02 3 4 5 6 7
5 10 15 20 25
PACKET SENDING DURATION (SECONDS)
ENERGY CONSUMPTION (JOULES)
010 15 20 25 30 35
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mobility because the node retains the information even when the two-hop node is moving. BN-MAC is
scalable because the one-hop topological change is easy to handle; each node knows the schedule of
the one-hop neighbor node. BN-MAC uses a promising time scheduler because the assigned slot does
not exceed the one-hop neighborhood. BN-MAC also performs the without changing the time slots of
existing nodes. The localized time slot allocation which is used with channels to synchronize for the
whole network. Otherwise, conflict between different traffic flows can occur. It also helps the node to
gather allocation information for all 1-hop and 2-hop neighbor nodes. This feature of slot allocation
re-use improves throughput and reduces node latency.
5.2. Intra-Semi-Synchronized Transmission Schedule
This mode is based on a semi-synchronized low duty cycle (the ratio between active time and the
complete active/sleeping time; a low duty-cycle MAC protocols obviously has a much extended
lifetime for operation, but pauses for the all-node-active assumption). The intra-semi-synchronized
process starts with channel sampling. The node wakes up for a short period of time to sample the
medium. Channel sampling is performed once during the channel allocation time. After channel
sampling, each node initially sends a short preamble message asynchronously using the Anycast
approach within the one-hop neighbor node to obtain the list of one-hop neighbor nodes. When the
sender receives a reply from the one-hop neighbor nodes, the sender attempts to fix the schedule with
the intended one-hop neighbor nodes (nodes that are chosen for future communication) before sending
the data. Each node knows the wake-up and sleep schedule of its intended neighbors. These dual
features of sending a short preamble asynchronously to obtain the list of neighbor nodes and fixing the
schedule synchronously reduce the network overhead. When the sender completes the scheduling
process with the intended nodes, the sender chooses the shortest efficient path for sending the data
using the LDSNS model, as explained in . This model helps to reduce energy consumption and the
links with the network layer. The use of a short preamble message allows for reductions in overhead
and latency at each hop. The short-preamble-enabled MAC protocols have an advantage over the
long-preamble-enabled MAC protocols due to their low-power duty cycle mechanism. The existing
lower power listening (LPL) technique uses a long preamble and suffers from the overhearing
problem, which increases energy consumption in non-targeted receivers, such as Z-MAC. LPL also
increases latency at each hop . In the long-preamble techniques, the node must wait until the long
preamble is received before it starts receiving data and acknowledgments. This approach increases
energy consumption on both the sender and receiver sides. Targeted receivers are also affected
because the targeted receivers have to wait until the long preamble is received, causing increased
X-MAC uses a short preamble message to reduce the energy consumption and latency, but one
disadvantage of X-MAC is that the destination address of the node is inserted into each short preamble
message. X-MAC forces all nodes to check the preamble to determine whether they are targeted nodes,
which increases energy consumption and the duty cycle (wake-up process). X-MAC is based on an
asynchronous mechanism, and no schedule of neighbor nodes is maintained, making it more difficult
for each node to send data without prior scheduling information. Unlike X-MAC, BN-MAC deploys
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both asynchronous features for sending short preamble messages to obtain the list of one-hop neighbor
nodes and synchronous features for fixing the schedule with the intended neighbors.
The MAC protocol should be capable of handling spatial correlation while also adjusting to changes
in the number of competing nodes . When multiple nodes want to communicate with the same
neighbor node within the region, BN-MAC uses a slotted contention window. Then, the nodes
randomly select a slot in the contention window.
The winner of the slot obtains access to the medium for communication. Thus, there is small
probability of collision at the medium. BN-MAC has more contention slots to compete, which reduces
congestion in the WSNs. BN-MAC has another feature the helps to reduce packet loss. If multiple
nodes attempt to select the same slot, BN-MAC uses sampling and randomization such that each node
has an equal probability of accessing the channel. Furthermore, BN-MAC uses 256 congestion
window slots, whereas the other MAC protocols use 1–32 contention windows for randomized
listening before sending the preamble messages. This increased number of slots reduces congestion
and latency and allows higher throughput to be obtained. We have used different numbers of
congestion window slots, with 30% of the active sensor node contenders allocated to each window
slot. These experiments indicated that BN-MAC produces the maximum throughput when 256 slots
are used, as shown in Figure 4. Similarly, we have checked the performance of hybrid MAC protocols
on their existing window size slots and compared the hybrid MAC protocols with BN-MAC.
Figure 4. Throughput at different congestion window slots.
The simulation results demonstrated that BN-MAC successfully delivers 99.8% of packets, whereas
other MAC protocols only successfully deliver 46%–72.7% of packets, as shown in Figure 5. Hence,
the use of 256 window slots increases the throughput considerably.
Sensor nodes also perform automatic buffering within the region during intra-communication to
reduce the drop rate and prolong the network lifetime. We demonstrate the process of long permeable
(LPL), short permeable (X-MAC), and BN-MAC in Figure 6.
16 32 64 128 256
CONGESTION WINDOWS SLOTS
040 60 80 100 120 140
Sensors 2014, 14 5087
Figure 5. Successful delivery of packets versus event monitoring time.
Figure 6. Comparison of the timelines of duty-cycle MAC protocols.
X-MAC uses a short preamble with a target address to access the channel to communicate with
another node. However, all of the nodes en route will remain awake until the short preambles are
received by the destination node, which results in increased energy consumption. X-MAC also has a
delay of transmission for sending the packets until the receiver wakes up . The BN-MAC protocol
does not use the target address of the node when sending a short preamble message. Thus, all of the
20 40 60 80 100
EVENT MONITORING TIME (MINUTES)
SUCCESSFUL DELIVERY OF PACKETS %
010 15 20 25 30 35
BN-MAC WITH 256 SLOTS
Z-MAC WITH 32 SLOTS
Y-MAC WITH 10 SLOTS
B-MAC WITH 32 SLOTS
EXTENDED WAIT TIME
RX WAKE UP TIME
SP SP SP
LISTEN TIME FOR BUFFER PACKETS
SHORT PREAMBLE WITH
RX WAKE UP SE-
TIME SAVE AT
TX & RX
RX WAKE UP TIME
SHORT PREAMBLE WITH
WITHOUT TARGET ADDRESS
ENERGY AND TIME
SAVE AT TX & RX
Sensors 2014, 14 5088
nodes do not continue to wait; instead, only the intended node wakes up to receive the short preamble.
Thus, each node is in sleep mode for a longer period of time. In addition, BN-MAC uses an automatic
packet buffering process similar to that used in ; this process reduces the wake-up time and
increases the network lifetime. In the automatic buffering process, the node uses a promiscuous mode
that enables the node to listen to all ongoing data traffic and coordinates, if requested. Furthermore, the
node saves a copy of the received packet regardless of the intended destination of the data packet until
receipt of the packet is acknowledged by the destination node. Such buffering requires a relay that is
used by the saturated conditions because each node is able to cooperate in sending data packets to
other buffers. As mentioned above, a short preamble consumes less energy and prolongs the network
lifetime. Let us find the energy consumed for channel sampling and short preamble messages.
The consumed energy for channel sampling is ―Ψ‖, the check period is É, and the average energy
consumed for channel sampling is ―γ‖:
The energy consumed for a short preamble consists of the average energy consumed for channel
sampling, ―‖, and the energy consumed in sending and receiving synchronization, “ω‖:
We use clock drift, ―
‖, which is the time consumed sending and receiving the synchronization,
and ―2ω‖ is the energy consumed by the sender and receiver for synchronization.
During intra-communication, the node that transmits its clock to the one-hop neighbor is called the
parent, and the receiving node at the one-hop neighborhood is called the child. The nodes that are
synchronized with the clock often use a short preamble without the target address of the node that
reduces the energy consumption.
Let us assume that the average energy consumed by the parent and child nodes for one work cycle
is ―‖ and ―‖, respectively. The average short preamble reception time could be reduced because the
receiving node wakes up based on the stored schedule of the neighbor nodes. The average energy
consumed by the parent and child nodes can be obtained as follows:
where ―k‖ is the starting point of the short preamble, ―n‖ is the ending point of the short preamble,
―‖ is the short preamble, ―‖ is the size of the preamble, ―‖ is the nature of the environment,
―‖ is the speed of the short preamble, and ―‖ is the total time spent sending the short preamble.
From Equations (3) and (4), we can obtain the total energy consumed sending the short preamble
during the event monitoring time. Equation (3) represents the energy consumed by the parent node in
sending the short preamble within the one-hop neighbor nodes, whereas Equation (4) represents the
energy consumed by the child node in receiving and sending by the short preamble to the two-hop
neighbor nodes and also acknowledges the parent node.
Sensors 2014, 14 5089
BN-MAC can clearly identify the consumed energy of the short preamble prior to sending the data.
BN-MAC has an advantage over low-duty-cycle long-preamble-enabled MAC protocols and X-MAC.
The reduced energy consumption and time requirements of BN-MAC compared to the other protocols
is shown in Figure 7. Figure 8 presents the superiority of BN-MAC compared with other low-duty-cycle
MAC protocols in terms of time consumed in sending the short preamble to confirm the synchronization
process for forwarding the data.
Figure 7. Energy consumption for BN-MAC and low-duty-cycle MAC protocols.
Figure 8. Channel accessing and data delivery time for BN-MAC and other low-duty-cycle
All of the nodes in BN-MAC maintain the same time frame during synchronization and maintain a
time slot of 0. Each node maintains its own local frame, which matches the frame size of the
neighborhood to avoid potential conflicts while contending with neighbors.
The nodes compete for CSMA equally during the contention phase because the random exponential
back-off (an algorithm that uses response to multiplicatively reduce the node’s frequent access to the
0 5 10 15 20 25 30 35
50 100 150 200 250
TIME IN (MINUTES)
CONSUMPTION OF ENERGY (JOULES)
0 15 30 45 60 75 90 105
2 4 6 810
NUMBER OF SENSORS
CHANNEL ACCESS AND DATA SENDING TIME (SEC)
Sensors 2014, 14 5090
channel dynamically in order to find the acceptable node to access the channel, as the part of network
congestion avoidance) preserves the right of each node to compete fairly for scheduled slots.
Intra-semi-synchronous communication is performed inside the region because BN-MAC is designed
purely for the region-based network, as many WSN application areas require a region-based network.
The intra-semi-synchronized transmission schedule is compatible with all types of radios, such as
CC2420 and CC2500.
5.3. Inter-Synchronized Transmission Schedule
BN-MAC is used with WSNs that consist of different regions. The previous section highlights how
to access the channel and forward the data inside regions. This section explains how to set the
schedules within and outside regions. Each region of the WSN includes a BN. Inter-synchronized
transmission is performed from one region to other regions. The BN receives intraregional data
packets within the region, and the BN forwards the inter data packets outside of the region.
When communicating within the region, the BN first broadcasts three ―hello‖ messages to warn the
nearest region nodes to prepare for receiving the BN indication signal (BNIS). The BN does not wait
to receive an acknowledgment from all of the region nodes. If the BN receives a single
acknowledgment from one of the nearest nodes, it assumes that the ―hello‖ message has been delivered
successfully. Thus, if any node is unable to obtain the ―hello‖ message, the neighbor node informs
other nodes of the schedule exchange time. In this manner, each node knows the BNIS. The BNIS
consists of the current time, the next distribution time, the next collection time, and the schedule for
obtaining intraregional data packets from the nodes of the region, as shown in Figure 9.
Figure 9. Inter synchronized transmission schedule with the region node and BNs.
WAIT TIME BSIF
EIETHER SLEEP OR
BUSY TIME WITHIN
WITH BN NODE
Sensors 2014, 14 5091
The BNIS also has the responsibility of exchanging traffic slots between the source and the
destination and describing the related offset time. Once the BN announces its schedule for the nodes of
the region, all of the nodes are responsible for following the given schedule. At the end of the
scheduled time of the region nodes, the BN synchronizes with another BN of a region to exchange an
interregional synchronous schedule to send and receive data communication. After the contention
period starts, the node responsible for the data exchange requests the schedule-slot for the next
scheduled distribution time.
The nodes only remain active during the BNIS. When the BN intends to communicate with another
BN of a region, the BN begins the interregional synchronized transmission schedule by using carrier
sensing. The BN forwards the message of request-to-send (RTS). In response, the BN receives a
clear-to-send (CTS) message from the BN of the other region. There is no hidden terminal problem in
BN-MAC because the BNs of all regions broadcast the messages to provide each BN with the schedule
of every region. Through this process, all of the BNs know the other BNISs. After receiving the CTS,
the transmitter of the BN forwards the broadcast source inter frame (BSIF) to another region. (BSIF is
the frame used by BN to synchronize for sending the data to another BN of adjacent region). The
receiver BN receives the broadcast destination inter frame (BDIF) during the interval with the CTS
and RTS and acknowledges the received packets (BDIF is the frame received by BN after sending
BSIF frame, it means BN is allowed to send the data to another BN of adjacent region).
We tested the intercommunication performance of BN-MAC and other hybrid protocols in terms of
throughput and average energy consumption. We use varying numbers of transmitting nodes at a low
duty cycle. Figure 10 presents the average energy consumption for each transmitter node, illustrating
that BN-MAC is superior to the other hybrid MAC protocols at a low duty cycle.
Figure 10. Energy consumption during heavy traffic using a low duty cycle.
As mentioned above, BN-MAC has an intra-semi-synchronous transmission schedule that follows
the low-duty-cycle mechanism as well as the inter-synchronized transmission schedule that supports the
low duty cycle under heavy traffic. BN-MAC is also outperforms the Z-MAC, A-MAC, Speck-MAC,
ADC-SMAC, LPRT-MAC protocols at a low duty cycle under heavy traffic. BN-MAC also consumes
0 2 4 6 8 10 12 14
2 4 8 10 12
Number of transmitting nodes
Average energy consumption for each node (Joules)
Sensors 2014, 14 5092
less energy over a heavy traffic load using the low duty cycle. Figure 11 presents the potential increase
in throughput obtained via BN-MAC during heavy traffic at a low duty cycle.
As the number of transmitting nodes increases, the energy spent for each node increases in
competing hybrid MAC protocols compared with BN-MAC. Other protocols consume 18%–45% more
energy than BN-MAC during heavy traffic, mainly because these protocols use many continuous
preamble messages, whereas BN-MAC uses a short preamble to guarantee the efficient delivery of
data. Another reason for BN-MAC’s superior throughput performance is the use of BNs, which have
automatic buffering capacity to store packets instead of discarding them.
Figure 11. Throughput under heavy traffic using a low duty cycle.
5.4. Selection of the BN
The BN is selected periodically based on the BN volunteer selection process (BNVSP), which is
similar to the process used in . The BNVSP chooses the BN based on the available energy and
memory allocation resources. No one node is compelled to declare itself as a BN based on a
probability-based calculation. Each sensor node possesses a different energy level in the region after
monitoring the event at any given time. The node energy level involves several factors, such as the
sleep/wake-up schedule, amount of data received, and amount of data transmitted. The sensor nodes
are actively involved in monitoring the events and forwarding the data of the targeted events to the
BN. This situation leads to the death of the BN before the other nodes that are not actively involved.
Thus, the BN faces a shortage of energy. To overcome this problem, the proposed BNVSP helps to
determine the energy level of each node to select a BN based on the maximum residual energy of the
sensor node in each region. Each sensor node announces its residual energy after completing the event
monitoring process. This maximum residual energy amount determines whether the node should be
considered a BN candidate or not but depends on the residual energy of the sensor node and the
distance from the node to the base station.
The BN is selected based on the energy level using the BNVSP and level of energy information
(LEI) algorithm, as shown in Table 1. The LEI function is used to determine the level of energy for
0 2 4 6 8 10 12 14
100 200 300 400 500
Number of transmitting nodes
Sensors 2014, 14 5093
each node, and the BNVSP is used to select the BN. We categorize the energy of sensors into six
levels is given in Table 1. Algorithm 1 determines energy level for each sensor node.
Table 1. Showing distribution of energy level of sensor node.
Level of Energy
Voltage Level of Sensors
3.3 to 3.7 V
3.0 to 3.3 V
2.7 to 3.0 V
2.4 to 2.7 V
2.1 to 2.4 V
Algorithm 1: Detection process of energy level for selection of boarder node.
1. Set N nodes = Number of nodes
2. Computer VL= Voltage level
3. If (VL >= 3.3 && VL <= 3.7) then
Set VL = EL
Declare EL = Very high
4. if (VL >= 3.0 && VL <= 3.3 then
Set VL = EL
Declare EL = High
5. if (VL >= 2.7 && VL <= 3.0) then
Set VL = EL
Declare EL = High moderate
6. if (VL >= 2.4 && VL <= 2.7) then
Set VL = EL
Declare EL = Moderate
7. if (VL >= 2.1 && VL <= 2.4) then
Set VL = EL
Declare EL = Low
8. else (VL <= 2.0)
Set VL = EL
Declare EL = Lowest
9. end if
When the energy level of the BN that is already working decreases, the responsibility is shifted
from one BN to another BN using the election flag bit (EFB), a signal alert sent by BN in the network
for the election of new BN when decreasing its energy level. The EFB specifies the process of the
immediate BN election. The proactive method is used to select the next BN to reduce the overhead
associated with this process. The base station broadcasts a short preamble message to each WSN node.
Each node calculates its distance from the base station based on the signal strength. The node that
Sensors 2014, 14 5094
receives a short preamble becomes a candidate for the BN. Other factors are also considered when
selecting the final BN, including the energy of the BN consumed during the contention time for
election (comparison time of the energy level), the energy consumption of the sensor node in each
state, and the time spent in each state and the transmitted data at each step. All candidate BNs check
their radio range and residual energy. The radio range is selected by the short preamble sent by the
base station, and the residual energy is selected using the BNVSP, as shown in Table 1. The residual
energy level for choosing the BNs is determined as follows:
where is the energy consumed by each sensor node in each state and is the time spent in each
state. Thus, 1 is ―radio on‖ for receiving traffic, 2 is ―radio off‖, and 3 is transmitting the data packets.
Let us determine the residual energy level of each sensor node. ―‖ is the residual energy level of
each sensor node after performing the event, so we apply the following derivation:
where is the current energy level of the sensor, V is the voltage level of the sensor node before the
event, is the number of contention window slots, which is equal to , and is the number of hops
required to travel the data, and , where is the distance between each hop of the WSN.
If the sensor node completes the event, then the sensor node decreases its energy level. Therefore,
the energy used in the event is equal to the difference between the sensor node’s final and initial
where is the remaining sensor energy after the event, is the initial energy of the sensor, is
the final energy of the sensor, and and are the initial and final energy levels, respectively.
If part or all of the consumed energy in the sensor node is renewable , then the new energy of
the sensor node
can be found as follows:
We obtain the energy of the sensor node by substituting for
Therefore, the level of energy can be expressed as:
Sensors 2014, 14 5095
The current BN also sends a signal for a new election when the battery is running down.
After the election process, the new BN resumes its duty and the current BN terminates its function.
In case of BN failure, the remaining nodes wait for four consecutive BNISs, and the BN is
subsequently considered a malfunction. The new BN is automatically selected, allowing network
disturbances to be avoided. We illustrate the complete mechanism of BN-MAC in Figure 12.
Figure 12. Message mechanism of the hybrid BN-MAC protocol.
6. Automatic Active and Sleep (AAS) Model
The node is periodically set into the sleep state in the duty cycling protocol . The node can
maintain a tradeoff between data latency and energy consumption by fixing the state of either sleep or
wake-up automatically. The node consumes less energy at a higher latency for data delivery with a
lower duty cycle. Once a node wakes up during its active duty cycle time, it should listen to
the channel for a specific period of time to determine whether other nodes are available for
communication. This situation creates difficulties and increases the overhead of the MAC protocol due
to idle listening, which is a major source of energy consumption. The nodes continue to monitor the
channel for incoming traffic, which increases energy consumption. Some of the WSN applications
require transfer at a low data rate, but the sensor nodes remain idle for a longer period of time after
performing their specific events. It is not advisable to keep the sensor nodes in the idle state for a
significant period of time. Thus, the AAS model is integrated with BN-MAC to shorten the
unnecessary idle listening time. Sensor nodes normally operate in two modes: ON and OFF. If the
TX Ch-S CS
Intra Data frame Time
Intra data frame Carrier sensing
(SP) Channel Sampling
RX Ch-S Ch-S ACK
Intra Data frame
(CTS) Broadcast Source Inter
Inter Frame (BDIF)
Sensors 2014, 14 5096
transceiver of the sensor node is active ―ON‖ without performing any events, time delay and wasted
energy result . The unnecessary waste of energy can be reduced if the transceiver of the sensor
nodes is controlled using the ―OFF‖ state. We use the AAS model by setting the threshold value for
the ―idle‖ and ―OFF‖ states to save energy.
Let us assume that the energy consumed in the idle time of the sensor nodes should always be less
than or equal to the ―OFF time‖. Then , where is the consumption of the energy
during the idle time and is the energy consumed during the OFF time.
Let us assume that is the minimum energy required for the sensor nodes to remain in the
idle state, is the energy consumed by switching from the idle state to the ―ON‖ state, and
is the energy consumed by switching from the idle state to the ―OFF‖ state. Thus, the energy
consumed in idle time can be computed using Equation (12):
Let us assume that is the minimum amount of time required for the sensor nodes to
consume energy by going from the ―ON‖ state to the ―OFF‖ state, is the time required for the
sensor nodes to consume energy by going from the ―OFF‖ state to the ―ON‖ state, and is the
energy saved by the nodes when they are in the sleep state. Thus, the total ―OFF‖ time energy can be
calculated using Equation (13):
Let us assume that , the total energy consumed during the ―OFF‖ state, is larger than the
energy consumed in the idle state, which was already proven and is given in Equation (14):
Our goal is to transition the sensor nodes into the sleep state if no event is underway. Equations (12)
and (13) indicate that the states of operations in the sensor nodes can be established automatically.
Let us set the transition states and β (beta) for sleep (OFF) and active (ON), respectively.
An automatic change of transitions can be justified if Equation (15) is satisfied:
where is the energy consumed during the active time and is the negligible amount of
energy consumed by going from the active (OFF) state to the sleep (ON) state. Thus, is greater
than or equal to the amount of energy consumed in the active and idle states minus the energy
consumed during the total ―OFF‖ time.
The above model indicates that the energy consumption due to idle listening can be avoided.
In BN-MAC, each node remains in the sleep state until the next data-sending schedule begins. The BN
also announces its schedule; therefore, there is no probability of consuming energy. There is also no
hidden terminal in BN-MAC. BN-MAC requires 832 µs to send a 14-byte BNIS message that
produces a 0.3% duty cycle; other hybrid MAC protocols require an average of 1,209–1,532 µs to send
each BNIS message that produces an average of 0.52–0.78 duty cycles, as shown in Figure 13. Thus,
BN-MAC reduces the overhead by using fewer BNIS control messages and a synchronization message.
Sensors 2014, 14 5097
Figure 13. Time consumption for sending a BNIS message with BN-MAC and other
7. Intelligent Decision-Making (IDM) Model
We use the IDM model to increase the efficiency of BN-MAC. This model decides the nature of the
environment, i.e., whether the environment is indoor or outdoor. The IDM model forces the sensor
nodes  to work on either the passive or active mode of communication in response to the nature of
the environment. The IDM model helps to reduce energy consumption in both modes but particularly
in the passive mode. The sensor nodes working in the passive mode do not consume the energy of their
battery but may instead harvest energy, such as solar energy from the environment.
Let us assume that ―K‖ is the number of sensor nodes available in the WSN, which are deployed to
detect the presence or absence of the indoor and outdoor environment (IOE). K sensor nodes collect
information regarding the IOE and then determine the nature of the environment. We have set values
for the IOEs.
If Di ≥ 1 indicates the presence of an indoor environment (IE), then Di < 1 indicates the presence of
an outdoor environment (OE). To prove ―IE‖ and ―OE‖, we also use a third environment, i.e., an
unknown environment (UE). The detection process is based on the maximized probability of detection
(MPD) method used by the Neyman-Pearson Lemma .
The K sensor nodes start the detection process from the UE because they are initially unaware of
the nature of the environment. We set the probability of ―UE‖ and pick a random variable that denotes
the constraint of the optimized problem in the form of UE = α (alpha), as shown in Equation (16):
One of the requirements for statistical optimization is establishing an expected value of UE. Hence,
we maximize the expected value of the probability to detect UE with respect to the constraints of the
expected value of the probability:
0.2 0.4 0.6 0.8 1
TIME FOR SENDING BNIS (MICRO SECONDS)
AVERAGE DUTY CYCLE FOR SENDING BNIS %
0600 900 1200 1500 1800 2100
14 BYTES BNIS
Sensors 2014, 14 5098
Substitute the value of in Equation (16) to find the UE that will be easier for the sensor
node to sense out of IE and OE.
and are linked by the relative operating characteristics (ROCs) that are required to
determine UE (ROCs are a strong method to validate probability estimates and particularly to compare
its performance with deterministic estimate. it is two-dimensional process). We use the following
probabilities to detect UEs and IEs:
UEi = P (Di < 1|IOE outdoor), βi = P (Di < 1|IOE outdoor), and PDi = P (Di > 1|IOE indoor), γi = P
(Di > 1|IOE indoor).
Let us assume that the sensor nodes detect the environment independently. Thus, K sensor nodes
detect UE based on the set probability values:
Di =1 indicates that the passive mode is initiated and the K sensor nodes reduce energy
consumption. If Di ≥ 1 or Di ≤ 2, then the environment is known, and the sensor nodes stop using the
energy of the battery and activate the passive mode to obtain energy from the environment. This
condition indicates the presence of an OE.
The reduction in energy consumed can be calculated as follows:
Let us assume that EN(X) and EN(Y) are the total energy saved by two different regions of the
WSN. E(i) and E(j) indicate the energy saved by nodes i and j during transmission, respectively. Thus,
we can define the total saved energy of the WSN using Equation (7):
where Tsaved is the total amount of saved energy.
If Di < 1, the active mode is activated. If Di ≥ 0 or Di < 1, then the IE is active, and the sensor
nodes use the battery and external energy. The amount of energy consumed is calculated using
Sensors 2014, 14 5099
Therefore, we measure the total saved and consumed energy of the WSNs using passive and active
modes based on the OE and IE. We also prove the energy saved using the WSNs using Lemma 1.
Lemma 1. Bluetooth-enabled sensor nodes follow the energy preservation process during the passive
mode using the integration method.
Here, we present the numerical time integrators that allow energy P(e) to be preserved. We begin by
assuming an x-point quadrature formula with nodes Ni. The required weight of ai is obtained through
Lagrange basis polynomials in interruption as follows:
Let a1, a2, a3,…, ax be different real numbers (0 ≤ Ni ≤1) for which ai ≠ for all i. Note that all
the values for different real numbers cannot be equal to ai. We use the polynomial p(d0) for satisfying
The quadrature formula with nodes Ni and weights ai decreases the integrator to a specific
collection of methods. We use polynomial degree 2x − 1; thus, Gauss points Ni are equal to 0 and
shifted with the Lagrange polynomial specific collection for A(x).
This formula treats arguments in A(x) and differently than a partitioned numerical method.
The solution obtained with these methods depends on the specific factorization of the vector field.
If A(x) = A is a constant matrix, let (1, 1) be a Hamiltonian system (a dynamic system used for the
mathematical formalism to define the evolution equations. It also provides the significant understanding
about the dynamics, even if the preliminary problem’s value cannot be solved systematically). Thus,
the Hamiltonian system becomes an energy-saving integrator. This result demonstrates that the sensor
nodes also consume a minimum amount of energy during the passive mood.
8. Simulation Setup and Analysis of Results
Real WSN environments use low-power radios because of their high asymmetrical communication
range and stochastic link characteristics. Simulation results could be slightly different from realistic
experimental results . If we make simple assumptions regarding wireless radio propagation, then
the simulation results could be significantly different from realistic wireless radio features and diverse
transmission power. It is critical to select a simulator that produces results that are reasonably close to
the real environment. Thus, for our experimental simulation setup, we use ns-2.35-RC7 because it
produces results that are highly similar to real environments.
In our experiments, the WSN is disseminated into N regions to collect information more quickly.
We have simulated different realistic mobility- and static-based scenarios. The main goal of the
Sensors 2014, 14 5100
simulation is to evaluate BN-MAC and compare it with other hybrid protocols, including Z-MAC,
A-MAC, Speck-MAC, ADC-SMAC, and LPRT-MAC.
The simulation scenarios consist of 105 nodes with a 30-m transmission range. The sensor nodes
are uniformly and randomly placed in a geographical area of 300 × 300 m2. The area is divided into N
number of 75 × 75 m2 regions. The initial energy of each sensor node is set to 40 J. The bandwidth of
the nodes is 50 kb/s, and the maximum power consumption for each sensor node is set at 16 mW. The
sensing mode is 12 mW. Each sensor is capable of broadcasting the data at a power intensity ranging
from −20 to 12 dBm.
The total simulation time is 35 min, and the pause time is set to 30 s during phase initialization at
the start of the simulation. During this phase, the BN is in the warm up phase, and the remaining
sensor nodes are automatically in power-saving mode. The presented results are an average of
10 simulation runs. The simulation parameters are illustrated in Table 2.
Table 2. Parameter values used in the simulation WSN.
BT node sensors
Sensing Range of node
Initial energy of node
Bandwidth of node
Number of sensors
105 BT node rev-3
Size of network
300 × 300 square meters
Size of each region
75 × 75 square meters
Packet transmission rate
Data Packet size
4, 8, 16, 32, 64, 128, 256 bytes
Pheromone termite mobility model
Initial pause time
Energy dissipation: actuation
−20 to 12 dBm
Minimum Cycle time, T
Start time of BN-MAC
(0, 30) s
Sink location in each region
Other MAC protocols
Z-MAC, A-MAC, Speck-MAC, ADC-SMAC, LPRT-MAC
Type of protocols
IDM, LDSNS and AAS models
0.5 to 3.5 m/s
Delivery of data at varying sensing range
100, 200, 300, 400, 500, 600 and 700 m
Sensors 2014, 14 5101
8.1. Network Coverage Efficiency
We conducted several simulation tests from different perspectives but with a particular focus on the
network coverage efficiency after deploying 1 to 105 sensor nodes.
Network coverage can be regarded as how efficiently WSNs monitor the targeted area of interest.
Network coverage can be considered a measure of the quality of service (QoS). Network coverage
efficiency is measured in different ways depending on the nature of the applications and what is being
monitored. The coverage is also crucial for maintaining the connectivity, which is defined as the
capability of the sensor nodes to reach the base station. To measure the network coverage, we have
created 15 sessions simultaneously to determine the actual behavior of the network using highly
congested network scenarios. The simulations indicated that BN-MAC achieved a network coverage of
99.8%, whereas Z-MAC, A-MAC, Speck-MAC, ADC-SMAC, and LPRT-MAC achieve network
coverage of 50%–87%, as shown in Figures 14 and 15.
Figure 14. Coverage efficiency of the WSN using a different number of sensor nodes.
Figure 15. Coverage efficiency of the network at different intervals.
0 15 30 45 60 75 90 105
0.2 0.4 0.6 0.8 1
Number of Sensors
Coverage efficiency of WSN
0 5 10 15 20 25 30 35
0.2 0.4 0.6 0.8 1
Coverage efficiency of WSN
Sensors 2014, 14 5102
BN-MAC achieves higher network coverage due to its compatibility with homogeneous sensor
nodes (region nodes and BNs). The homogeneous set of nodes with a deterministic positioning
attempts to guarantee the network coverage and connectivity with a minimum number of sensor nodes.
The nodes are distributed in the targeted area of interest into regions to determine where to deploy the
The limited energy resources must be used efficiently when choosing the BN because the BN is one
of the major nodes in each region selected based on the presence of a high energy level using the LEI
algorithm, which improves the connectivity of the WSN for a longer period of time. Furthermore,
sensor nodes must be transitioned into the sleep mode using the AAS model while conserving energy
to adjust the transmission range properly so that the sensor nodes may use the minimum amount of
energy needed to communicate with the BN and neighbor nodes. The performance of BN-MAC is also
improved because the one-hop neighbor node searches are optimized using LDSNS so that the data
can be forwarded to the base station using the shortest and most efficient path. Energy is preserved by
alleviating the routing load on some sensor nodes. By reducing the energy consumed via data routing,
the network coverage is improved by prolonging the lifetimes of the sensor nodes. The minimum
number of sensor nodes that are required to cover the entire network can be calculated as follows:
where Nmin(s) is the minimum number of sensor nodes required to cover the entire area to maintain
connectivity and coverage and ―r‖ is the sensing range of the sensor.
Let us assume that the sensing range is smaller than the dimensions of the monitoring area.
is the maximum number of sensor nodes, and ―R‖ is distance of the entire network.
is the upper bound on ―R‖, and Nmin(s) is the lower bound on Si,
where Nmin(s) =
Proof: Let the upper bound on ―R‖ be linear, with the maximum number of sensor nodes (total
number of sensor nodes) equal to Nmax(s). The lower bound on ―Si‖ is invariant with Nmax(s). In addition,
these bounds are not considered tight as long as they do not consider the transmission radius ―Tr‖ of
the sensor nodes. However, a more accurate heuristic solution is required to follow these bounds
closely regardless of changes that occur in the network parameters. Hence, the lifetime of the network
is linearly asymptotic with Nmax(s), and thus, ―Si‖ will be constant with Nmax(s).
Figure 16 demonstrates the network lifetime using BN-MAC and the other hybrid MAC protocols.
BN-MAC outperforms the other hybrid protocols because the other hybrid protocols are not capable of
achieving the same network lifetime with an increased number of nodes. The network lifetime depends
largely on the battery lifetime of the sensor node. The major concern is to extend the lifetime with
respect to energy limitations. One way of extending the lifetime of the sensor nodes is to turn off redundant
nodes and let the redundant nodes go into the sleep state to conserve energy. Our coverage-preserving
BN idea reduces the energy consumption and therefore increases the system lifetime. BN-MAC has
the ability to manage traffic and reduce the idle listening time. The BN-MAC mechanism consists of a
semi-synchronous approach that helps to reduce the channel accessing time. BN-MAC also uses a
Sensors 2014, 14 5103
short preamble message for accessing the channel without an integrated destination address in each
preamble that reduces energy consumption and prolongs the network lifetime.
Figure 16. Lifetime of MAC protocols using a different number of sensors.
We illustrate the WSN lifetime using BN-MAC and competing hybrid MAC protocols as shown in
Table 3. Lifetime of hybrid MAC protocols over WSN.
Name of MAC
Figure 17 presents the average packet delay at different packet generation rates using a fixed
mobility of 1 m/s. The average delays of BN-MAC and Z-MAC are considerably less than those
of A-MAC, ADC-SMAC, LPRT-MAC, and Speck-MAC. BN-MAC and Z-MAC have a low level of
latency due to the use of a short preamble. The sensor nodes in BN-MAC use three directions (down,
up, and local) to transmit data to neighbors according to whether the nodes are 1-hop closer, 1-hop
farther, or at the same hop distance, respectively. When a sensor node has data to send, the sensor node
first senses the channel to confirm whether the channel is free. If the channel is free, the sensor node
transmits a short preamble message without a destination address because the destination address
consumes the excess network bandwidth and reduces the network connectivity. We include
transmission, propagation, and processing delays that help the preamble message to arrive at the
required node during the channel polling time that also guarantees delivery of the data packets to the
sensor node. The preamble transmission also overcomes the problem associated with small drifts in the
0 15 30 45 60 75 90 105
100 200 300 400 500
Number of sensors
Unicast: Network lifetime VS Number of sensors (64 Bytes packet length)
Sensors 2014, 14 5104
clocks. Packet transmission starts when the transmission of the preamble ends. BN-MAC has
automatic buffering because each node waits for the first packet to arrive, after which the remaining
packets are buffered automatically to shorten the average packet delivery delay. The semi-synchronous
mechanism is one of the most significant characteristics of BN-MAC because the semi-synchronous
mechanism reduces the average packet delay.
Figure 17. Average packet delay at different intervals.
Figure 18 presents the average packet delay of BN-MAC and other participating protocols at
different mobility rates. BN-MAC can manage its timeframe, number of random access frames, and
rate of transfer frames while maintaining a nearly constant average delay. In contrast, Z-MAC (and
other competing hybrid MAC protocols) does not have the mobility support, and thus, the average
delay is increased. BN-MAC receives routing support from the EAP protocol at the network layer,
which also helps to minimize the time needed for path discovery and route maintenance.
Figure 18. Average packet delay at different mobility levels (speed in m/s).
3 6 9 12 15 18 21 24
Packet generation interval (Seconds)
Average delay (Seconds)
Unicast: Network lifetime VS Number of sensors (64 Bytes packet length)
0 05 1 1.5 2 2.5 33.5
Average delay (Seconds)
Sensors 2014, 14 5105
Figure 19 presents the number of packets delivered by BN-MAC and other protocols using variable
packet sizes. BN-MAC delivers more packets than the other protocols. BN-MAC uses a balanced
semi-synchronous schedule between the neighbor nodes. A semi-synchronous schedule helps to reduce
energy consumption. Thus, the node energy exhibits a sharp decrease as the packet size exceeds an
optimal length. This trend can be attributed to the maximum overhead, which increases the average
re-transmission and thus decreases throughput. As the packet size increases, the exposed interval and
probability of an interfering node increase. When BN-MAC uses 256 contention windows to avoid
interfering nodes, there is a marginal likelihood that the packets will be dropped. In this manner, the
size of the packets does not decrease the performance.
Figure 19. Number of packets delivered with variable packet sizes.
BN-MAC also uses the dynamic adjustment of packet size (DAPS) function, which handles the
variable size of the packets. Thus, there is a marginal likelihood of packet re-transmission. BN-MAC is
also advantageous in terms of sampling and randomization, thus avoiding the packet loss. The other
MAC protocols use 1–16 contention windows for randomized listening before sending their preamble
message. BN-MAC configures the contention window to 256 slots. Thus, there is small probability of
dropping the data packet because only 5% of the nodes may choose the same slots at the same time.
Figure 20 presents the energy consumption for 20,000 variable-length packets delivered and
acknowledged using BN-MAC and other hybrid MAC protocols. BN-MAC consumes less energy than
A-MAC, ADC-SMAC, LPRT-MAC, Speck-MAC, and Z-MAC. The variable size of the packets does
not significantly affect BN-MAC due to the use of the DAPS function to handle the variable packet
lengths. Less energy is consumed because the idle listening time is controlled, as the sensor node
consumes the maximum amount of its energy without performing actions on the channel. The AAS
model brings the sensor node into the sleep state after the event processes are no longer being monitored.
Thus, the AAS model helps to maintain the fairness of the energy in the network during events.
In Figure 21, we present the duty cycle for BN-MAC and other hybrid MAC protocols at different
sensing ranges. BN-MAC exhibits a low duty cycle, whereas the other MAC protocols exhibit a higher
duty cycle. The capacity to send packets at faster rate is affected as the sensing range is increased. In
duty cycling, the node is periodically placed into the sleep state, which is effective for decreasing the
0 4 8 16 32 64 128 256
5000 10000 15000 20000 25000
Size of Packets (Bytes)
Number of Packets
Sensors 2014, 14 5106
energy dissipation in the network. In BN-MAC, energy is saved to bring the sensor node into the sleep
state using the AAS model and a semi-synchronous technique. The packet adjustment-based duty cycle
feature of BN-MAC also effectively reduces energy consumption without significantly reducing
throughput and increasing latency. Other participating MAC protocols take an even longer period of
time to access the channel and deliver the packets, thus increasing the energy consumption. As a result,
the sensor node consumes additional energy when sending larger control messages, which consume
40%–70% of the network bandwidth. Thus, there is not a sufficient amount of power remaining in the
other MAC protocols to send data for longer distances. For example, when the sensing range is 700 m,
the duty cycle of BN-MAC is approximately 11%–12%, whereas A-MAC, ADC-SMAC, LPRT-MAC,
Speck-MAC, and Z-MAC have duty cycles of 20%–29% because it takes a longer period of time to
access the channel and forward the data packets.
Figure 20. Energy consumption with variable packet sizes.
Figure 21. Average duty cycles at variable sensing ranges.
0 100 200 300 400 500 600 700
10 20 30 40 50
Sensing Range (meter)
Average Duty Cycle (%)
Sensors 2014, 14 5107
8.2. Broadcast Traffic
We evaluated the performance of BN-MAC and other hybrid MAC protocols under broadcast flood
traffic. In this experiment, we measure the strength of the BN when floods are first sent to other regions.
Figure 22 presents the packet delivery rate for BN-MAC and the other competing MAC protocols
under broadcast flood traffic. The packet delivery ratio of the BN is calculated as the total number of
flood messages received from all nodes and delivered to other regions, which is divided by the total
number of distinctive messages generated by all nodes. Each message of the node consists of a
sequential number to find the uniqueness of the message. The simulation demonstrated that BN-MAC
outperforms all of the other hybrid MAC protocols.
Figure 22. Delivery ratio under broadcast flood traffic.
The BN-MAC curve is considerably higher than those for the other curves because the delivery
ratio remains stable with the different network traffic floods. The high delivery rate is maintained
because latency and idle listening are controlled. BN-MAC outperforms the other MAC protocols in
BN-MAC also avoids network congestion using a congestion window with 256 slots, whereas
A-MAC, ADC-SMAC, and LPRT-MAC do not have the ability to support simultaneous transmission,
thus causing collisions. As a result, the same message is re-transmitted multiple times, and the packet
delivery rate is reduced dramatically.
Figure 23 presents the latency of BN-MAC and the other hybrid protocols at different hops and
traffic flows. BN-MAC provides uniform latency at various hops and different numbers of flows. The
BN-MAC mechanism uses Anycast for scheduling and unicast for sending data at the one-hop
neighbor node, which helps to improve the throughput and reduces the latency. There is also an
extremely small probability of failure of a one-hop path. If a one-hop path fails, then a second
alternative best one-hop path is chosen for intraregional data communication based on the stored
information for the one-hop neighbor nodes. The mechanisms of the other MAC protocols support a
multi-hop path technique. If one path fails, then it is difficult to immediately regain a path. Thus, the
0 1 2 3 4 5 67
0.2 0.4 0.6 0.8 1
Delivery Ratio %
Sensors 2014, 14 5108
latency is increased, and the throughput decreases. Overall, BN-MAC has a low latency and
outperforms the other hybrid MAC protocols.
Figure 23. Latency of BN-MAC and the other hybrid protocols using different hops and
8.3. Minimum Path Detection Time for Efficient Route
Routing in WSNs is usually assorted due to several limited constraints. The network performance
depends on flexibility of routing protocol. From other side, an effective energy-efficient routing
protocol design is big challenge for energy-constrained network . In this experiment, our aim to
choose proper routing protocol from existing routing protocols that should be compatible with
BN-MAC features to create robust WSN. The suitability of routing protocol generally depends on
application requirements because routing protocols maintain and discover the routes in the network.
The function of routing protocols extend network lifetime while maintaining the high-quality of
connectivity and allowing the reliable communication between nodes.
The sensor nodes are not accessible in some conditions because they are either located on the
unreachable points or undergrounded for sensing the events. Hence, immediate human access to those
sensor nodes is not possible . Therefore, routing protocols should be mobility aware to deal with
WSN applications’ node mobility, event mobility and sink mobility.
Let us identify the routing protocol that should be more suitable with BN-MAC. Hierarchal routing
protocols (HRPs) categorize the nodes based on their functionality. Nodes are divided into groups or
clusters, and head node is selected to coordinate with inside and outside of the cluster .The HRPs
are proposed to increase network lifetime. However, HRPs are not using multi-hop communication. As
a result, HRPs can be used with BN-MAC because BN-MAC mechanism supports single hop search.
Attribute or data-centric based is another category of routing Protocols that is named as sensor
protocols for information via negotiation (SPIN). These routing protocols distribute the information
among the sensor nodes using energy-constrained efficiently . The base of SPIN communication
nodes depends on specific knowledge of application. SPIN allows sensor nodes to disseminate
1-hop 2-hop 3-hop 4-hop 6-hop-4
2 4 6 810
Multihop Traffic Flow
Delivery Latency (Seconds)
Sensors 2014, 14 5109
information using less energy resources efficiently. Four types of SPIN protocols are available:
SPIN-EC and SPIN-PP are used for point-to-point network, and another SPIN-RL and SPIN-BC are
appropriate for broadcast network traffic and also providing 1-hop destination search. SPIN-RL does
not provide optimal route at 1-hop destination, but helps to improve the search capability.
Additionally, the mechanism of data advertisement of SPIN-RL is not highly guaranteed for reliable
delivery of data. Energy aware routing protocol (EAP) is the energy efficient that uses sub-optimal
routes to enhance the network lifetime. In EAP, single efficient path is chosen from many multiple
paths to preserve energy. EAP has also priority over directed diffusion routing protocol family because
EAP improves network performance and saves energy 21.5% to 44% .
We choose EAP protocol based on its compatible features with BN-MAC. EAP works in
combination with the LDSNS model to find optimized 1-hop shortest paths (the LDSNS model is used
to choose the best efficient one-hop neighbor node to establish the path to the destination node.
LDSNS reduces energy consumption while choosing an efficient route to path).
EAP helps to maintain resource awareness, and improves the network lifetime. EAP also possesses
some hierarchal features, which can support to BN to coordinate with intra and inter data transmission
efficiently. BN-MAC with EAP maintains data aggregation that helps BN to coordinate and
communicate without any reservation over WSN.
Figure 24. Path detection time for different number of hops.
In this experiment, we have used WSN consisting of 16 hop-destination with 15 concurrent
established sessions. If, we analyze the Figure 24, it is observed that time for maximum hop number is
calculated 0.8 s for BN-MAC whereas it takes from 2 to 3.5 s for other participating hybrid MAC
protocols. The competing hybrid MAC protocols have used their original underlying routing protocols.
The designed WSN for BN-MAC protocol is composed of regions. Each region consists of several
sensor nodes that are controlled and coordinated by BN. In this experiment, BN broadcasts the control
message while setting the paths for data transmission. The broadcasting message process consumes
enough energy amount but sensor nodes lack the adequate energy resources. Thus, BN-MAC saves
0 2 4 6 12
0.5 1 1.5 22.5
Number of Hops
Path Detection Time (Seconds)
Sensors 2014, 14 5110
energy to use low duty cycle semi synchronous mechanism and AAS model, which also control the
idle listening issue at the MAC level. From other side, EAP chooses single efficient path from group of
multiple paths to save energy.
Figure 25. Broken paths% at different intervals.
Figure 25 shows broken routes during entire simulation time. BN-MAC is superior to competing
hybrid MAC protocols throughout the entire simulation time. The competing MAC protocols
experience the problem due to use of their original routing protocols. As a result, those protocols took
enough time for route discovery. The route discovery time could be longer in some critical
circumstances. Further, it is also easier to discover the route in WSNs based on single hop discovery
process. The single-hop discovery process can handle the scalability and maintain the network
mobility efficiently . Path detection time for each hop is varied because it depends on the density
of nodes that can be calculated as follows:
Let us assume that is the probability density function (a function that defines the comparative
probability for the random variable to yield desired value; it is usually associated with absolutely
continuous univariate distributions). H [Nr] is the number of hops in network and is the length of
network. Thus, the value of can be calculated as follows:
where: ―‖, distance from source node ―‖ to destination node ―‖.
Substituting the value of and we get as:
Simplifying (29), we get:
0 5 10 15 30
0.02 0.04 0.06 0.08 0.1
Time for Simulation (Seconds)
Path Broken % throughout Simulation
Sensors 2014, 14 5111
Determining the discovery time for broken links at each path, we need to consider the number of
hops, network size and velocity of each node.
Where: , time for maximum number of hops; ), total network area and ―‖ is
corresponding velocity of each node.
Therefore, consumption time for maximum number of hops can be calculated as follows:
Substitute the values of then we can get as follows:
Substitute the value of in Equation (32) to get Equation (34):
9. Discussion of Results
Energy consumption has been known to be one of the greatest challenges of WSNs and will
continue to be an immense challenge for the deployment of WSNs because the advancement in battery
technology has been slower than the growth of processing power and data communication rates. This
challenge has attracted researchers to introduce several new energy-efficient protocols to address this
problem . To address this challenge, several MAC protocols have been introduced at the MAC
level. Hybrid MAC protocols are of paramount importance because they have lower energy
consumption and better scalability than other categories of MAC protocols. In this section, we discuss
and compare the strengths and weaknesses of BN-MAC versus other hybrid MAC protocols.
The Z-MAC protocol belongs to the hybrid family that supports multi-hop topology, and the nodes
are fixed at their positions. The global time synchronization is used to synchronize the nodes, and slots
are assigned to nodes but not fixed for each node. Z-MAC competes for the channel within any slot for
data transmission. The assigned node is given high priority, which reduces collisions. The latency is
increased, and the throughput is moderated. Z-MAC faces some problems because of the use of long
preamble messages with a destination address, which increases the duty cycle and energy
consumption. The fixed topology limits the node scalability of WSNs. The setup of the network phase
becomes more difficult when a new node joins or leaves the network. Mobile nodes are unable to
receive and send data packets. As a result, the network paths are broken.
Sensors 2014, 14 5112
Speck-MAC is a variant of the B-MAC protocol but exhibits redundant short-packet transmission
and integrated destination addresses. Speck-MAC is efficient in transmitting the unicast messages, but
the sender wastes excess energy by sending additional frames even though the receiver has already
received the frames. The additional frames consume channel bandwidth and thus reduce the packet
delivery rate. Speck-MAC supports mobility when the network path is broken, which increases latency.
LPRT-MAC is based on an efficient bandwidth allocation mechanism and uses super frames fixed
into mini-slots to communicate with the base station. LPRT-MAC reduces the power consumption and
coordinates with the channel. LPRT-MAC exhibits significant packet loss, which is affected by bit
errors. LPRT-MAC also suffers from the star topology. Once the central node fails, the entire network
suffers because the node maintenance requires a longer period of time in the WSN. This situation
reduces the throughput and increases latency. Additionally, there is no dynamic node selection in
LPRT-MAC, which could help to replace the node prior to its failure. The star topological network
also exhibits low mobility because the nodes are tightly linked and cannot leave or join the network.
LPRT-MAC also cannot be used for other communication topologies because it is not suitable
for multi-hop WSNs due to topological constraints. A-MAC is based on a collision-free and
non-overhearing mechanism and is particularly suitable for surveillance and monitoring applications.
The nodes are attentive and inactive for longer periods of time until an event is detected. The major
advantage of A-MAC is that it allows nodes to be notified in advance. However, A-MAC exhibits
rather high idle listening and packet overhead. A-MAC consumes high amount of energy due to
advertisements. Additionally, the high level of latency reduces the throughput. Sensor nodes are
deployed tightly in A-MAC, causing mobility issues.
ADC-SMAC improves upon two features of S-MAC: node utilization and sleep delay. The
advantage of ADC-SMAC is that it introduces flexible duty cycles and forwards new scheduling
information to the neighbor sensor nodes. ADC-SMAC also supports real-time data communication.
However, local synchronization in ADC-SMAC consumes a significant amount of energy and
increases the latency. ADC-SMAC is not suitable for controlling idle listening and overhearing
problems. ADC-SMAC also does not support mobility.
Our proposed BN-MAC is an energy efficient, semi-synchronous, and low-duty-cycle hybrid
protocol that is especially designed to support applications in which events occur in different locations.
BN-MAC is simulated on different region-based WSNs. Each region is controlled by a BN. BN-MAC
does not compel any node to be elected as a BN based on a probability calculation. BN-MAC selects
the BN based on the energy level that improves the network lifetime using the one-hop neighbor node
with a semi-synchronous mechanism for scheduling at the MAC level. The multiple hops on the path
create nonlinearities in the system. The node must wait for the next hop node to wake up. In this
manner, the packet is held on every link of the path for different amounts of time .
EAR and LDSNS are used to determine the shortest efficient path at the routing level. Thus, there is
a small probability of failure for the one-hop path. If the one-hop path fails, then the second best
one-hop path is chosen based on the information stored for each one-hop neighbor node. Furthermore,
BN-MAC performs localized time slot allocation without changing the time slots of existing nodes.
This procedure reduces the latency and overhead and has a small probability of broken links. AAS is
an energy-efficient search that reduces the energy consumption because the nodes automatically sense
the environment. IDM is another feature implemented in BN-MAC. IDM forces the sensor node to
Sensors 2014, 14 5113
work either in the passive or active mode, depending on the environment. Furthermore, the BN
supports mobile environments because the sensor nodes exchange the schedule at the one-hop path but
keep the information for two hops. This feature supports scalability. The characteristics of BN-MAC
and other hybrid MAC protocols are illustrated in Table 4 that demonstrates the strengths and
weakness based on simulation results.
Table 4. Characteristics and affecting factors of Hybrid Medium Access Control (MAC)
Protocols and BN-MAC protocol.
Packet size affect
Duty cycle %
Delivery ratio %
Path detection time
This paper introduces a new energy-efficient BN-MAC hybrid protocol with mobility support.
The BN-MAC is proposed and simulated for the battlefield scenario over WSNs. The protocol
leverages the features from both CSMA and TDMA. CSMA features embedded in BN-MAC consist
of semi-synchronization, which uses a short preamble to access the channel and maintain the schedule
at the one-hop neighbor nodes. TDMA features are imported into BN-MAC for collision-free data
delivery. We have introduced the IDM model, which automates the sensor nodes to work either in the
passive or active mode with respect to the environment. The IDM model reduces the energy
consumption when working in the passive mode. BN-MAC also has a reduced idle listening time
based on the use of the AAS model. The AAS model forces the sensor nodes to go into the sleep state
after collecting information on the events. Latency is reduced using the LDSNS model and EAP
routing protocol. LDSNS provides the efficient one-hop path search. EAP is suitable for maintaining
route discovery and path maintenance at the one-hop destination for faster data delivery. BN-MAC
also uses two types of messaging schemes to control congestion and reduce latency: Anycast is used to
obtain information from the one-hop neighbors, and unicast is used to forward the data. To evaluate
the features of the proposed BN-MAC in the battlefield scenario, we used ns2.35-RC7 to demonstrate
the performance from different perspectives. We have also simulated other hybrid protocols, such as
Z-MAC, A-MAC, ADC-SMAC, LPRT-MAC, and Speck-MAC. The simulation results demonstrate
Sensors 2014, 14 5114
that BN-MAC reduces the energy consumption by 18%–45%, improves the throughput, and decreases
the latency compared with other hybrid MAC protocols with the same node density and topology. Our
findings prove that BN-MAC is a scalable and mobility-aware protocol with real-time communication
support. The protocol can be disseminated for other WSN applications, such as monitoring, controlling
natural disasters, human-centric applications, and tracking mobile and static home automation devices.
In the future, we will implement its features in a realistic environment.
The authors wish to thank Lili Zhu, Massachusetts Institute of Technology (MIT) for discussing the
existing hybrid MAC protocols and helping to set up simulation scenarios in ns2. The special thanks to
Mohamed F. Younis, Department of Computer Science and Electrical Engineering, University of
Maryland, USA for his valuable comments and suggestions for improving the quality of the paper.
Finally, we are also thankful to anonymous reviewers for their valuable feedback.
This research work is part of Abdul Razaque’s Ph.D. dissertation work. The work has been
primarily conducted by Abdul Razaque under the supervision of Khaled M. Elleithy. Extensive
discussions about the algorithms and techniques presented in this paper were carried between the two
authors over the past year.
Conflicts of Interest
The authors declare no conflict of interest.
1. Meng, W.; Xie, L.; Xiao, W. Optimality analysis of sensor-source geometries in heterogeneous
sensor networks. IEEE Trans. Wirel. Commun. 2013, 12, 1958–1967.
2. Razaque, A.; Elleithy, M.K. Automated Energy Saving (AES) Paradigm to Support Pedagogical
Activities over Wireless Sensor Networks. In Proceedings of the 6th Springer/ACM International
Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI), Vitoria-Gasteiz,
Spain, 3–5 December 2012; pp. 512–519.
3. Joshi, Y.K.; Younis, M. Autonomous recovery from multi-node failure in Wireless Sensor
Network. In Proceedings of the IEEE Conference on Global Communications Conference
(GLOBECOM), Anaheim, CA, USA, 3–7 December 2012; pp. 652–657.
4. Handbook on Ambient Intelligence and Smart Environments; Hideyuki, N., Aghajan, H.,
Augusto, J.C., Eds.; Springer: New York, NY, USA, 13 November 2009.
5. Razaque, A.; Elleithy, M.K. Least Distance Smart Neighboring Search (LDSNS) over Wireless
Sensor Network. In Proceedings of the IEEE Conference on European Modeling Symposium
(EMS), Manchester, UK, 20–22 November 2013.
Sensors 2014, 14 5115
6. Reddy, S.; Samanta, V.; Burke, J.; Estrin, D.; Hansen, M.; Srivastava, B.M. MobiSense—Mobile
Network Services for Coordinated Participatory Sensing. In Proceedings of the 9th International
Symposium on Autonomous Decentralized Systems (ISADS), Athens, Greece, 23–25 March 2009;
7. Kabara, K.; Calle, M. MAC protocols used by wireless sensor networks and a general method of
performance Evaluation. Int. J. Distrib. Sens. Netw. 2012, 2012, 1–11.
8. Demirkol, I.; Ersoy, C.; Alagöz, F. MAC protocols for wireless sensor networks: A survey.
IEEE Commun. Mag. 2006, 44, 115–121.
9. Rezaie, M.G.; Mansouri, S.V.; Mani, M. Critical Area Attention in Traffic Aware Dynamic Node
Scheduling for Low Power Sensor Networks. In Proceedings of the IEEE Wireless
Communications and Networking Conference, New Orleans, LA, USA, 13–17 March 2005;
Volume 4, pp. 1933–1938.
10. Huang, P.; Xiao, L.; Soroor, S.; Mutka, M.W.; Ning, X. The evolution of MAC protocols in
wireless sensor networks: A survey. IEEE Commun. Surv. Tutor. 2013, 15, 101–120.
11. Razaque, A.; Elleithy, M.K. Automatic energy saving (AES) model to boost ubiquitous wireless
sensor networks (WSNs). Int. J. Comput. Technol. 2013, 10, 1–11.
12. Bachir, A.; Dohler, M.; Watteyne, T.; Leung, K.K. MAC essentials for wireless sensor networks.
IEEE Commun. Surv. Tutor. 2010, 12, 222–248.
13. Van Hoesel, L.F.W.; Havinga, P.J.M. A Lightweight Medium Access Protocol (LMAC) for
Wireless Sensor Networks: Reducing Preamble Transmissions and Transceiver State Switches.
In Proceedings of the 1st International Workshop on Networked Sensing Systems (INSS), Tokyo,
Japan, 22–23 June 2004; pp. 205–208.
14. Liqi, S.; Fapojuwo, A. TDMA Scheduling with optimized energy efficiency and minimum delay
in clustered wireless sensor networks. IEEE Trans. Mob. Comput. 2010, 9, 927–939.
15. Rhee, I.; Warrier, A.; Aia, M.; Min, J. Z-MAC: A Hybrid MAC for Wireless Sensor Networks.
In Proceedings of the International Conference on Embedded Networked Sensor Systems
(SENSYS), San Diego, CA, USA, 2–4 November 2005.
16. Rhee, I.; Warrier, A.; Aia, M.; Min, J.; Sichitiu, M.L. Z-MAC: A hybrid MAC for wireless sensor
networks. IEEE/ACM Trans. Netw. 2008, 16, 511–524.
17. Suh, C.; Ko, Y.-B. A Traffic Aware, Energy Efficient MAC Protocol for Wireless Sensor Networks.
In Proceedings of the IEEE International Symposium on Circuits and Systems, Kobe, Japan,
23–26 May 2005; Volume 3; pp. 2975–2978.
18. Liu, A.; Yu, H.; Li, L. An Energy-Efficiency and Collision-Free MAC Protocol for Wireless
Sensor Networks. In Proceedings of the 2005 IEEE 61st Vehicular Technology Conference,
Stockholm, Sweden, 30 May–1 June 2005; Volume 2, pp. 1317–1322.
19. Ye, W.; Heidemann, J.; Estrin, D. Medium access control with coordinated adaptive sleeping for
wireless sensor networks. IEEE/ACM Trans. Netw. 2004, 12, 493–506.
20. Zhou, G.; He, T.; Krishnamurthy, S.; Stankovic, J.A. Impact of Radio Irregularity on Wireless
Sensor Networks. In Proceedings of the 2nd International Conference on Mobile Systems,
Applications, and Services, Boston, MA, USA, 6–9 June 2004; pp. 125–138.
Sensors 2014, 14 5116
21. Woo, A.; Culler, D. A Transmission Control Scheme for Media Access in Sensor Networks. In
Proceedings of the 7th Annual International Conference on Mobile Computing and Networking,
Rome, Italy, 16–21 July 2001; pp. 221–235.
22. Polastre, J.; Hill, J.; Culler, D. Versatile Low Power Media Access for Wireless Sensor Networks.
In Proceedings of the 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys),
Baltimore, MD, USA, 3–5 November 2004.
23. Merlin, C.J.; Heinzelman, W.B. Schedule adaptation of low-power-listening protocols for
wireless sensor networks. IEEE Trans. Mob. Comput. 2010, 9, 672–685.
24. Lin, J.; Ingram, A.M. SCT-MAC: A Scheduling Duty Cycle MAC Protocol for Cooperative
Wireless Sensor Network. In Proceedings of the 2012 IEEE International Conference on
Communications (ICC), Ottawa, ON, Canada, 10–15 June 2012; pp. 345–349.
25. Liu, Y., Lionel, M. A New MAC Protocol Design for Long-Term Applications in Wireless Sensor
Networks. In Proceedings of the 2007 International Conference on Parallel and Distributed
Systems, Hsinchu, Taiwan, 5–7 December 2007; pp. 1–8.
26. Wong, K.J.; Arvind, D. SpeckMAC: Low Power Decentralized MAC Protocols for Low Data
Rate Transmissions in Specknets. In Proceedings of the ACM International Workshop on
Multihop Ad-Hoc Networks: From Theory to Reality, Florence, Italy, 26 May 2006; pp. 71–78.
27. Yan, W.W.; Xiang, Y.L.; Mo, L.; Wei, L. Energy-efficient wake-up scheduling for data collection
and aggregation. IEEE Trans. Parallel Distrib. Syst. 2010, 2, 275–287.
28. An, Z. Medium access control protocol with dynamic duty cycle in wireless sensor network.
Int. J. Future Comput. Commun. 2012, 1, 36–39.
29. Afonso, J.A.; Rocha, L.A.; Silva, H.R.; Correia, J.H. MAC Protocol for Low-Power Real-Time
Wireless Sensing and Actuation. In Proceedings of the 11th IEEE International Conference on
Electronics, Circuits and Systems, Nice, France, 10–13 December 2006; pp. 1248–1251.
30. Liu, M.; Cao, J.; Chen, G.; Wang, X. An energy-aware routing protocol in wireless sensor
networks. Sensors 2009, 9, 445–462.
31. Ioannis, C.A.B.; Kinalisa, A.; Nikoletseasa, S.B. Fault tolerant and efficient data propagation in
wireless sensor networks using local additional network information. J. Parallel Distrib. Comput.
2007, 67, 456–473.
32. Razaque, A.; Elleithy, M.K. Pheromone Termite (PT) Model to provide Robust Routing over
WSNs. In Proceedings of the IEEE International Conference for American Society for
Engineering Education (ASEE), Bridgeport, CT, USA, 3–5 April 2014; in press.
33. Razaque, A.; Elleithy, M.K. Mobility-Aware Hybrid Medium Access Control Protocol for Wireless
Sensor Network (WSN). In Proceedings of the 2014 IEEE Sensors Applications Symposium,
Rydges Lakeland Resort, Queenstown, New Zealand, 18–20 February 2014; in press.
34. Brownfield, M.; Yee, G.V.; Anderson, E.; Han, R. X-MAC: A Short Preamble MAC Protocol for
Duty-Cycled Wireless Sensor Networks. In Proceedings of the ACM 4th International Conference
on Embedded Networked Sensor Systems (SenSys), Boulder, CO, USA, 1–3 November 2006;
35. Jamieson, K.; Balakrishnan, H.; Tay, Y.C. Sift: A MAC Protocol for Event-Driven Wireless
Sensor Networks. In Proceedings of the 3rd European Workshop on Wireless Sensor Networks
(EWSN), Zurich, Switzerland, 13–15 February 2006; pp. 260–275.
Sensors 2014, 14 5117
36. Yang, O.; Heinzelman, W.B. Modeling and performance analysis for duty-cycled MAC protocols
with applications to S-MAC and X-MAC. IEEE Trans. Mob. Comput. 2012, 11, 905–921.
37. Antonopoulos, A.; Verikoukis, C. Network coding-based cooperative ARQ medium access control
protocol for wireless sensor networks. Int. J. Distrib. Sens. Netw. 2012, 2012, 601321:1–601321:9.
38. Brownfield, M.I.; Mehrjoo, K.; Fayez, A.S.; Davis, N.J., IV. Wireless Sensor Network
Energy-Adaptive MAC Protocol. In Proceedings of the 3rd IEEE Consumer Communications and
Networking Conference (CCNC), Las Vegas, NV, USA, 8–10 January 2006; pp. 778–782.
39. Ba, H.; Demirkol, I.; Heinzelman, W.B. Passive wake-up radios: From devices to applications.
Ad Hoc Netw. 2013, 11, 2605–2621.
40. Chiasserini. C.; Rao, R.R. Improving energy saving in wireless systems by using dynamic power
management. IEEE Trans. Wirel. Commun. 2003, 2, 1090–1100.
41. BTnodes—A Distributed Environment for Prototyping Ad Hoc Networks. Available online:
http://www.btnode.ethz.ch/ (accessed on 26 August 2013).
42. Kay, M.S. Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm
Development, 1st ed.; Signal Processing Series; Prentice Hall: Upper Saddle River, NJ, USA,
5 April 2013.
43. Zhao, J.; Govindan, R. Understanding Packet Delivery Performance in Dense Wireless Sensor
Networks. In Proceedings of the ACM on 1st International Conference on Embedded Networked
Sensor Systems (SenSys), Los Angeles, CA, USA, 5–7 November 2003; pp. 1–13.
44. Akyildiz, I.F.; Pompili, D.; Melodia, T. Underwater acoustic sensor networks: Research challenges.
Ad Hoc Netw. 2005, 3, 257–279.
45. Tilak, S.; Abu-Ghazaleh, N.B.; Heinzelman, W. A taxonomy of wireless micro-sensor network
models. ACM SIGMOBILE. Mob. Comput. Commun. 2002, 6, 28–36.
46. Kulik, J.; Heinzelman, W.; Balakrishnan, H. Negotiation-based protocols for disseminating
information in wireless sensor networks. Wirel. Netw. 2002, 8, 169–185.
47. Kemal, A.; Mohamed, F.Y. A survey on routing protocols for wireless sensor networks.
Ad Hoc Netw. 2005, 3, 325–349.
48. Gupta, P.; Kumar, R.P. The capacity of wireless networks. IEEE Trans. Inf. Theory. 2000, 46,
49. Merlin, C.J.; Heinzelman, W.B. Duty Cycle Control for Low-Power-Listening MAC Protocols. In
Proceedings of the 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems,
Atlanta, GA, USA, 29 September–2 October 2008; pp. 497–502.
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