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Least Distance Smart neighboring Search (LDSNS) over Wireless Sensor Networks


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In this paper, we introduce a novel least distance smart neighboring search (LDSNS) to determine the mostefficient path at one-hop distance over WSNs. LDSNS helps to reduce the energy consumption and speeds up scheduling for delivery of data. It provides cross layering support and linking MAC layer with network layer to reduce the amount of control messages. LDSNS is a robust and efficient approach that isbased on single-hop communication mechanism. To validate the strength of LDSNS, we incorporate LDSN in Boarder Node Medium AccessControl (BN-MAC) protocol [ 15] to determine the list of neighboring sensor nodes and choosing best 1-hop efficient search to avoid collision and reducing energy consumption. Evaluation of LDSNS is conducted using network simulator-2 (ns2).The performance of LDSNS is compared with minimum energy accumulative routing problem (MEAR) [12], asynchronous quorum-based wakeup scheduling scheme (AQWSS) [14] and Minimum Energy Relay Routing (MERR) [13]. Simulation results show that LDSNS is highly energy efficient and faster as compared with MEAR, AQWSS and MERR. It saves 24% to 62% energy resources and improves12% to 21% search at 1-hop neighboring nodes.
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Least Distance Smart neighboring Search (LDSNS)
over Wireless Sensor Networks
Abdul Razaque, Member IEEE,Khaled .M. Elleithy, Senior member IEEE
Wireless and Mobile Communication (WMC) Laboratory
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06604
Abstract: - In this paper, we introduce a novel least distance
smart neighboring search (LDSNS) to determine the
mostefficient path at one-hop distance over WSNs. LDSNS
helps to reduce the energy consumption and speeds up
scheduling for delivery of data. It provides cross layering
support and linking MAC layer with network layer to reduce
the amount of control messages. LDSNS is a robust and
efficient approach that isbased on single-hop communication
mechanism. To validate the strength of LDSNS, we
incorporate LDSN in Boarder Node Medium AccessControl
(BN-MAC) protocol [ 15] to determine the list of neighboring
sensor nodes and choosing best 1-hop efficient search to avoid
collision and reducing energy consumption.
Evaluation of LDSNS is conducted using network simulator-2
(ns2).The performance of LDSNS is compared with minimum
energy accumulative routing problem (MEAR) [12],
asynchronous quorum-based wakeup scheduling scheme
(AQWSS) [14] and Minimum Energy Relay Routing (MERR)
[13]. Simulation results show that LDSNS is highly energy
efficient and faster as compared with MEAR, AQWSS and
MERR. It saves 24% to 62% energy resources and
improves12% to 21% search at 1-hop neighboring nodes.
Keywords: LDSNS, Wireless sensor network, Energy
consumption, MAC layer.
WSNs is one of the prominent research areas in recent years.
Unlike traditional networks, WSNs are particularlyusedin
physical environments to develop high degree of perceptibility
[6]. It is one of the rapidly growing fields with attractive
features to use in several application areas[2], [3]. WSNs are
considered as low-cost and easy to set up [4]. The advent of
WSN has improved progress in surveillance and monitoring
systems, home automation devices, earthquake and disaster
applications etc. [1] & [5].
WSNsfaceseveral design and performance issues such as
waste of energy in idle listening, overhearing, extra control
messaging, emitting and congestion. In addition, experiencing
several performance impairing factors such as scalability,
mobility, lack of robustness, uniformity etc. The energy
consumption is considered as one of the major apprehensions
[7] that stimulates challenges for industrial and academic
sectors. Therefore, proper energy handling is one of the key
skills to preserve energy [9].
The radio is the main power consuming section of sensor in
WSNs that can be handled by introducing robust MAC
protocols. Thus, an efficient MAC protocol improves WSN
lifetime. In addition, MAC protocol has capability to handle
the issue of sharing the wireless channel and reduces the
collisions in order to improve throughput.
Several MAC protocols have been proposed to reduce energy
consumption and to providefaster delivery of data but
problemis not fully yet resolved. Two types of mechanisms
are used to support scheduling and routing the data: single hop
and multiple-hop.
According to some protocols, a node takes a part in data union
and consumes more energy. Since, the transmission of power
is quadratically proportional tothe distance, multi-hop
approach consumes less energy than one hop communication,
but it creates more overhead on the network and it also
experiences severe problem when routes are broken.
Furthermore, joining of new node and leaving of working
node reduce the throughput and consumes sufficient amount
of energy [10].In this regard, one-hop communication is
efficient and more reliable.
An energy-efficient minimum transmission energy
consumption (MTEC) protocol is proposedto reduce the
energy consumption and increase throughput during data
transmissions. METC has also discussed that MAC protocol
can dynamicallyadjust the size of contention window on basis
of successful ratios of data transmissions using cross-layer
protocol [8]. MEAR developed heuristic approach to
determine an energy efficient wave-path and compared with
traditional shortest path algorithm.The authors urgedthat the
existing shortest path algorithm has shortcoming of optimal
relaying strategy and channel propagation. MEAR also filsto
decide which node should contribute in transmission schedule
and the order of nodes to transmit and their transmission
power [12].
AQWSS [14] introduced a set of asynchronous quorum-based
wakeup scheduling mechanism to provide a better trade-off
between average delay and energy consumption for neighbor
discovery under variable environments.
MERR is introduced for linear sensor topology to consume
less energy based on optimal transmission distance [13]. All of
the discussed techniques tryto reduce the energy consumption
but from other side, they increased the overhead of network.
Keeping these factors in mind, we introduce LDSNS approach
to support BN-MAC protocol to reduce energy consumption
and speed up the delivery of data without putting any extra
overhead of control packets on network. The reminder of
paper is organized as: section 2, explains the Least Distance
Smart Neighboring Search (LDSNS). Section 3, simulation
and analysis of result are discussed and thepaper is concluded
in section 4.
In LDSNS, any node monitors the channel after every 500 ms.
ifgain of channel is less than set threshold value, it shows that
there is no activity on medium from its neighbor nodes;
resulting that the node decides to sleep again.When a
transmitter wants to communicate, it first sends short preamble
to alertone hop neighboring nodes for sending the data. When
the targeted receiversenses short preamble, it wakes up and
responds with an acknowledgment (ACK) to transmitter. After
the transmitter gets ACK,it starts to send the data packets.
Pictorial illustration of the protocol is given in Figure 1.
S- W
Figure 1: Mechanism of LDSNS to communicate with 1-hop neighbor nodes
Let us prove this idea by using Lemmas and definitions. The
least distance smart neighboring search is based on 1-hop
distance and route discovery. The designed WSN consists of
different regions. The node which communicates within
region that maintains local connectivity, whereas node that
communicates out of region and schedules within region is
called boarder node (BN). Let us assume that directed graph D
= (V, A), consisting of the set of sensor nodes V. The set of
edges are called arcs that are A V2. It helps to differentiate
between 1-hop destination and more than 1-hop destination
nodes. The digraph distance between nodes is simply the
number of shortest path between them [12]. We assign a name
to each sensor node in V. A local route discovery method is
based on relay scheme that works as follows.
For any destination node in Vspecified by name v, the
scheme targets the 1-hop destination nodes u on basis of
stored information in routing table regarding the shortest path
1-hop destination node. Each 1-hop destination node delivers
the shortest path to its predecessor during exchange of control
message.Finally destination v is acquired with efficient path.
We apply method [8] for estimation of global technology of
sensors by dividing nodes into routable boundaries and
extracting adjacency associations between these boundaries.
The objective of creating each boundary is to make the
topology simpler, so that the searching process works
efficiently within the boundaries. For a number of sensor
nodes V and communication digraph D.
We pretend that D is connected, thus we just consider
connected components autonomously. Therefore,u:((u, v) A)
can be denoted for hop count of neighboring search between
u, v in communication digraph.
Definition 1. Let P(x, y) denote set of paths from „x‟ to‟ y
for 1-hop neighbor nodes in direct graph (Dg). Hence, S (x, y)
is the distance (S) between two neighbor nodes x, y in Dg,
which shows shortest path from node x to y. It can be
computed as:
=,=     ,1
Lmin: Minimum length from one node to other node.
p: path value
If Dg (x, y) = then Dg (x, y) = ∞.
Dg(x, Ѐ) between node x and subset of nodes Ѐ E that is
defined as:
=, É =min,& É (2)
Thus, Ẋ, E, be the distance between two neighbor nodes
that can be computed as:
min , É& É (3)
Thus, we can add random infinitesimal for unique path.
Definition 2. For a digraph D = (V1, V2), be the set of 1-hop
destination nodes for vertex v that is explained as:
  = {   . 2}, and beyond of 1-hop
destination nodes are explained as:
+= { . 2},
λ: Total number of neighboring nodes
D(v): Pair of one hop neighboring nodes
v: Value of link between two neighboring nodes
V1: Vertex of node
V2: Vertex of neighbor node
We describe 1-hop destination nodes of a vertex V1as union
with set of 1-hop destination nodesvertex V2. If the distance
exceeds more than 1-hop destination nodes, it can be
expressed as:
= +   4
The range and out of range distance can be found as:
 = +5
Above equation (5) shows that node is within range.
 =   6
Equation (6) shows that node is out of range.
From equation (5) and (6), we deduce that
  
We may again exclude subscript if digraph Dg = (V1, V2) is
clear from context. The weighted graphs also get association
of assorted length, cost and strength. We only focus on edge-
weighted graph that is opposite to node-weighted graphs. We
also need to restrict edge weights to 1 that yield an un-
weighted graph.
Consider digraph Dg = (V1, V2) and its subset for boundary of
regions R V1, explain boundary B (v) of a node. Therefore,
v R and whose nearest region is v.
Thus, boundary of all regions can be expressed as follows:
=  1w R, u, v , } (7)
Lemma 1. Let simple path = (,1 , 1,...,1)that
connects tworegion nodes d= a1 and t= with e edges and
path of length is „p'. The related boundary path p* has
maximum length in boundary dual graph
such as L(P*) ≤ e
Proof: The path includes e-1 that is used for more than 1-hop
destination nodes and e edges that pass through e +1 multi-
hop in the same region. The most of regions e+1 are intrusive
regions, it means that original path does not go directly to
those nodes but shortest path does. The L (P) in original graph
is sum of edge weights that can be defined as:
LP= ds, t= w( , 2
+ 1 ) (8)
Equation (8) shows the creation of path from transmitter to
receiver. „e‟ is an edge between two nodes of boundaries on
path P* that is bounded as follows:
=  , + 1  ,
+ ,+ 1
+ (+ 1, + 1)] (9)
Nbou : Node in region
d*: Connecting two region nodes
Let us assume that„s‟and„t‟be two nodes of region that could
be source and target nodes, and defined as follows:
( ,    , (  ,
=(  , )
It yields:
,= (, [ (
 ,
+ ( , +1 + +  + 1)+1
+  1,
,+  , (10)
[ (
,) + (
+ 1, 1+ 1 )] (
+ ( ( 1),( 1, )
(,) + (,
) + ( , ) (11)
Simplifying the equation (11), we get as:
LP= e. L(P) (12)
From equation (12), we see that Bound is observed to be tight
because constructions exist.
For example, If any choice for m >λ> 0, thus edge graph
weights of graph for two nodes of regions can be described as:
(, (=  , w ,+ 1 = (13)
,=1, = (14)
Since 2m + (e-2) λ is the length of path and 2m +(e-2) λ 2(e-
1)*(e- λ) is the length of whole region.
Therefore, the worst case for „λ‟ can be written as:
λ → 0, and ratio can be shown as follows:
e 15
If region nodes are available on shortest path, thus maximum
expansion will be shorter than number of edges on shortest
path. We hereby prove that maximum expansion is
proportional to largest gap between region nodes on path.
Lemma 2. For any node u B (v), the shortest path from
node u to v is completely included in B (v).
Proof: If lemma were incorrect, there would exist  
()on the shortest path from node u to destination node
,< ,
And such that:
, ,+ ,< ,+ ,
This statement contradicts with hypothesis, such as   ;
thus lemma must be correct. One inference of this lemma is
connection of boundary cells on spanning graph. Region cells
are dirichlet, connecting all points of sensor field. Region has
simple topology in all dimensions that is stronger point of
connectivity. The simpler topology helps to make subsets of
sensor fields, when sensor filed experiences large holes. Thus,
edges u1, u2 є B (v).
Lemma 3: For node in each region of WSN calculates
maximum cost for all one-hop neighboring nodes for selection
of lowest cost path.
Proof: Let „x1 be node, which calculates the cost for each 1-
hop neighboring nodes x2
Here, „Tcost is total cost for all 1-hop neighbor nodes and
„Scost is the cost for one neighbor node, which can be
calculated as follows.
 =  1+ 2+1,217
We set value zero to Tcost(x1) because „x1 is initiating node
that calculates the path cost that will be starting point. Energy
Level is used to calculate transmitting and receiving cost of
node with remaining energy of nodes. Nodes with value of
high cost are discarded and the cost of each 1- hop neighbor
node is saved into routing forwarding table (RFT).
Thus, „x2 calculates the minimum cost distance „D‟ for
reaching at 1-hop destination node with RFT using following
 1= 1
,2 18
It is proved that minimum cost for establishing path from „x1
to „x2 is set in RFT of „x1.
The Realistic environment of WSNs use low power radios
with stochastic link and high asymmetrical communication
range. The simulation results could be different from expected
realistic results.We simulate LDSNS, MEAR, AQWSS and
MERR using NS 2.35-RC7. For simulation, we have designed
WSN that consists of different regions. Each region has
boarder node (BN) that forwards the collected information of
its region to BN of next region. We have simulated different
realistic scenarios:mobileand static. The main goal of
contribution is to reduce energy consumption and
supportingfaster search at one hop neighbor nodes.
The simulation scenario consists of 140 nodes with
transmission radius of 30 meters. The Bluetooth enabled
(BT)sensor nodes are uniformly and randomly placed in
geographical area of 300 * 300 square meters. Area is divided
into 75m x 75m different regions. The initial energy of nodes
is set 40 Joules. The bandwidth of node is 50 Kb/Sec and
maximum power consumption for each sensor is set 16 Mw.
Sensing and idle modes 12 mW and 0.5 mW respectively but
in our case, there is no idle mode. Sensors either go to active
or sleep mode. Each sensor is capable of broadcasting the data
at 10 power intensity ranging from -20 dBm to 12 dBm.
Total simulation time is 35 minutes and set 30 seconds
pause time for initialization of phase at start of simulation.
During this phase, only BN remains active and remaining
sensors of all regions go into power saving mode
automatically. The results demonstrate an average of 10
simulation runs. The energy consumption pertaining with
different radio modes and simulation parameters are summed
up in Table 1.
Table 1: Summarized simulation parameters for WSN
Name of parameters
Transmission Range
30 meters
Type of sensors
BT node sensors
Sensing Range of node
10 meters
Initial energy of node
40 Joules
Bandwidth of node
50 Kb/Sec
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
30 Packets/Sec
Mobility model
Random way- point
Simulation time
35 minutes
Initial pause time
30 Seconds
Tx energy
16 mW,
Rx energy
12 mW,
Power intensity
-20 dBm to 12 dBm.
Start time of BN-MAC
(0,30) Seconds
Sink location in each
(60, 40)
MAC protocol
Type of protocols
and MERR
0.5 m/sec to 3.5 m/sec
Size of packets
8, 16,32,64,128,256 and
512 bytes
Routing Protocol
Pheromone termite
To demonstrate the validity of this novel environment of WSN
for handling several devices, we conduct several tests from
different perspectives. Having proved the mathematical
model, let us now evaluate the e efficiency of WSN.
We target preserving amount of energy of the network after
deploying from sensors as shown infigures 2 to 6. Our
simulated network shows that our proposed paradigm achieves
almost 100% efficiency and saves 62% energy using
maximum 140 sensors. We establish 15 sessions
simultaneously in order to determine the actual behavior of the
network in a highly congested environment. If we have less
number of sensors, it is hard to establish many sessions at the
same time. Figure 2 shows 14 hops in WSN and the used duty
cycle. We notethat LDSNS has consumed minimum duty
cycle as compared with MERR, MEAR and AQWSS. During
1 hop destination, all of the techniques use 1.5 % to 2.0% duty
cycles as the hops increase then their consumption ratio of
duty cycles decrease. The reason is that there is no overhead
on single hop-node and work is distrusted on other nodes that
are part of other hops. LDSNS uses less duty cycle because it
is integrated with BN-MAC protocol that has fully support of
semi synchronous mechanism as well as robust nature of
LDSNS makes it capable to use low duty cycle.
0.5 1 1.5 2.0 2.5
04 6 8 10 12 14
Figure.2. Consumed duty cycles versus number of hops
InF 3, we show consumption of energy for each scheme
against variable size of packets. LDSNS consumes less energy
with variable size of packets. In case of broken link, LDSNS
uses alternate link to send data packets but other schemes do
not have sound alternate link to forward the data. Further,
LDSNS uses both proactive and reactive approaches, when
nodes are static and there is possibility of leaving and joining
the new node.If the node decides to leave or other node wants
to join then LDSNS uses reactive approach to get run time
informationaccording to change of topology.
0.5 1 1.5 2.0 2.5
016 32 64 128 256 512
Figure 3: Consumption of energy VS variable size of packets
In Figure 4, we show consumption of energy versus number of
hops. LDSNS consumes less energy than MERR, MEAR and
AQWSS. The increase in the number of hops affects all
protocols but LDSNS has better energy consumption. LDSN is
based on an efficient approach whichis integrated mobility and
scalability aware BN-MAC protocol. Meanwhile,the
otherapproaches experience energy increase due to the limited
support at MAC layer.
Figure 4. Energy consumption at variable number of hops of WSN
Figure.5. Effective duty cycles due to neighboring nodes
In Figure 5, we show the effective duty cycle versus the
number of neighboring nodes. Due to the increase in the
number of neighbor nodes, LDSNS, MERR, MEAR and
AQWSS are affected Meanwhile LDSNS is not highly
affected. In addition, LDSNS is supported with semi
synchronous approach inherited by BN-MAC protocol. In all
of cases, LDSNS performs better than other protocols and
consumes less energy and gets higher data packet rates.
In this paper, we have introduced the Least Distance Smart
Neighboring Search (LDSNS) to reduce energy consumption
and provide faster scheduling for data delivery.LDSNS
performs better than other techniques even under low duty
cycle and different packet sizes. LDSNS is integrated with
BN-MAC protocol to support several applications such as
disaster, home automation, hospital and monitoring
applications of WSNs. To test the strength of LDSNS, our
designed WSNis divided into N-regions. Each region consists
of one boarder node that communicates with a neighboring
region. The boundary node israndomly elected on the basis of
high energy available inthe node. To validate the proposed
LDSNS, we have simulated LDSNS and compared its
performance with other known protocols: MEAR, AQWSS
and MERR by using ns2.35-RC7. On the basis of simulation
results, we demonstrate thatLDSNSperformsbetter than other
mechanisms (protocols) in terms of low duty cycle, energy
consumption, increasing number of neighbor nodes and size of
packets. LDSNS saves 24% to 62% energy resources and
improves by 12% to 21% search at 1-hop neighboring
nodes.In the future, we plan tocombine LDSNS with different
analytical models to test its strength in different scenarios.
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Journal of computers and technology 2013.
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... In handling this disaster situation, the static and mobility-aware nodes use the semi-synchronization approach to synchronize with each other. Semi-synchronization is a novel approach that helps to select the particular nodes in the 1-hop neighborhood for communication, and this approach consumes only the energy of those nodes that are randomly selected based on their available resources (e.g., energy, bandwidth, and shortest distance), as explained in [15]. The semi-synchronization approach is deployed for the nodes in regions that are following the interior communication process explained in the next section. ...
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Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the quality of service (QoS) provisioning and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: 1) the operational medium access control (O-MAC) hybrid protocol and 2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: 1) determining the packet generation rate to avoid congestion and 2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model, we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff, hospital staff and disaster victims brought into the hospital. Moreover, the proposed paradigm can be used for general purpose applications. Finally, the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.
... INTRA-REGIONAL HANDOFF COMMUNICATION PROCESS SE-MAC reduces the communication delays, channel delays and control delays. The communication process follows the 1-hop destination used in [17], [18]. During this process, each node sends a short permeable asynchronously prior to sending the data. ...
Conference Paper
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Scalable and efficient Medium Access Control (MAC) protocol has been of the paramount significance for boosting the performance of wireless sensor networks (WSNs). In this paper, scalable and efficient medium access control (SE-MAC) protocol is introduced for WSNs. The Goal of SE-MAC is to reduce the communication delay time, channel delay time and control delays caused by acknowledgment packets, request-to-send (RTS), clear-to-send (CTS) etc. Thus, reducing the delays, SE-MAC incorporates the adaptable application independent aggregation (AAIA) model to achieve the expected goals. Furthermore, SE-MAC is supported with handoff process feature, which helps extend the network lifetime. AAIA model for SE-MAC plays a role of cross-layering that extensively reduces the different delays incurred at MAC sub-layer and network layer. Evaluation of SE-MAC is conducted using network simulator-2 (NS2) then compared with known MAC protocols: Zebra medium access control (Z-MAC), receiver-initiated asynchronous duty cycle MAC (RI-MAC) and an energy-efficient multi-channel mac (Y-MAC). Based on the initial Simulation results, we demonstrate that SE-MAC protocol saves extra 9.8-15% time and energy resources for channel delays as compared with other MAC protocols.
... Wireless Sensor Networks (WSNs) comprises small size sensor nodes that are dispersed in the area of interest for monitoring the events used for gathering the data. Meanwhile, WSNs face several challenges at all network stacks such as excess energy consumption, scalability, mobility, coverage and decrease of throughput due to latency [1,2]. Several protocols have been introduced at each layer, but the network lifetime is of utmost importance. ...
Conference Paper
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Detecting the location of a mobile sensor node using signal strength has developed an area of dynamic research. The major issue in this situation curtails from the difficulty of how signals spread through space, particularly in the presence of hurdles such as people, walls and buildings. From another perspective, sensors are available with limited power capacity and energy resources. The signal strength guides the node to choose the right node for forwarding the data to the base station. In this paper, we propose a novel location tracking energy efficient (LTEE) model for wireless sensor networks. The presented model in this paper tracks the neighbor node based on the signal strength used for forwarding the data to the next-hop node. LTEE reduces energy consumption and prolongs the network lifetime. The simulation results demonstrated that LTEE consumed 8% to 12.5% less power as compared to other protocols.
... The wireless sensor networks comprise of tiny nodes with limited energy resources. They are scattered in the different regions of the network to collect the critical information from the physical environment [12]. These tiny nodes process the collected critical data to sink node in order to forward to end node. ...
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Cyber-physical systems (CPS) have developed rapidly in recent years, contributing to an efficient integration between the cyber and physical worlds in intelligent and connected city environments. However, efficient mobility in a CPS is not well solved. Here, we present a prototype for a privacy-aware secure human-centric mobility-aware (SHM) model proposed and tested to analyze physical and human domains in IoT-based wireless sensor networks (WSNs). The proposed SHM model involves five modules: sensor advertisements, mobile sensor recruitment, load balancing, transmission guarantee, and privacy with data-sharing phases. The proposed model is also validated using an accurate testing method that involves software and hardware tools and mathematical modeling to confirm secure communication. The model provides a trade-off between energy efficiency and quality-of-service (QoS) requirements and compares the performance with other known models/protocols. Our testing process continued for four days, demonstrating that the SHM model provides compelling features of a secure cyber-physical system based on actual testing results. In practice, our model can be used in hospitals, as evident from validation in a real-life environment following the protocols.
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In wireless sensor networks, nodes have a low computing capacity, a small antenna and a very limited energy source; thereby batteries are considered as a critical resource and should be used efficiently. On the other hand, the antennas are the biggest consumers of energy, therefore, and their use must be very efficient to minimize energy consumption. In a dense WSN, each node may route messages to destination nodes either through short-hops or long-hops, by using a short or a long radio range. Thus, the hop length optimization can save energy. In this article, the authors propose a theorem to optimize the hop lengths and a routing algorithm to improve the WSN power consumption. The theorem establishes a simple condition to ensure the optimal hop lengths which guarantees the minimum energy consumption. And the proposed algorithm based on that condition is used to find the optimal routing path. The simulation results are obtained by applying the condition and the algorithm on WSNs and reveals a high performance regarding WSNs energy consumption and network lifetime.
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In wireless sensor networks, nodes have a low computing capacity, a small antenna and a very limited energy source; thereby batteries are considered as a critical resource and should be used efficiently. On the other hand, the antennas are the biggest consumers of energy, therefore, and their use must be very efficient to minimize energy consumption. In a dense WSN, each node may route messages to destination nodes either through short-hops or long-hops, by using a short or a long radio range. Thus, the hop length optimization can save energy. In this article, the authors propose a theorem to optimize the hop lengths and a routing algorithm to improve the WSN power consumption. The theorem establishes a simple condition to ensure the optimal hop lengths which guarantees the minimum energy consumption. And the proposed algorithm based on that condition is used to find the optimal routing path. The simulation results are obtained by applying the condition and the algorithm on WSNs and reveals a high performance regarding WSNs energy consumption and network lifetime.
Conference Paper
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Wireless Sensor Networks (WSNs) are emerging as an effective solution for a wide range of real-life applications. In scenarios where a fine-grain sensing is not required, sensor nodes can be sparsely deployed in strategic locations and special Mobile Elements (MEs) can be used for data collection. Since communication between a sensor node and a ME can occur only when they are in the transmission range of each other, one of the main challenges in the design of a WSN with MEs is the energy-efficient and timely discovery of MEs. In this paper, we consider a hierarchical ME discovery protocol, namely Dual beacon Discovery (2BD) protocol, based on two different beacon messages emitted by the ME (i.e., Long-Range Beacons and Short-Range Beacons). We develop a detailed analytical model of 2BD assuming a sparse network scenario, and derive the optimal parameter values that minimize the energy consumption at sensor nodes, while guaranteeing the minimum throughput required by the application. Finally, we compare the energy efficiency and performance of 2BD with those of a traditional discovery protocol based on a single beacon. Our results show that 2BD can provide significant energy savings, especially when the discovery phase is relatively long.
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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 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.
Conference Paper
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Efficient MAC protocol has been paramount importance for improving the performance of WSN. In this paper, Boarder Node Medium Access Control (BN-MAC) mobility aware hybrid protocol is introduced for WSN. BN-MAC controls overhearing, idle listening and congestion problem to save energy. BN-MAC mechanism is based on novel semi synchronous low duty cycle that takes less time for accessing channel and faster delivery of data. The objective of introducing BN-MAC protocol is to support four application areas: monitoring and behavioral areas, controlling natural disasters, tracking and handling home automation devices and human-centric application areas. These application areas need contention free mobility support features with high delivery of data. BN-MAC also provides mobility support for these applications. Evaluation of BN-MAC is conducted using network simulator-2 (ns2) then compared with known low power listening (LPL) and X-MAC low duty cycles MAC protocols. Additionally, we have also compared BN-MAC with MAC hybrid protocols: Zebra medium access control) (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, Adaptive Duty Cycle SMAC (ADC-SMAC), low power real time medium access control (LPR-MAC) protocol. On basis of initial Simulation results, we demonstrates that BN-MAC protocol saves extra 18% to 45% energy resources as compared with other MAC protocols.
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We deploy BT node (sensor) that offers passive and active sensing capability to save energy. BT node works in passive mode for outdoor communication and active for indoor communication. The BT node is supported with novel automatic energy saving (AES) mathematical model to decide either modes. It provides robust and faster communication with less energy consumption. To validate this approach, network simulator-2 (ns2) simulation is used to simulate the behavior of network with the supporting mathematical model. The main objective of this research is to remotely access different types of servers, laptops, desktops and other static and moving objects. This prototype is initially deployed to control MSCS [13] & [14] from remote place through mobile devices. The prototype can further be enhanced to handle several objects simultaneously consuming less energy and resources.
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Fast expansion in ambient intelligence (AmI) has attracted different walks of people. AmI systems provide robust communication in open, dynamic and heterogeneous environments. This paper presents a AES paradigm that introduces wireless sensor networks to control remote servers or other devices at remote place through mobile phones. The main focus of paper is to consume minimum energy for obtaining the objectives. To realize the paradigm, mathematical model is formulated. The proposed paradigm consists of automatic energy saving model senses the environment to activate either the passive or active mode of sensor nodes for saving energy. Simulations are conducted to validate the proposed paradigm; we use two types of simulations: Test bed simulation is done to check practical validity of proposed approach and Ns2 simulation is performed to simulate the behavior of wireless sensors network with supporting mathematical model. The prototype can further be implemented to handle several objects simultaneously in university and other organizations.
A sensor network, unlike a traditional communication network, is deeply embedded in physical environments and its operation is mainly driven by the event activities in the environment. In long-term operations, the event activities usually show certain patterns that can be learned and exploited to optimize the network design. However, this has been underexplored in the literature. One work related to this is using Activity Transition Probability Graph (ATPG) for radio duty cycling [Tang et al. (2011) ActSee: Activity-Aware Radio Duty-Cycling for Sensor Networks in Smart Environments. Proc. IEEE INSS 2011, Penghu, Taiwan, June 12-15. IEEE Press]. In this paper, we present a novel Energy and Activity-aware Routing (EAR) protocol for sensor networks. As a case study, we have evaluated EAR with the data trace of real Smart Environments. In EAR an ATPG is learned and built from the event activity patterns. EAR is an online routing protocol, that chooses the next-hop relay node by utilizing: activity pattern information in the ATPG graph and a novel index of energy balance in the network. EAR extends the network lifetime by maintaining an energy balance across the nodes in the network, while meeting the application performance with desired throughput and low data delivery latency. We theoretically prove that: (i) the network throughput with EAR achieves a competitive ratio (i.e. the ratio of the performance of any offline algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) that is asymptotically optimal, and (ii) EAR achieves a lower bound in the network lifetime. Extensive experimental results from: (i) a 82 node Motelab sensor network testbed [Werner-Allen et al. (2005) MoteLab: A Wireless Sensor Network Testbed. Proc. ACM IPSN 2005, Los Angeles, CA, USA, April 25-27, pp. 483-488. IEEE Press, NJ, USA] and (ii) a varying size network (20-100) in sensor network simulator TOSSIM, validate that EAR outperforms the existing methods both in terms of network performance (network lifetime, network energy consumption) and application performance (low latency, desired throughput) for an energy-constrained sensor network. © 2012 The Author 2012. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as one of the most fundamental solutions for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakage-prone. In contrast to legacy energy conservation approaches, we aim at energy-synchronized computing for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match the activities of sensor nodes with dynamic energy supply from environments. We conduct system evaluation under both indoor and outdoor typical real-world settings. Results indicate our leakage-aware energy-synchronized control can effectively utilize energy that could otherwise leak away.
In this paper, we propose a minimum transmission energy consumption (MTEC) routing protocol that reduces energy consumption and prolongs network lifetime in user-centric wireless networks. MTEC is proposed for selecting the minimum transmission energy consumption path for data transmission based on the proportion of successful data transmissions, the number of channel events, the remaining node energy of nodes, and the traffic load of nodes. The simulation results showed that our proposed MTEC provided better packet delivery rate and throughput than DSR and TSA. MTEC also exhibited lower energy consumption during data transmission and a higher network lifetime than existing protocols.