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Scalable and Energy Efficient Medium Access Control Protocol for Wireless Sensor Networks

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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.
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Scalable and Energy Efficient Medium Access Control Protocol for
Wireless Sensor Networks
1
Abdul Razaque, Member IEEE
2
Khaled Elleithy, Senior Member IEEE
Wireless & Mobile Communication Laboratory Wireless & Mobile Communication Laboratory
University of Bridgeport University of Bridgeport
Bridgeport, CT-06604, USA Bridgeport, CT-06604, USA
1
arazaque@my.bridgeport.edu
1
arazaque@my.bridgeport.edu
Abstract 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.
General terms: Design; Experimentation; Performance;
Algorithms.
Keywords—MAC protocols; SE-MAC protocol; delays; Wireless
sensor networks; adaptable application independent aggregation
.
I. INTRODUCTION
Wireless Sensor networks have been appealing research area
since last decade [1] [2]. WSNs comprises of a large number of
sensor nodes disseminated in the field of interest for
monitoring the different events and activities. WSNs can be
deployed from the environmental monitoring to battle field
applications [3], [4], [5]. WSNs have not only improved the
living standard, but they experience the problems due to
limited battery power caused by several delays including
channel delays, transmission delays and control delays. The
delays affect the channel bandwidth and cause the additional
energy consumption. Reducing the delays, the MAC protocols
could play an important role in the WSNs when node shares
the communication channels.
The significance work is available in [6], [7], [8], [9] to
reduce the sleep delays, carrier sense delays, scheduling delays
for extending the network lifetime. However, the delays that
caused by the data aggregation are not properly handle. A few
contributions in [10], [11] attempted to reduce the data
aggregation delays, but limited with the MAC sub-layer.
Furthermore, some work for handling the data aggregation
delays is proposed in [12], [13]. However, restricted with only
network layer. Thus, there is a lack of mechanism to reduce
the possible delays at MAC and network layers simultaneously
when data are being aggregated.
Existing MAC protocols are not capable to cope with such
kinds of delays. As result, WSNs experience an additional
energy depletion and scalability issues. On the other hand,
achieving the quality of service (QoS) provisioning and energy
efficiency under assorted traffic conditions; the multi-channel
MAC protocols; (Z-MAC), (Y-MAC) and (RI-MAC) for
WSNs are introduced to reduce the channel delays .However,
these protocols focused on an idle listening and overhearing
delays, but marginally touched to the cross-layering delays
when aggregating the data.
These are some of the challenges, which need to be
addressed when designing scalable and efficient MAC protocol
for data aggregation. To address the cross-layering delays such
as communication delays, channel delays and control delays;
SE-MAC is introduced with support of AAIA model and
handoff feature, which focus on the cross-layering delays when
aggregating the data from MAC layer to network layer. This
contribution reduces the different delays by reducing the
energy consumption and prolonging the network lifetime. The
remaining of this paper is organized as follows. In section 2, an
intra-regional handoff communication process is explained. In
section 3, AAIA model is presented. In section 4, initial
experimental results are demonstrated and conclusion of the
paper is given in section 5.
II. 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. This short preamble
alerts to 1-hop neighbor nodes to be ready for receiving the
data packets. The communication process continues between
the sender and 1-hop neighborhood node until the receiver
initiates the handoff process. When a node wants to leave the
communication or node has to be a short of the energy, then
node incorporates the flag in the last sent packets to inform the
receiver about its current status. Once, a transmitter receives
the packets, then it assumes that the 1-hop communicating
node has to leave the communication (handoff) or will lose the
energy shortly. As, this situation helps the receiver side to
choose another alternate 1-hop neighborhood node prior to
happen this situation depicted in Figure 1. Hence, the sensor
node is enabled to maintain the QoS by choosing the alternate
node before discontinuing the communication with current
node. In addition, this process also helps the nodes either join
or leave the network that could improve the network
scalability. The handoff process is explained in algorithm 1.
Figure 1: Intra-regional communication handoff process
Algorithm 1: Handoff Process for QoS and Scalability
1. Input(S
n
: Sender node, R
n
: Receiver node, N
n
: 1-hop
neighbor node)
2. Output (
: Alternate path)
3. Parameters (
: Flag signal,

: Energy loss ,

: Handoff process)
4. Set S
n
initiates
the communication with R
n
5. Set S
n
uses
N
n
6. if N
n
intends to

or has a

then;
7. N
n
sends
embedded in the last packet to S
n
8. Set an advance detection process for
9. else if S
n
continues a communication through N
n
10. end if
11. end else
In line 4-5, sender node sends the data to the receiver node
using 1-hop neighbor node. In line 6-7, if 1-hop neighbor node
wants to leave the communication (handoff) or node is lacking
the energy, then it embeds the flag signal in the last sent
packet. The flag signal indicates to the sender that the node has
to handoff or lacks the energy. Subsequently, a sender node
starts detecting alternate path to continue the communication
without any disturbance explained in line 8. As, this process
helps improve the QoS and scalability. In line 9, the sender
continues the communication with another alternate 1-hop
neighbor node.
III. ADAPTIVE APPLICATION INDEPENDENT
AGGREGATION MODEL
We introduce AAIA model to support SE-MAC. The AAIA
resides between network layer and MAC sub-layer to provide
the cross-layered support for data aggregation. AAIA is
entirely independent of application. The primary objective of
this AAIA is to address the issues of energy limitation, low
bandwidth inherited by sensor technology. Another goal is to
employ the AAIA model to utilize the communication channel
efficiently. The AAIA aggregates with network layer to reduce
the overhead experienced by acknowledgment and other
delays.
A. AAIA Design Components
AAIA consists of following three components, which
collectively perform the joint task of data aggregation depicted
in Figure 2.
Processing Unit
Aggregation Function Unit
Service Access Unit
The processing unit performs the task of packet
aggregation and de-aggregation. Whereas, service access unit
controls timer setting and fine-tunes to perform the required
data aggregation. Once outgoing packets come from the
network layer, which are sent to the processing unit.
Subsequently, the processing unit forwards the packets to
aggregation function unit. The responsibility of aggregation
function unit is to apply one of the four addressing
methodologies for building the aggregate including anycasting,
multicasting, Unicasting, and Broadcasting. Finally, the built
aggregated is forwarded to the MAC sub-layer for
transmission.
Figure 2: Adaptive application independent aggregation model
The service access unit has to decide how many packets
need to be aggregated and when forwarding to MAC sub-layer.
Incoming traffic is similar to the out-coming traffic sent by the
MAC sub-layer and then forwarded to AAIA. As a result,
AAIA re-fragments the coming data packets into its original
network unit, then it passes to the router at the network layer.
The multiple network unit aggregation turns into a single
aggregation to transmit the data, which causes the reduction in
channel overhead, transmission overhead and including control
overhead packets such as RTS/CTS/ACK and
acknowledgment. Single aggregation helps save the contention
time on each transmission. Let us calculate the delays incurred
by the MAC protocol.
∆

,…,


 



where ∆: MAC delay for the packets, 
: MAC delay
without collision, : Successful transmission during the
number of collisions in period of time ‘t’ and
_
: Total
time for collision delay including time incurred for resolving
the collision.
Let us assume that several packets from different sensor nodes
in particular time interval 

are ready for transmission.
However, AAIA sends only an average number of packets

to compete for the channel to avoid the possible collision.
Thus, the number of transmitted packets

at given time with
respect to an average collision probability 

can be
obtained as

11




2
We need to identify the number of an average transmission
for the successful packet transmission 

that is obtained as

1
1  

3
After successful packet transmission, there is a need to
detect an expected number of collision 

against each
successful packet transmission, which can be calculated as
follows

1
1  1


/

4
Combining the equations (1) & (2), we can obtain the
approximate correlation 

between number of aggregated
packets and possible protocol delays.

 

,…,





1
1  1


/

5
In equation (5), we determine the total delay for
communication, channel delay and acknowledgment delay are
calculated.
IV. SIMULATION SETUP AND EXPERIMENTAL
RESULTS
We simulate SE-MAC, Z-MAC, Y-MAC and RI-MAC using
NS2 with Ubuntu 15.04 operating system. The network
consists of 270 sensor nodes that are randomly dispersed in the
400 × 400 square meter area. When the simulation starts, the
mobile sensor nodes move back and forth in the network area.
Each simulation continues for 20 minutes. We deploy the
pheromone termite (PT) routing protocol to route the data to
detect the shortest route as explained in [19].
We use different size of packets and consider a sensor
application module with a constant bit-rate source that helps
maintaining the QoS requirements. The detail of simulation
parameters is presented in Table 1.
TABLE 1: Simulation parameters and corresponding values
PARAMTERS VALUE
Size of WSN 400 × 400 square meters
Number of nodes 270
Medium Access Control
Protocol
Z-MAC, SE-MAC, (Y-MAC) and
(RI-MAC)
Queue-Capacity 40 Packets
Number of aggregation 90
Maximum number of
retransmissions allowed
03
Event distances 30 meters
Size of Packets 64, 128,192, 256 bytes
Initial energy of node 4.0 Joules
Sensing Range of node 30 meters
Transmitter Power 13 mW
Receiver Power 14.2 mW
Maximum bandwidth 260 kilobytes/second
Simulation time 20 minutes
Average Simulation Run 12
Based on the simulation, we obtained the initial results to
demonstrate the performance of the SE-MAC, Z-MAC, Y-
MAC and RI-MAC:
Time saving based on different number of
aggregation and packet sizes.
Energy saving at different number of aggregation.
A. Time-saving
To validate the AAIA, Figure 3 demonstrates the saving
time vs. number of aggregation on the different packet sizes.
From the Figure 3, we observe that as the number of
aggregation increases, the average saving time also increases
significantly. Furthermore, we also observe that as packet size
increases, then, time-saving also decreases. This situation
happens when data transmission time becomes a high ratio of
the entire transmission time. We discovered that when we used
a small packet size then we saved more time. As, this
discovery confirms if we need to forward the small amount of
data over the WSNs, then we need to pick small sized packet.
Figure 3: Number of aggregation VS time saving at different packet sizes
B. Energy-saving
In this experiment, we compared the energy-saving
capability of SE-MAC, Z-MAC, Y-MAC and RI-MAC using
variable number of aggregations. We observed in Figure 4 that
SE-MAC saves 0.8 joules as compared with other competing
MAC protocols as saved 0.64- 0.70 joules when increasing the
number of aggregations. The initially energy of node is set 5.5
joules for performing 20 complete monitoring cycles. As, the
number of aggregation increases then each protocol saves the
energy.
Figure 4: Number of aggregation VS energy- saving with 256 packet size
V. CONCLUSIONS
This paper introduces a SE-MAC protocol to reduce the
communication delays, channel delays and control delays over
the WSNs. SE-MAC uses AAIA model to reduce these delays.
The handoff process is handled using intra-regional handoff
communication process. This process could help improving the
QoS and scalability.
To demonstrate the soundness of the proposed SE-MAC,
we reported some exciting results by using ns2.35-RC7. We
have measured the performance of SE-MAC using different
number of aggregation and variable data packet-sizes. Based
on simulation, we discovered the small-sized packets save
more time that could be good choice for improving the QoS in
case we require to send small amount of data over the WSNs.
Furthermore, SE-MAC is compared with known delay-
reducing MAC protocols; Z-MAC, (Y-MAC) and (RI-MAC).
Simulation results demonstrate that SE-MAC has consumed
less energy as compared with other competing MAC protocols.
SE-MAC has saved 9.8-15% more energy resources than other
MAC protocols. In the future, we plan to analyze different
features of SE-MAC protocol in detail.
REFERENCES
.
[1] Razaque, Abdul, and Khaled M. Elleithy. "Energy-efficient boarder node
medium access control protocol for wireless sensor networks." Sensors
14, no. 3 (2014): 5074-5117.
[2] Razaque, Abdul, and Khaled Elleithy. "Robust Sink Failure Avoidance
Protocol for Wireless Sensor Networks." International Journal 2, no. 9
(2014): 721-731.
[3] Joshi, Yatish K., and Mohamed Younis. "Autonomous recovery from
multi-node failure in Wireless Sensor Network." In Global
Communications Conference (GLOBECOM), 2012 IEEE, pp. 652-657.
IEEE, 2012.
[4] Liu, Yunhuai, and Lionel M. Ni. "A new MAC protocol design for long-
term applications in wireless sensor networks." In Parallel and
Distributed Systems, 2007 International Conference on, vol. 2, pp. 1-8.
IEEE, 2007.
[5] Razaque, Abdul, and Khaled M. Elleithy. "Low Duty Cycle, Energy-
Efficient and Mobility-Based Boarder Node—MAC Hybrid Protocol for
Wireless Sensor Networks." Journal of Signal Processing Systems
(2014): 1-20.
[6] Lu, Gang, Bhaskar Krishnamachari, and Cauligi S. Raghavendra. "An
adaptive energy-efficient and low-latency MAC for data gathering in
wireless sensor networks." In Parallel and Distributed Processing
Symposium, 2004. Proceedings. 18th International, p. 224. IEEE, 2004.
[7] Rajendran, Venkatesh, Katia Obraczka, and Jose Joaquin Garcia-Luna-
Aceves. "Energy-efficient, collision-free medium access control for
wireless sensor networks." Wireless Networks 12, no. 1 (2006): 63-78.
[8] Lu, Gang, Bhaskar Krishnamachari, and Cauligi S. Raghavendra. "An
adaptive energy-efficient and low-latency MAC for data gathering in
wireless sensor networks." In Parallel and Distributed Processing
Symposium, 2004. Proceedings. 18th International, p. 224. IEEE, 2004.
[9] Sun, Yanjun, Shu Du, Omer Gurewitz, and David B. Johnson. "DW-
MAC: a low latency, energy efficient demand-wakeup MAC protocol for
wireless sensor networks." In Proceedings of the 9th ACM international
symposium on Mobile ad hoc networking and computing, pp. 53-62.
ACM, 2008.
[10] Van Dam, Tijs, and Koen Langendoen. "An adaptive energy-efficient
MAC protocol for wireless sensor networks." In Proceedings of the 1st
international conference on Embedded networked sensor systems, pp.
171-180. ACM, 2003.
[11] Çam, Hasan, Suat Özdemir, Prashant Nair, Devasenapathy
Muthuavinashiappan, and H. Ozgur Sanli. "Energy-efficient secure
pattern based data aggregation for wireless sensor networks." Computer
Communications 29, no. 4 (2006): 446-455.
[12] He, Tian, Brian M. Blum, John A. Stankovic, and Tarek Abdelzaher.
"AIDA: Adaptive application-independent data aggregation in wireless
sensor networks." ACM Transactions on Embedded Computing Systems
(TECS) 3, no. 2 (2004): 426-457.
[13] Ozdemir, Suat, and Yang Xiao. "Secure data aggregation in wireless
sensor networks: A comprehensive overview." Computer Networks 53,
no. 12 (2009): 2022-2037.
[14] Rhee, Injong, Ajit Warrier, Mahesh Aia, Jeongki Min, and Mihail L.
Sichitiu. "Z-MAC: a hybrid MAC for wireless sensor networks."
IEEE/ACM Transactions on Networking (TON) 16, no. 3 (2008): 511-
524.
[15] Kim, Youngmin, Hyojeong Shin, and Hojung Cha. "Y-mac: An energy-
efficient multi-channel mac protocol for dense wireless sensor
networks." In Proceedings of the 7th international conference on
Information processing in sensor networks, pp. 53-63. IEEE Computer
Society, 2008.
[16] Sun, Yanjun, Omer Gurewitz, and David B. Johnson. "RI-MAC: a
receiver-initiated asynchronous duty cycle MAC protocol for dynamic
traffic loads in wireless sensor networks." In Proceedings of the 6th ACM
conference on Embedded network sensor systems, pp. 1-14. ACM, 2008.
[17] Razaque, Abdul, and Khaled Elleithy. "Efficient Search (RES) for One-
Hop Destination over Wireless Sensor Network." arXiv preprint
arXiv:1310.1129 (2013).
[18] Razaque, Abdul, and Khaled Elleithy. "Least Distance Smart
Neighboring Search (LDSNS) over Wireless Sensor Networks (WSNs)."
In Modelling Symposium (EMS), 2013 European, pp. 549-554. IEEE,
2013.
[19] Razaque, Abdul, and Khaled Elleithy. "Pheromone termite (PT) model to
provide robust routing over Wireless Sensor Networks." In American
Society for Engineering Education (ASEE Zone 1), 2014 Zone 1
Conference of the, pp. 1-6. IEEE, 2014.
... Asynchronous WSN approaches include SE-MAC [16] which introduces a sub-layer called AAIA that lies between the network and MAC layers. AAIA combines network packets into a single MAC packet to reduce overhead. ...
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Wireless sensor networks often consists of a large number of low-cost sensor nodes that have strictly limited sensing, computation, and communication capabilities. Due to resource restricted sensor nodes, it is important to minimize the amount of data transmission so that the average sensor lifetime and the overall bandwidth utilization are improved. Data aggregation is the process of summarizing and combining sensor data in order to reduce the amount of data transmission in the network. As wireless sensor networks are usually deployed in remote and hostile environments to transmit sensitive information, sensor nodes are prone to node compromise attacks and security issues such as data confidentiality and integrity are extremely important. Hence, wireless sensor network protocols, e.g., data aggregation protocol, must be designed with security in mind. This paper investigates the relationship between security and data aggregation process in wireless sensor networks. A taxonomy of secure data aggregation protocols is given by surveying the current “state-of-the-art” work in this area. In addition, based on the existing research, the open research areas and future research directions in secure data aggregation concept are provided.
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In this paper we describe T-MAC, a contention-based Medium Access Control protocol for wireless sensor networks. Applications for these networks have some characteristics (low message rate, insensitivity to latency) that can be exploited to reduce energy consumption by introducing an active/sleep duty cycle. To handle load variations in time and location T-MAC introduces an adaptive duty cycle in a novel way: by dynamically ending the active part of it. This reduces the amount of energy wasted on idle listening, in which nodes wait for potentially incoming messages, while still maintaining a reasonable throughput. We discuss the design of T-MAC, and provide a head-to-head comparison with classic CSMA (no duty cycle) and S-MAC (fixed duty cycle) through extensive simulations. Under homogeneous load, T-MAC and S-MAC achieve similar reductions in energy consumption (up to 98%) compared to CSMA. In a sample scenario with variable load, however, T-MAC outperforms S-MAC by a factor of 5. Preliminary energy-consumption measurements provide insight into the internal workings of the T-MAC protocol.