Analyzing the MAC Protocols for Wireless Sensor
Network for Energy Efficiency
SUMMARY
SUBMITTED TO
MATS UNIVERSITY, RAIPUR (C.G.)
FOR THE AWARD OF THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
COMPUTER SCIENCE ENGINEERING
By:
P. UDAYAKUMAR
Under the Supervision of
Dr. RANJANA VYAS
Reader (On leave)
MATS UNIVERSITY
Raipur
(2013)
Analyzing the MAC Protocols for Wireless Sensor
Network for Energy Efficiency
SUMMARY
SUBMITTED TO
MATS UNIVERSITY, RAIPUR (C.G.)
FOR THE AWARD OF THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
COMPUTER SCIENCE ENGINEERING
Submitted By
P.UDAYAKUMAR
Under the Supervision of
Dr. RANJANA VYAS
TABLE OF CONTENTS
Abstract I
Chapter 1: Introduction 1
1.1 Overview of WSN 1
1.2 WSN Architecture 1
1.3 Evaluation of WSN 3
1.4 Application of WSN 4
1.5 Medium Access Control Protocols 4
Chapter 2: Related Work 6
2.1 Overview of MAC 6
2.2 Attributes of good protocols 7
2.3 Routing mechanisms 9
2.4 WSN Architecture 11
Chapter 3: Thesis Objectives 12
3.1 Energy wastein WSN 12
3.2 Designing energy efficiency 13
3.3 Energy calculation in election phase 13
Chapter 4: Noteworthy Contribution in the Field of Proposed Work 15
4.1 MAC protocols for WSNs 15
4.2 Analysisof MAC Protocols 18
4.3 Basic synchronization procedure 21
Chapter 5: Design of Proposed Methodology 22
5.1 Related work 22
5.2 Comparative analysis of MAC protocols 24
5.3 Problems in existing protocol and motivation of proposed protocol 26
5.4 Architecture of proposed system 26
Chapter 6: Implementation: Results and Discussion 30
6.1 Comparison of contention-based protocols 30
6.2 Comparison of cluster-based protocols 32
Chapter 7: Conclusion and Future Work 36
References 37
Appendix 40
List of Papers Published by the Candidate 41
LIST OF TABLES
Table 1.1 Hardware features of sensor nodes 2
Table 1.2. Evolution of wireless sensor network 3
Table 1.3 Comparison of WSN protocols 5
Table 2.1 Battery life estimation of sensor nodes 8
Table 2.2 Power used by Tyndall nodes 11
Table 4.1 Radio Model 18
Table 4.2 Current consumption in receiving and sleeping mode 20
List of Figures
Fig. 1.1 Architecture of WSN node 1
Fig. 1.2 Applications of WSN 4
Fig. 2.1 Protocol stack 6
Fig. 2.2 Two tired heterogeneous network 11
Fig. 3.1 Overhearing Mechanism 12
Fig. 3.2 Hidden and Exposed node 12
Fig. 4.1 S-MAC protocol design 15
Fig. 4.2 T-MAC Protocol Design 16
Fig. 4.3 Time-slot structure of TRAMA 17
Fig. 4.4 BMAC low power listening 20
Fig. 5.1 Energy Consumption of existing protocols 25
Fig. 5.2 Latency of existing protocols 25
Fig. 5.3 Energy consumption vs. number of sources 26
Fig. 5.4 Architecture of ETB-MAC protocol 27
Fig. 5.5(a) Sending CTS and RTS 27
Fig. 5.5(b) Exchange of Data and ACK packets 28
Fig. 5.5(c) Sending data 28
Fig. 5.6(a) Proposed RTS format 28
Fig. 5.6(b) Proposed CTS frame format 28
Fig. 5.7 Data transmission of ETB-MAC 29
Fig. 6.1 Random rooting tree of 60 nodes 30
Fig. 6.2 Average delay when sensing range 50m 31
Fig. 6.3 Average delay with varying sensing range 31
Fig.6.4 Packet delivery ratios 32
Fig.6.5 Average delay vs. number of CBR sources 32
Fig.6.6 Energy consumption with varying radius 33
Fig.6.7 Energy consumption with varying distance 33
Fig. 6.8 Energy withvarying node number 33
Fig. 6.9 Total energy consumption vs. number of nodes 33
Fig. 6.10 Total energy in larger network 34
Fig. 6.11 Total energy in smaller network 34
Fig. 6.12 Total energy in with varying number of nodes 34
Fig. 6.13 Average network life time 34
Fig. 6.14 Network life time vs. transmission radius 35
Fig. 6.15 Network life time vs. number of nodes 35
The purpose of this research is to investigate and design a new energy efficient protocol for
wireless sensor networks. The reason for choosing the theme “Analyzing the MAC Protocols for
Wireless Sensor Network for Energy Efficiency” was to explore a relatively new concept in
which our new protocol performs better than the existing protocols.
The first chapter gives an introduction and the architecture for wireless sensor networks. The
introduction includes history of WSNs, design issues and applications. The Medium Access
Control (MAC) protocols and its classifications have been discussed in this chapter.Chapter two
gives a detailed overview of WSN routing protocols.This chapter also gives the description of
MAC layer protocols. It also includes thecomputation of energy, performance and quality of
service of the protocols.
The main objective of our work is described in chapter three and in the fourth chapter we have
analyzed the noteworthy contribution in the field of proposed work. We have also analyzed the
merits and the demerits of the existing protocols. To increase the efficiency we have designed a
new protocol in chapter five. A detailed architecture of the proposed protocol isalso defined in
this chapter.Chapter six presents the performance analysis of the proposed protocol, giving a
detailed comparative analysis of the same with the existing protocols.
The conclusion chapter summarizes the investigations performed and the results obtained. The
scope and the possible future investigations also indicated. Symbols and acronyms used in this
summary are summarized in Appendix A.
Key words: Energy efficiency, MAC, WSN
1
CHAPTER 1
INTRODUCTION
1.1 Overview of WSN
A wireless sensor network consists of thousands of inexpensive tiny nodes with low power, low
cost digital signal processors and radio frequency circuits [1],[2], each having sensing capability
with limited communication power. The prospect of the sensor networks is accelerated by
MEMS (Micro Electromechanical Systems) [3] and radio frequency systems. They comprise of a
radio transceiver, microcontroller, power supply and the actual sensor. The main challenge in
sensor networks is to maximize the lifespan of sensor nodes. Other objectives of WSNs are
reliability, accuracy, cost effectiveness, fairness and throughput and security.
1.2 WSN Architecture
A typical wireless sensor node consists of five main hardware modules as shown in Figure 1.1:
(i) Microcontroller (ii) Radio transceiver (iii) One or more sensors (iv) Memory chip (v) Battery.
The WSN nodes usually wake up on the timer interrupt. They may also have application
dependent additional components such as location finding system and a power generator [1].
Fig. 1.1 Architecture of WSN nodes
2
1.2.1 Sensing unit
A sensor is a hardware device that produces a measurable response in signal to a change in
physical condition such as temperature, pressure, humidity, etc. Sensors send detected values to
the processor which runs the sensor operating system and manages the procedures required to
carry out the given sensing task.
1.2.2 Storage unit
Memory in a sensor node includes in-chip flash memory and RAM of a microcontroller and
external flash memory. Flash is used for persistent storage of application code and text segments
and static random access memory (SRAM) is used for runtime data storage.
1.2.3 Transceiver unit
A transceiver is responsible for wireless communication of sensor node. The operational states of
transceiver are: transmit, receive, idle and sleep. The transceiver unit consists of a radio and
antenna. The radio contains different operating modes like transmit, receive, idle and sleep for
power management purposes. The sleep mode provides the lowest power consumption.
1.2.4 Power unit
The power entity is generally composed of a couple of standard AA batteries. Many research [2]
[4] [5] are being conducted to minimize energy consumption and increase the lifetime of sensors.
Power can be stored mainly in batteries or also in capacitors. As shown in Table 1.1 TelosB
consumes the least power, but very limited in terms of storage and processing. On the other
hand, IMote2 is the most powerful in terms of processing, but it consumes a lot of power.
Table 1.1 Hardware features of sensor nodes
MICAZ(Crossbow) TelosB IMOTE2
Processor (MHz) 16 8 13-416
RAM(Kb) 4 10 256
Flash(K B) 512 1024 32000
Active(ma) 48 25 >45
Idle(ma) 8 2 >30
Sleep(µa) 15 6 388
1.2.5 Processing unit
The processing unit consists of mainly of processor; the responsibilities include controlling
sensors, gathering and processing sensed data, executing WSN applications, managing
communication protocols with the help of operating system.
3
1.3 Evolution of Wireless Sensor Network
Department of computer science, Carnegie Mellon Univ. Pittsburgh, USA organized a first
international workshop on sensor [4] on Distributed Sensor Networks (DSN) in 1978, followed
by DARPA organized DSN workshop in 1990 [6]. In 2001 DARPA launched a research program
called SenseIT [7], meanwhile IEEE initiated low data rate wireless personal area network
implemented by standard IEEE802.15.4 [8]. The ZigBee Alliance [9] has published ZigBee
standard for high-level communication protocols for WSNs. Currently, the companies like
Crossbow Technology [10] and Dust networks [11] were also started for implementation of
WSNs. Today WSN developers can choose from several existing and well-developed operating
systems such as TinyOS [12]. Many research programs are still in progress [13]-[17]. The
evolution of wireless sensor networks is shown in Table 1.2.
Table1.2. Evolution of wireless sensor network
Year Authors / Organization Journal/Conf.
/Workshop
Title Remark
1978 Dept. of computer science,
Carnegie Mellon Univ.
Pittsburg, PA, USA.
Workshop Distributed Sensor Nets
(DSN)
First International
workshop on
Sensor
1994 C. E. Nishimura and D. M.
Conlon
Workshop Monitoring Whales and
Earthquakes using SOSUS
Real time
application
2001 A.Manjeshwar and
D.P.Agrawal
Symposium TEEN: a routing protocol for
enhanced efficiency in WSN
Parallel and
Distributed
processing WSNs.
2002 A. Perrig, R. Szewczyk,
J.D.Tygar, V. Wen, and D.
E.Culler
ACM Journal of
Wireless
Networks
SPINS [15]: security
protocols for sensor
networks
Protocol for
security.
2005 M.Connolly & F.O‟Reilly Workshop
REALWSN‟05
Sensor Networks And The
Food Industry
Application in
Food Industries
2008 L.M.Ni International
Conference
SUTC‟08
Ubiquitous and Trustworthy
Computing
For Ubiquitous
computing
2011 Mo Sha, G.Hackmann &
Chenyang Lu, WQOS‟11
IEEE
Conference
Multi-Channel Reliability &
Spectrum Usage in Real
Homes
Empirical Studies
for Home-Area
Sensor Networks
2012 A.Saifullah, Chengjie Wu,
P.B.Tiwari, Young Fu &
Chenyang Lu
Symposium
RTAS‟12
Near Optimal Rate Selection
for Wireless Control Systems
Real time
Embedded
Technology
2013 P. Gireesan Namboothiri,
Krishna M. Sivalingam
Wireless Netw
2013
Throughput Analysis of
multiple channel based
WSNs [17]
Throughput
analysis for large
scale network
4
1.4 Applications of WSN
Wireless sensor networks has many applications, some examples are listed below Figure 1.2.
Fig. 1.2 Applications of WSN
Wireless sensor networks consisting of tiny devices which monitor physical or environmental
conditions such as temperature, pressure, humidity and noise level, home automation, monitoring
physiological health, inventory control and underground mines [18][19].
1.5 Medium Access Control Protocols
Medium Access Control (MAC) protocols play an important role in accessing the channel in
wireless communication. There are many issues in MAC protocol design, such as hidden
terminal problem, collision, overhearing, QoS, etc. Energy is very important for sensor nodes
because all the sensors are battery powered. Some issues may consume more energy, such as
collision, duplication of data or idle listening. Throughput, fairness and end to end delay are
related to QoS in the MAC protocol design.
MAC protocols are classified into three categories: CSMA based protocols, TDMA protocols
and hybrid protocols. In CSMA based protocols, the sensor nodes periodically wake up, listen to
Wireless Sensor networks
Vehicle Monitoring
Medical Monitoring
Animal Monitoring Machinery Monitoring
Navigation Monitoring
Aero-space Monitoring
BSC (Base station controller Processing)
Home Automation
Environmental monitoring
5
the channel and go back to sleep again. The advantage of using CSMA is that it has lower delay
and promising potential throughput at lower traffic loads.
PAMAS [20] is CSMA based protocol tries to avoid overhearing, but does not avoid collisions.
S-MAC [21] is an improvement over PAMAS, by making idle nodes shut off their radios, there
by reduces further wastage of energy. But it does not avoid collisions, which is a significant
wastage of energy. T-MAC [22] solves the S-MAC problem by using short non-sleeping period
when the channel is idle.
On the other hand, in TDMA based protocols, the slots are assigned for sensor nodes and these
nodes wake up and listen to the channel in that assigned slots and then go back to sleep in other
slots. The main advantage is that due to collision free medium access, it will increase throughput
at high traffic loads. But, at low traffic load throughput is decreased due to idle slots. TRAMA
[23] is a TDMA based protocol, using traffic-based scheduling algorithm to avoid wasting slots.
IEEE802.15.4 [8] is the hybrid protocol that is the combination of TDMA and CSMA. It allows
devices to access channels in a contention access period or a collision free period. Z-MAC [24]
and A-MAC [25] also the hybrid type protocols, robust to synchronization error. The comparison
of WSN protocols is shown in Table 1.3.
Table 1.3 Comparison of WSN protocols
Protocol Advantages Disadvantages Time
Sync
needed
Type Suffers
Hidden
Terminal
Problem
S-MAC, T-MAC,
TA-MAC, PAMAS,
B-MAC
Simple, flexible,
lower delay,
topology change at
any time
More collisions,
Low throughput at
higher traffic
No
CSMA
Yes
TRAMA, E-MAC
Increase throughput
At high traffic,
Collision free
Sync Problem, Low
throughput at low
traffic, Fixed topology
Yes
TDMA
No
Z-MAC, A-MAC,
Funneling MAC
Robust to sync
errors, collision
free, good
throughput
Fixed topology,
High overhead
Yes
Hybrid
yes
6
CHAPTER 2
RELATED WORK
2.1 Overview of MAC
The protocol stack used by the base station and sensor nodes is shown in Figure 2.1. The
protocol stack consists of five layers and three planes [1]. The layers are: the physical layer, data
link layer, network layer, transport layer and application layer. The planes are: power
management plane, mobility management plane and task management plane.
2.1.1 Physical layer
The physical layer is to meet the needs of receiving and transferring data collected from the
hardware. The physical layer is responsible for carrier frequency generation, frequency selection,
signal detection, modulation and data encryption, transmission and receiving mechanisms.
2.1.2 Data link layer
The data link layer should be power-aware and at the same time to minimize the collisions
between neighbors‟ signals because the environment is noisy and sensor nodes themselves are
highly mobile. The data link layer should meet the requirements for medium access, error
control, multiplexing and error detection and correction.
2.1.3 Network layer
The network layer is responsible to find efficient routing for the packet to travel on its way to a
destination. Network layer provide deliver the packets across the network.
Fig. 2.1 Protocol stack
7
2.1.4 Transport layer
The transport layer regulates traffic flow through the network to the distant end and provides
reliability measures. The transport layer divides large, upper layer application data into sequential
segments and also reorders and reassembles them into data packages for forwarding up to the
application layer. Transport layer can provide flow control and congestion control, and high-level
packet error checking.
2.1.5 Application layer
The application layer provides network services directly to the user for electronic mail, file transfers,
virtual terminal, and file servers [26][27]. Network layer also define the format and order of message
exchanges between processes operating on different network or subnet.
The power management plane is responsible for power utilization by the nodes. The Mobility
management plane manages the movement pattern of the sensor nodes, if they are mobile. The
task management plane schedules the sensing and forwarding responsibilities of the sensor
nodes. As wireless sensor networks are fully dependent on battery, the nodes should be
operational for years without the necessity of exchanging the batteries.
2.2 Attributes of Good Protocols
In order to design a good MAC protocol for WSNs, the following attributes are to be considered
[21] [28].
2.2.1 Energy efficiency
Energy efficiency defines as the energy consumed per unit of successful communication. It is
also defined as the ratio of the total energy consumed to the total energy transmitted. The lesser
the number better the efficiency. Since sensor nodes are battery powered, it is very difficult to
change or recharge batteries. The energy consumed by hardware of sensor node is given in Table
2.1.
8
2.2.2 Latency
Latency is referred as end-to-end delay. It is measured by the time interval between when a
message is queued for transmission at the physical layer until the last bit is received at the receiving
node. The latency is measured in seconds, and the performance rating decreases with increasing time.
2.2.3 Throughput
The network throughput is defined as the total number of packets delivered at the sink node per
unit time. It is better that sink node receives more data. Throughput is measured in bits/second
and packets/second, and the performance rating increases with higher rates.
2.2.4 Scalability
The capability of communication systems regardless of the number of sensor nodes performing a
transaction and the size of the network is called scalability. It refers the ability to accommodate
the change in the network size.
2.2.5 Stability
The communication system should handle the traffic congestion and sudden increase in loads is
called stability.
Table 2.1 Battery life estimation of sensor node
System specifications Current
Processor Current (full operation) Current (Sleep)
8 mA 8 µA
Radio Current in receive Current transmit Current sleep
8 mA 12 mA 2 µA
Logger Memory Write Read Sleep
15 mA 4 mA 2 µA
Sensor Board Current (full operation) Current (Sleep)
5 mA 5 µA
Computed battery capacity (mA-hr) 250 1000 3000
Battery life(months) 1.45 5.78 17.35
9
2.2.6 Fairness
Channel capacity should be fairly shared among the nodes. Usually fairness [28] is not very
important factor, but it increases the quality of service.
2.3 Routing Mechanisms
Many specific algorithms [29] [30] [31] have been proposed to solve these problems of routing
data in wireless sensor networks. These routing mechanisms could be classified as data-centric,
hierarchical and location based protocols depending on the network structure and applications.
2.3.1 Data-centric protocols
Data-centric protocols are query-based and depend on the naming data of interest, which can
reduce repeated transmissions. These protocols are able to select a set of sensor nodes and can
allocate data aggregation.
SPIN: Sensor Protocols for Information via Negotiation (SPIN) uses Meta data instead of a full
data packet transmitted at each node to all nodes [15]. SPIN ensures that low redundant data sent
throughout the network and solve problems, such as wasting energy and bandwidth to send extra
copies of data by sensors in the same area [1], of a broadcasting mechanism of flooding. SPIN is
more efficient and fairly simple, but it consumes more energy.
2.3.2 Hierarchical protocols
TEEN: Threshold Sensitive Energy Efficient (TEEN) is a hierarchical protocol designed mainly
for sudden changes in the sensed environment [14]. The sensor network architecture in TEEN is
based on hierarchical grouping. The core advantage of TEEN is that it works well in conditions
where quick changes in the sensed attributes occur. But, in large area networks TEEN tends to
consume considerable amounts of energy, because of long distance transmissions.
LEACH: Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm is to form clusters of
the sensor nodes based on the received signal strength, and use local cluster heads as routers to
the sink [31]. In LEACH mechanism the transmissions are mainly managed by cluster heads to
save energy. The drawback of LEACH is that the dynamic clustering brings extra overhead, such
as rotation of cluster head, advertisement etc., and therefore consumes energy.
10
PEGASIS: Power-Efficient Gathering in Sensor Information Systems (PEGASIS) is an energy
efficient protocol [32] that is slightly better than the LEACH. The PEGASIS is a protocol in
which each and every node communicates with a nearby neighbour for exchanging the data. In
PEGASIS, the cluster head selection neither follows the residual energy of the nodes nor the
location of the base station. The drawback of PEGASIS is that the nodes are grouped into chains
[33], which cause redundant data transmissions.
2.3.3 Location-based protocols
Since sensor nodes are randomly scattered where there is no addressing scheme like IP-
addresses. In most applications, location information is needed to optimize routing in an energy
efficient way. Some algorithms in this scheme are:
MECN: Minimum Energy Communication Network (MECN) [34] recognizes a relay region for
each node, which is consisted of nodes in a surrounding area. The main advantage is that MECN
dynamically adapts to eliminate the nodes to the new sensors since it is capable of self-
reconfiguring. But MECN is not widely used as it increases the overhead.
2.3.4 Network flow and QoS-based protocols
QoS-based protocols consider end-to-end delay requirements and establish paths in sensor
networks. A few examples of these are discussed in this section.
MLER: Maximum Life Energy Routing (MLER) [35] is used to maximally extend the network
lifetime by defining link cost as a function of residual energy of node. The protocol leads to
establish traffic distribution as a result of maximizing the lifetime of the network, that is a
possible solution to the routing problem in sensor networks.
SAR: Sequential Assignment Routing (SAR) is the first protocol [1] for WSN that includes a
notion of QoS. The main aim of the SAR algorithm is to minimize the average weighted QoS
metric throughout the lifetime of the network. The advantages include fault-tolerance and fast
recovery. But SAR suffers from the overhead of maintaining the table and states at each sensor
nodes.
11
2.4 Wireless Sensor Network Architecture
WSNs can be classified into three types of architectures namely, homogeneous, heterogeneous
and hybrid.
2.4.1 Homogenous sensor networks
The base stations and sensor nodes equipped with equal capabilities of computational power and
storage capacity. Data gathering is based on the structure of data dissemination. Flat and
hierarchical topologies belong to this group [36].
2.4.2 Heterogeneous wireless sensor networks
Heterogeneous WSNs have different types of nodes with different functions. The nodes have
different components depending on the type of sensors being used [37]. A two-tiered
heterogeneous network is shown in Figure 2.2. These nodes can be embedded easily [38]. The
energy consumption in different nodes is given in Table 2.2.
Table 2.2 Power used by Tyndall nodes
Mode Small node (10mm) Larger node (25mm)
Sleeping 20 µW 53µW
Processing 10 mW 29 mW
Accessing memory 13 mW 31 MW
Receiving/ listening radio 55 mW 75 mW
Transmitting of radio +10dbm 109 mW 128 mW
Figure 2.2 Two tired heterogeneous network
12
CHAPTER 3
THESIS OBJECTIVES
3.1 Energy Waste in WSN
The main objective of WSN is the lifetime maximization. Radio transceiver is the main energy
consumer. MAC protocols play an important role to control the operation of radio and it
significantly affects the energy consumption of the whole network. Major sources of energy
waste are basically classified into five types [9] [24] [39].
3.1.1 Collision: When at least two sensor nodes try to access the communication channel at the
same time, data collision occurs. The collided packets must be retransmitted and this leads to
wastage of energy.
3.1.2 Packet overhead
Sending, receiving and hearing certain control packets in WSN also consume more energy.
3.1.3 Idle listening
In wireless sensor networks a node listens to the traffic if it is in idle state. If a node does not
transmit or receive, if it has no packets, it means that the node is in listen mode.
3.1.4 Overhearing mechanism
When a node picks up packets that are destined to other nodes is called overhearing. Overhearing
consumes more energy, which may increase when traffic load is heavy. In Figure 3.1, node A
overhears the transmission of packets from the node B to C.
Fig. 3.1 Overhearing Mechanism
Fig. 3.2 Hidden and Exposed node
13
3.1.5 Hidden node problem
In Figure 3.2, nodes P and R are within the range of node Q, but they are not in the range of each
other. If node P is communicating to node Q, and node R wishes to communicate to node S, node
R may sense the channel and finds it idle. Otherwise, it causes collision at node Q.
3.2 Computing Energy Efficiency
Energy efficiency is defined as the ratio of throughput versus energy consumed [33].
Energy Efficiency = Throughput / Energy consumed.
Energy consumption in ordinary sensor node is computed as follows:
Sensing Energy Es: The energy used to activate sensing circuitry within the node. The magnitude
of this energy depends on task that is assigned to the sensor.
Transmitter energy Et: The energy needed for transmission of data. It is depending on transmitter
power, size of data packet, and the data transfer rate.
Receiver Energy Er: A sensor node is also in charge of receiving packets from other nodes is
called receiver energy.
Computation energy Ec: Sensor processing unit must be activated to process the circuitries.
The total energy Etot = Es + Et + Er + Ec.
3.2.1 Energy in S-MAC
The total energy Etot can be given by the sum of the overhearing avoidance Eoa and the ratio of
sleep phase and discover phase Tsl/Tdis.
Etot = Eoa + Tsl/Tdis.
3.2.2 Energy in T-MAC
T-MAC uses adaptive duty cycle. Energy for idle listening Eid and sent messages Est, received
messages Ers, and overhead Eoh, and the total energy Etot can be calculated as
Etot = Eid + Est + Ers + Eoh + (Tsl – Ta)/Tdis.
3.3 Energy Calculation in Election Phase
The distance d between a cluster head CH and base station BS for long distance is given by ld.
The energy consumed to transmit b bits of message is given by:
Etrl = b*Eelec + b*Eamp * ld ----------------------------------------------------(3.1)
14
Where Eamp is the energy consumed by amplifier and Eelec is energy consumed by Electronic
circuit. Energy consumed for transmission with b bits of message for shorter distance sd is given
by:
Etrs =b* Eelec +b* Efs * sd -------------------------------------------------------(3.2)
Where Efs is Energy consumed in short distance by the amplifier. Energy consumed to receive b
bits of message is given by:
Erc=b* Eelec ------------------------------------------------------------------------(3.3)
Let number of sensor nodes is n and c is the clusters, then totally n/c nodes in each cluster. The
energy consumed by cluster head CH from equations (2.2) & (2.3) is given by:
Ech = b* Eelec +b*Efs* sd + {(n/c-1) * (b* Eelec)} ---------------------------(3.4)
Energy consumed by non-cluster head Enc is given by:
Enc=(K*b* Eelec) +{(b* Efs * sd) + (b* Eelec) }-------------------------------(3.5)
Energy consumed to receive messages from the remaining nodes that are not part of the group of
cluster head [39] is given by:
Enrc = (n/c-1) *(Eelec +Eag) --------------------------------------------------------(3.6)
Where Eag is the energy consumed during data aggregation. The total energy consumed by
cluster head from equation (1) and (6) is given by:
Echt = (b* Eelec + b* Eamp * ld) + {(n/c-1) *( Eelec + Eag)} ------------------(3.7)
Normally the sensor nodes sense the environment and transmit data to the cluster heads. The
cluster head receives all the data and aggregates it before sending it to the base station.
15
CHAPTER 4
NOTEWORTHY CONTRIBUTION IN THE FIELD OF PROPOSED WORK
4.1 MAC Protocols for Wireless Sensor Networks
We introduce various MAC protocols for wireless sensor networks. These protocols are
classified into contention-based, contention-free and hybrid protocols.
4.1.1 Contention-Based protocols
S-MAC: S-MAC [21] consists of three major components: periodic listen and sleep, collision
and overhearing avoidance, and message passing as shown in Figure 4.1.
Normal
Active state
Sleep state
S-MAC
Fig. 4.1 S-MAC protocol design
Periodic Listen and Sleep
In many sensor network applications, if no sensing event occurs, then nodes remain idle for a
long time. The data rate during this period is considerably low. In comparison to TDMA
schemes with very short time slots, S-MAC requires low synchronization among neighbouring
nodes, which are free to choose their own listen/sleep schedules. The drawback of the scheme is
that latency is increased due to periodic sleep of each node.
T-MAC: T-MAC (Timeout MAC) [22] is a contention-based, adaptive energy efficient MAC
protocol for wireless sensor networks. T-MAC reduces energy consumption by introducing an
active/sleep duty cycle. TDMA-based protocols are naturally energy preserving, because they
have a duty cycle built-in, and do not suffer from collisions [40].
16
Normal
Active time
TA Sleep time TA TA
T-MAC
Fig. 4.2 T-MAC Protocol Design
Every node in T-MAC periodically wakes up to communicate with its neighbors using a
Request-To-Send (RTS), Clear-To-Send (CTS), Data, Acknowledgement (ACK) scheme, that
provides both collision avoidance and reliable transmission [11], and then goes to sleep again
until the next frame. In the active period, a node will keep listening and potentially transmitting
the messages. When no activation event has occurred then active period ends for a time TA as
shown in Figure 4.2. T-MAC consumes much less energy than S-MAC, but it suffers from the
early sleeping problem.
R-MAC: Reservation-MAC (R-MAC) [41] is a contention-based protocol, uses two separate
periods during the communication process. Initially, nodes compete for time slots reservation for
their future transmissions, and in the next period, each node either transmits data or receives data
from the corresponding sender. R-MAC is slightly better than S-MAC and T-MAC in energy
consumption.
TA-MAC: TA-MAC protocol is based on S-MAC protocol uses both active and sleep periods.
The duty cycle mechanism proposed in the S-MAC protocol is energy efficient, it increases
packet forward latency but reduces throughput in the network. TA-MAC protocol [5] solves this
problem by using Busy-Signal (BS) packet. The main function of the TA-MAC is if a node fails
to get the medium, it goes to sleep. It is active when the receiver is active.
17
4.1.2 Contention-Free protocols
TRAMA: Traffic adaptive medium access protocol (TRAMA) [23] reduces energy consumption
by using unicast, multicast and broadcast transmissions. TRAMA uses a distributed election
scheme in slotted time and determines which node can transmit at a particular time slot. TRAMA
is fair and avoids collisions. TRAMA consists of three components, namely, neighbor protocol
(NP) which gathers information from neighbor nodes, Schedule exchange protocol (SEP) used to
exchange two-hop neighbor information and programs and lastly, adaptive election algorithm
(AEA) decides on the nodes in current time zone.
Fig. 4.3 Time-slot structure of TRAMA
There are two types of time slots namely random access slots for signaling and schedule access
slots for transmission as shown in Figure 4.3. In random access mode, each node transmits by
selecting a slot randomly. All nodes must be in either transmit to neighbors or receive from the
neighbors.
DE-MAC: Distributed Energy-Aware-MAC (DE-MAC) is a TDMA based MAC protocol [42]
to address the energy management problem in WSNs. It employs a periodic sleeping mechanism
to avoid idle listening and overhearing. In the beginning of the election process, each node sends
its energy level to all of its neighbors. A node with the minimum energy level is elected at the
end of election process. One or more winners are elected by this process and winners have time
slot twice the number of losers. This gives more energy savings compared to TRAMA.
4.1.3 Hybrid protocols
Z-MAC: Zebra-MAC (Z-MAC) is hybrid protocol [24], the combination of both CSMA and
TDMA. Z-MAC is robust to synchronization errors but its performance always falls back to that
of CSMA. Z-MAC is robust in synchronization error, slot assignment failures and time varying
18
channel conditions. But the problem in Z-MAC is that it does not solve hidden terminal
problem, and also it suffers huge overhead. Hence it is not suitable for individual nodes.
Funneling-MAC: Funneling-MAC is the combination of hybrid TDMA and CSMA/CA MAC
protocol [43]. It has a unique funneling effect [44], in this the events generated by the sensor
fields travel hop-by-hop in a many-to-one traffic pattern towards different sinks. It uses local
TDMA scheduling the funneling region alone to give additional scheduling opportunities to the
nodes near to the sink.
4.2 Analysis of MAC Protocols
This thesis analyses the MAC protocols and the models are validated and compared in detailed
simulations. Each simulation result is the result of more than 15 runs with the outcome of the
factors like collisions, latency, and the likes.
4.2.1 Energy consumption
The main constraint on sensor network is that sensors rely on batteries. As sensors used in large
numbers it will be difficult to change or recharge batteries in the sensors. A classical energy
model was proposed by [44] in Table 4.1, consists of low power consumption radio.
Table 4.1 Radio Model
Radio Mode Energy Consumption
Transmitter Electronics (ETx-elec)
Receiver Electronics (ERx-elec)
Eelec = ETx-elec = ERx-elec
50nJ/bit
Transmitter Amplifier (Eamp) 100 pJ/bit/m2
Idle (Eidle) 40nJ/bit
Sleep 0
Energy used in transmitting or receiving one bit is found by using power value, i.e.
Energy = power * time.
The energy consumption of Mica2 is the sum of energy transmitting, receiving, listening,
sampling and sleeping [45]. The calculation of energy in transmitting and receiving one bit is
given as:
19
Energy = Current * Voltage * Time …………………………….………..(4.1)
According to [46], a node sends\ a packet every 80msec and every packet will take 9msec for
sending and receiving. When a node receives a packet it will retransmit immediately. Let B be
the bakeoff time, time to switch to radio mode is STx, i.e. 250 µsec, Time to transmit a frame is
Txt and Srx be the time to switch to receive mode. Then time frame can be calculated as:
TF = B + STx + Txt + Srx ……………………………………………….(4.2)
If Rxt is the time taken to receive a frame, then the time cycle can be calculated as,
T = B + 2STx + Rxt + Txt ………………………………………………..(4.3)
Typical sources of energy loss in wireless sensor networks include idle listening, packet
collisions, protocol overhead, and message overhearing.
(A) Idle Listening
Idle listening occurs when a station listening an inactive medium. For example Chipcon CC2420
transceiver of 250 kbps [47] node can transmit 4.1ms energy. There are four techniques to reduce
idle listening. They are: static sleep scheduling, dynamic sleep scheduling, preamble scheduling
and off-line scheduling.
(B) Static Sleep Scheduling
Every frame in S-MAC protocol is divided into listen and sleep periods as discussed in section
4.1.1 is further divided into the synchronization period and data transfer period. In sleep period a
node listens for a SYNC message from its neighbour. Within the time period, if a node does not
hear any SYNC message then the node will set and broadcast its own sleep schedule.
(C) Dynamic sleep schedule
T-MAC protocol as discussed in section 4.1.1 introduces a listening timeout mechanism by
dynamically adapting an active listening period in response to network traffic to improve idle
listening overhead. T-MAC achieved five times the energy savings than S-MAC.
(D) Preamble scheduling
BMAC (Berkeley-MAC) [45] and WiseMAC [48] approach to organize sleep schedules by
allowing nodes to adopt sleep schedule with fixed sleeping cycle frequency. As shown in Figure
4.4, if a node senses activity, it wakes up, synchronizes and receives the packet.
20
Fig. 4.4 BMAC Low power listening
(E) Offline scheduling
Traffic-Adaptive MAC (TRAMA) is a schedule-based protocol described in section 4.1.2 uses an
Adaptive Election Algorithm (AEA) to randomly assign time slots. TRAMA establishes
collision-free data transfer to maximize sleep time for effective utilization of channel and
minimize latencies.
4.2.2 Frame collisions
When a wireless sensor node sends a MAC protocol frame, or message, which collides in time
with another message frame, collision occurs. In most single-channel radios, the radio cannot
simultaneously receive while in transmit mode. S-MAC and T-MAC protocols use contention
and RTS-CTS exchanges to reduce collisions.
4.2.3 Protocol overhead
Wireless protocol overhead consumes both energy and bandwidth. The networks serve as an
integrated system to transfer data between distributed application layer programs and provide
reliable data delivery. Adding data message headers and 2-to-1 Manchester encoding to the RFM
TR1001 [49] transceiver reduces an effective 60% [50] reduction.
4.2.4 Message overhearing
Receiving and discarding messages projected for other nodes is called message overhearing.
Receiving all messages is an efficient method will increase throughput and decrease latency
specifically in cases where the radio receive mode spends more energy than the transmission
mode.
Table 4.2 Current consumption in receiving and sleeping mode
Radio Receive Mode Power-down mode
CC2420 [48] 19.7A 1A
CC1000 [48] 9.6A 0.2A
Sending Node
Receiving Node
Sample period
Preamble
Sample period Sample period
21
4.2.5 Node energy capacity
IEEE 802.15.4 WSN transceiver platforms operates on two AA batteries and can achieve
approximately 3000mAh assuming 2.1 volt cutoff and a 20mA slow drain application [51]. The
current consumption of CC2420 and CC1000 is given in Table 4.2.
4.3 Basic Synchronization Procedure
In WSN synchronization transfer of time value from one node to another is the main procedure.
The method is very simple, read the clock, put the time value in radio packet and finally send it.
Node A read the time value Ta and node B can read the time value Tb. The difference in time D
can be computed as,
D = Tb-Ta ………………………………………………… (4.4)
Round-trip synchronization:
In this procedure, node A sends a packet with timestamp T1 to node B. After the communication
delay F, node B receives the packet and records its time T2.
T2 = T1 + F + D ………………………………………………... (4.5)
Now node B sends a response packet with timestamp T3 to node A. when the second packet
arrives, node A makes timestamp T4. The value of T4 is computed as,
T4= T3 + F – D …………………..…………………………….. (4.6)
Equation (3) – (2) gives the clock offset D
T4 – T2 = T3 + F – D – T1 – F – D ………..……………………………(4.7)
D = (T3 – T1 – T4 + T2) / 2 ……………………………………….……(4.8)
Sum of the equations (2) & (3) can be used to compute communication delay F
T2 + T4 = T3 + F – D + T1 + F + D ………………………………..… (4.9)
F = (T4 + T2 – T3 – T1) / 2 ………………..…………………………....(4.10)
If the value of D is known, node A can estimate the time of node B.
22
CHAPTER 5
DESIGN OF PROPOSED PROTOCOL
5.1 Related work - Clustering Algorithms
The clustering algorithm provides network scalability and energy efficient communications by
minimizing transmission overhead and increasing transmission consistency. Clustering method is
used to save communication bandwidth since it limits the scope of inter-cluster interactions to
cluster heads and avoids duplicate messages among sensor nodes [52]. Moreover, clustering can
stabilize the network topology at the level of sensor nodes and thus reduces the maintenance
overhead [53].
5.1.1 LEACH
In recent years many clustering algorithms on self-configuring clustering had been presented for
energy efficiency. Low-Energy Adaptive Clustering Hierarchy (LEACH) [44][54] is used in
distributed algorithms in which clustering explicitly encourages data aggregation to reduce the
transmission burden in the network. Later, the low energy adaptive clustering hierarchy with
deterministic cluster head selection (DCHS) was proposed [55]. It extends LEACH‟s algorithm
by a deterministic component followed by hybrid energy-efficient distributed clustering (HEED)
[56].
Operation of LEACH
The operation of LEACH is classified into number of rounds. Each sensor node elects itself to be
a cluster head at the beginning of round r + 1 (which starts at time t) with probability Pi(t). Pi(h)
is chosen such that the expected number of cluster heads for this round is k. Thus, if there are N
sensor nodes in the network, the expected number of cluster heads is:
E[N] = …………………………………….(5.1)
Each and every sensor nodes to be a cluster head in N/k rounds on average. Ci(t) is denoted as
the indicator function determining whether or not sensor node i has been a cluster head in the
23
most recent (r modN/k) rounds, then each sensor node should choose to become a cluster head at
round r with probability:
……………………5.2)
In this case, only sensor nodes that have not already been cluster heads recently may become
cluster heads at round r + 1.
In our research, we divide these clustering algorithms into auto-configuring cluster formation
and centralized cluster formation. In centralized cluster formation, the base station elects leaders
(cluster heads) each round to afford guarantee about the placement and number of cluster heads
by a centralized clustering scenario. Hence, these protocols often need sensor nodes to be
equipped with high-sensitivity global positioning system receivers for gathering position
information of sensor nodes. In auto-configuring cluster formation, each sensor node makes
autonomous decisions itself by means of distributed algorithm. The major advantages of this
approach are that no long-distance communication with the base station is required and
distributed cluster formation can be done without the exact location information of the sensor
nodes in the network. Finally, no global communication is needed to set up the clusters [55].
5.1.2 Deterministic Cluster Head Selection (DCHS)
DCHS is the modified version of LEACH designed to increase the lifetime of LEACH. Equation
(3) shows the sensor node‟s residual energy, which increases the probability of any sensor nodes
that have not been a cluster head for last K/N rounds.
………..(5.3)
Where r is the number of consecutive rounds in which a sensor node has not been a cluster head.
and denote the residual and initial energy for sensor node n respectively. r is reset to 0
when a sensor node becomes a cluster head. For the deterministic selection of cluster heads only
local and no global information is necessary. Also, the nodes determine themselves whether they
become cluster heads.
24
5.1.3 Hybrid Energy-Efficient Distributed clustering (HEED)
HEED also based on clustering algorithm, but unlike LEACH, it does not select cluster heads
randomly [53]. The sensor nodes that have a high residual energy only can become CH. Its main
characteristics are:
To achieve well distribution of cluster heads in the network.
Avoiding the probability that two sensor nodes within each other‟s transmission range
becoming cluster heads.
Each sensor node is mapped to exactly one cluster and can directly communicate with
CH.
5.2 Comparative Analysis of MAC Protocols
MAC is a wide-ranging research area where many researchers have done research work in the
area of MAC protocol for wireless sensor networks. Contention-based MAC protocols are
widely used because of its simplicity and robustness [57] to the hidden terminal problem. Due to
idle listening, energy consumption for these contention-based protocols is very high. TDMA-
based MAC protocols [58] on the other hand are based on reservation and scheduling and have
the natural advantage of energy conservation because of the duty cycle of radio is reduced and
also there is no overhead and collisions. SAS-TDMA [59] improves the quality of service for the
entire network. It achieves significant improvements for realistic dynamic wireless sensor
networks when compared to existing scheduling algorithms with the aim to minimize latency for
real-time communication.
S-MAC as discussed in section 4.1.1 is a contention-based MAC protocol uses fixed duty cycle
and reduces energy consumption by putting nodes into sleeping mode intermittently, but it is
unable to adapt its operation to varying traffic rates. TA-MAC has two mechanisms. First one is
an adaptive contention window, which can protect data from loss due to limit of the buffer. The
second method is to use BS packet, which tries to tell nodes a few hops away about the current
transmission so that they can wake up in time to continue the transmission. L-MAC is well suited
for data collection applications in which sensors have to report to sink nodes or base station
25
through multiple nodes. R-MAC protocol mainly focuses on overhearing avoidance by adjusting
the listen-sleep durations according to the traffic load of the network. R-MAC reduces the
collisions and also uses variable duration of the listen and sleep periods according to the traffic
load.
Simulation Setup
We use Castalia 3.0 in OMNET++ [60] [61] software to simulate the three MAC protocols under
the same scenario. Every node except sink collects information and sends messages to the sink
node through several hops. We have done different tests to measure energy consumption.
Fig. 5.1 Energy Consumption of existing protocols
Fig. 5.2 Latency of existing protocols
Figure 5.1 shows the energy consumption of S-MAC, L-MAC and TA-MAC protocols. It shows
that S-MAC uses more energy than L-MAC and TA-MAC. Energy consumption increases with
increase in message inter-arrival time. In our experiment, it is seen that when traffic is light L-
MAC and TA-MAC protocols can save more energy than S-MAC protocol due to idle listening.
It is seen from Figure 5.2 that the latency of L-MAC is much less than the other two protocols.
Under the high traffic, and TA-MAC and S-MAC suffer the huge delay. Further TA-MAC
suffers more latency than S-MAC.
26
Fig. 5.3 Energy consumption vs. number of sources
We have compared S-MAC, T-MAC, R-MAC and TA-MAC protocols for energy consumption
vs. number of sources. It is shown in Figure 5.3 that if the number of sources increased, then the
energy consumption will be slightly decreased. S-MAC consumes more energy than other
protocols. TA-MAC consumes less energy than S-MAC, T-MAC and R-MAC protocols.
5.3 Problems in the Existing Protocols and Motivation of Proposed Protocol
The first drawback of existing protocols (S-MAC, T-MAC and TRAMA) is the increased
idle listening.
The second limitation is overhearing. This is due the fact that the sensor nodes listen to
messages that were not projected for them.
The third problem is collisions. The sensors waste energy in retransmitting the collided
packets.
Many authors have designed the proposals (R-MAC, OB-MAC and SIFT) to solve these issues,
but only to a certain level. TRAMA is a schedule-based protocol that attempts to solve these
problems by using distributed election algorithm. It uses Neighbor Protocol (NP) to get
information from the neighbor nodes. This increases overhearing, which is the motivation to
design a new protocol: Energy Efficient Token Bus Protocol (ETB-MAC) that can handle these
problems.
5.4 Architecture of Proposed System
ETB-MAC architecture is shown in Figure 5.4 in which the data related to this event can be
considered an urgent traffic and must be delivered to the sink (cluster head).
27
Fig. 5.4 Architecture of ETB-MAC protocol
ETB-MAC designed to provide low latency and an energy efficient path to deliver this urgent
traffic from the source node generating this traffic towards the cluster head.
Fig. 5.5 (a) Path established from S node to Sink
Initially a node starts a transmission by sending an RTS packet indicates that it has a packet to
send. Upon receiving an RTS packet, each neighbour node decides to join the communication by
sending message, otherwise simply transmits the message to the neighbouring nodes except the
node from where the message comes from. The sender sends the data through the path as shown
in Figure 5.5(a) by S-A-B-C-D-E-F-Sink. In our approach, only the nodes closer to the
destination are considered for sending data.
S
A B
C
D E
F
Sink
28
Fig. 5.5(b) sending CTS and RTS
Fig. 5.5(c) sending data
5.4.1 Format of RTS and CTS
The structure of RTS and CTS are proposed as shown in Figures 5.6 (a) & 5.6 (b) without
violating the IEEE802.11 standard. NNA stands for Next-Node-Address obtained by the sender‟s
routing agent and defines the address of the next node in the routing path in which the packets
are transmitted. SA is Sender-Address defines a node ignores receiving a CTS in which address
of previous node is specified. The CTS and RTS messages have identical structure of frame
format. FC refers to frame control and CRC is cyclic redundancy check.
FC Duration(NAV)
Address of
Previous Node
NNA Sender
Address CRC
Fig. 5.6(a) Proposed RTS frame format
FC Duration(NAV)
Address of
Previous Node
NNA Sender
Address CRC
Fig. 5.6(b) Proposed CTS frame format
29
When a node has data to transmit to sink, it takes immediate procession of the medium and also
informs the neighbors of that decision. Suppose a node has no data, it turns off its radio to save
energy. Normally if a medium is allocated to one node, other nodes also try to occupy the
medium.
5.4.2 Description of algorithm
The node (S) begins by sending RTS packets to the next neighbor node (A) as shown in Fig.
5.5(b). After receiving control packets, node (A) replies CTS message back to the sender. All
other neighbors of sender and receiver turn off their radio and go to sleep except the next node
(B) of the receiver. The node (A) now gets DATA packets from S after getting CTS packets from
the next node (B) as shown in Fig. 5.5(c). The sender S exchanges DATA/ACK packet from
node A and the forwarding process of CTS packets continues between the nodes included in the
path B-C-D-E-F till it reaches the Sink. From the Figure 5.7, it is shown that for each
DATA/ACK packets corresponds to transmission time „T‟ during which each neighbor can be
switched off its radio to save energy. That means in our proposal the start node S can transmit
RTS/CTS/DATA/ACK to its neighbor A within the time duration T. Sometimes the contention
problem may arise when two RTS packets collide in case two source nodes try to transmit
DATA packets at the same time. But it can be resolved by using Backoff algorithm.
Fig. 5.7 Data transmission of ETB-MAC
30
CCONTENTION-BASEDHAPTER 6:
IMPLEMENTATION: RESULTS AND DISCUSSION
6.1 Comparison with Contention-Based Protocols
In this chapter, we provide extensive simulation results and comparisons of the performance
between our ETB-MAC algorithm and two other popular routing algorithms like S-MAC and T-
MAC in terms of throughput (packet delivery ratio) and latency under variable workload
conditions. We use Castalia-3 on OMNeT++ simulator to evaluate the performance of our new
ETB-MAC protocol.
In our simulation, sensor nodes N are randomly distributed in a [100m × 100m] flat area with
transmission radius R from 50m to 200m depending upon the density of the network with data
length 2000 bits. The sink node is located at one end as shown in Figure 6.1. In addition, all
sensor nodes are stationary. When an event happened, a node senses it and sends a report to the
sink.
Fig. 6.1 Random rooting tree of 60 nodes
6.1.1 Performance analysis
In this section, we study the average delay with different sensing range and throughput with
varying CBR sources. We compare our ETB-MAC algorithm with well-known contention-based
31
protocols of S-MAC and T-MAC. ETB-MAC1 is the output if our algorithm is implemented
with S-MAC and ETB-MAC2 is the output if our algorithm is implemented with T-MAC.
We plot the average delay as a function of the number of event reports required when we fix the
sensing range of 50m as in Figure 6.2. It shows that even with the higher number of reports
required, ETB-MAC outperforms S-MAC and T-MAC in terms of the end-to-end delay.
Next we would prefer to consider the case when the sending nodes are scattered throughout the
network. In this experiment, we randomly place 120 nodes in the simulation area. After
observation we find that at every 0.5 second duration of the 100 seconds simulation time, ETB-
MAC achieves better latency than S-MAC and T-MAC, which is illustrated in Figure 6.3.
Fig. 6.2 Average delay when sensing range 50m
Fig. 6.3 Average delay with varying sensing range
We then compared the throughput achieved by ETB-MAC with S-MAC and T-MAC protocols
under constant-bit-rate (CBR) traffic. In our model, 120 sensors are scattered over a
[100m×100m] area, the transmission range of each sensor is 60m.
When the number of contending CBR flow is lesser as plotted in Figure 6.4, ETB-MAC loses
throughput compared to contention based MAC protocols. This loss in throughput happens
because in this case, contention window in S-MAC and T-MAC is large enough to resolve the
collision between the CBR flows. When the number of CBR flows increases further (Figure 6.5),
ETB-MAC outperforms S-MAC and T-MAC in terms of raw throughput (packet delivery ratio is
higher).
32
Fig. 6.4 Packet delivery ratios
Fig. 6.5 Average delay vs. number of CBR sources
6.2 Comparison with Cluster-Based Protocols
We provide extensive simulation results and comparisons of the performance between our ETB-
MAC algorithm and two other popular „cluster‟ based routing algorithms like LEACH and
HEED algorithms in WSNs.
Simulations are done under various network environments with various factors such as node
number, transmission radius, BS location, network scale, traffic pattern as well as network
structure (flat and hierarchical). If our algorithm is implemented with LEACH, then the outcome
is ETB-MAC1 and in case if it is implemented with HEED, then the outcome is ETB-MAC2.
6.2.1 Energy consumption
In this section, our study is focused on the performance of average energy consumption under
different transmission radius R , different source to BS distance d , different node number N,
different BS location, different network scale as well as different traffic pattern.
We primarily study the influence of transmission radius R on energy consumption since different
routing algorithms will choose the next hop node based on their next hop selection criteria. The
simulation environment has 120 nodes randomly deployed in a 100×100m2 area with base station
BS placed in the middle of the area. In Figure 6.6 we can see that LEACH always consumes the
largest amount of energy since it utilizes the multi-path energy model with average long distance.
33
Fig. 6.6 Energy consumption with varying radius Fig. 6.7 Energy consumption with varying distance
In case of the HEED routing algorithm, the hop number is larger when R is small; this results in
more energy consumption. As R increases, it will prefer to choose the next hop with the distance
ri ≈ R to get close to the BS (HEED). It gets the best performance of energy consumption at R
=100. ETB-MAC2 algorithm consumes the least energy since it always tries to divide d into
several pieces with similar distances. Figure 6.7 shows the energy consumption under different
source to BS distance. When d > 120, LEACH consumes the largest energy since multi-path
model is used under which power attenuates in the fourth order of distance.
From the Figure 6.8 we conclude that the variation or fluctuation of the average energy
consumption becomes smaller as N increases. Our proposed ETB-MAC algorithm consumes less
energy as it always finds the suboptimal hop number and intermediate nodes as N increases.
Fig. 6.8 Energy with varying node numbers
Fig. 6.9 Total energy consumption vs. number of nodes
Then, we study the energy consumption under different network scale. Figure 6.9 shows a small
scale network where there are 100 nodes randomly deployed in 120×120m2 area with BS is
34
located at (50, 100) and we set R = 50, dc= 100 and Δ = 25. Our ETB-MAC2 has the best
performance. This is because the average source to the sink node is relatively small.
Figure 6.10 shows a similar case under large scale network where there are 200 nodes randomly
deployed in 200×200m2 area with BS at (150, 150). It is worth observing that again our ETB-
MAC2 performs better than other algorithms, the reason is that in ETB-MAC algorithm all the
nodes within the cluster are in sleeping mode, but one node (elected node) collects the
information from the surroundings and sends them to the sink node or base node.
In a very small scale network where there are 50 nodes randomly deployed in an area of
100×100m2 as shown in Figure 6.11, our ETB-MAC algorithm suffers slightly due to the
election algorithm.
Fig. 6.10 Total energy in larger network
Fig. 6.11 Total energy in smaller network
Fig. 6.12 Total energy with varying number of nodes
Fig. 6.13 Average network life time
35
As shown in Figure 6.12 the total energy consumed is increasing with increasing number of nodes. It
is suggested that all parameters including energy usage, battery level and distance should be
incorporated in the cost function. Our proposed ETB-MAC algorithm consumes less energy than
other algorithms.
6.2.2 Network lifetime
Our major study includes the average network lifetime under different network topologies, as shown
in Figure 6.13, there are 250 nodes randomly placed in an area of 200×200m2 with BS placed at (120,
120). The network lifetime usually decreases with R since more energy will be consumed on average.
HEED algorithm has a longer lifetime when R ≈ 110, because it tends to choose the next hop node
with distance when R = 150. When R ≤ 100, the lifetime of ETB-MAC is relatively shorter as shown
in Figure 6.14 because the sub-optimal hop number cannot be met and larger hop number is needed.
The network lifetime of different cluster head selection schemes is shown in Figure 6.15.
Thus to sum up the comparative studies reveal that our ETB-MAC2 has the longest lifetime while
LEACH algorithm has the worst average network lifetime.
Fig. 6.14 Network life time vs. transmission radius
Fig. 6.15 Network life time vs. number of nodes
In this chapter, we have compared the performance of our ETB-MAC with LEACH and HEED
routing algorithms in terms of energy consumption, hop number, network lifetime, successfully
delivered packets and delay time. We also studied the latency and throughput our algorithm with the
contention-based algorithms of S-MAC and T-MAC. From the extensive simulation results, we see
that our ETB-MAC has the best performance of energy consumption, packet delivery ratios, average
delay and network lifetime.
36
CHAPTER 7
CONCLUSION AND FUTURE WORK
In this proposal we have designed a new protocol ETB-MAC and compared against the existing S-
MAC, T-MAC, HEED & LEACH protocols. Depending on the traffic load, our protocol improves the
energy consumption significantly when compared to the contention-based protocols of S-MAC and T-
MAC. This is due to the fact that one node is elected as cluster head which consumes energy and most
of the other nodes within the cluster go to sleep. The simulation results show that ETB-MAC
consumed less energy than other protocols within an area of about 100 m2 even in high traffic. We
have also proposed new optimized cluster head selection method by reducing energy consumption of
overall networks and preserves network topology and connectivity.
The results of the simulation show that ETB-MAC outperforms the cluster-based protocols of HEED
& LEACH algorithms on network lifetime, data transmission capacity and energy efficiency with the
concern of position distributions. Therefore, our scheme can surely guarantee to prolong network
lifetime, reduce data transmission latency and improve the utilization of energy.
In our research we have analyzed contention-based protocols up to 100 nodes, and in the future,
simulations can be done with more number of nodes and also with other existing protocols with
different parameters.
37
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40
APPENDIX A
A1. Symbols Symbol Meaning
Es Sensing Energy
Et Transmitter Energy
Er Receiver Energy
Ec Computation Energy
Etot Total Energy
Eoa Overhearing Avoidance
Tsl Energy in Sleep Phase
Tdis Energy in Discover Phase
Eid Energy in Idle Listening
A2. Acronyms
Acronym Meaning
CRC Cyclic Redundancy Check
CSMA Carrier Sense Multiple Access
CSMA/CA Carrier Sense Multiple Access with Collision Avoidance
IEEE Institute of Electrical and Electronics Engineers
MAC Medium Access Control
QoS Quality of Service
RTS Request To Send
TMAC Time out MAC
TDMA Time Division Multiple Access
WSN Wireless Sensor Network
BS Base Station
CH Cluster Head
HEED Hybrid, Energy-Efficient, Distributed
LEACH Low Energy Adaptive Clustering Hierarchy
41
LIST OF PAPERS PUBLISHED BY THE CANDIDATE JOURNALS
1. P. Udayakumar, Ranjana Vyas, O.P. Vyas, the research paper titled “Energy Efficient Election
Protocol for Wireless Sensor Networks”, was published in the IEEE DigitalExplore, pp. 1028-
1033, ISBN 978-1-4673-4921-5, July 2013.
2. P. Udayakumar, Ranjana Vyas, O.P. Vyas, Research paper titled “TokenBusBased MAC Protocol for
Wireless Sensor Networks”, was published in the International Journal of Computer
Applications (IJCA), ISSN 0975-8887, vol. 43, No.10, April 2012.
3. P. Udayakumar, Ranjana Vyas, O.P. Vyas,Research paper titled “Analysing and Designing Energy
Efficiency in Wireless Sensor Networks”, was published in the International Journal of
Engineering Research & Technology(IJERT), ISSN: 2278-0181, vol. 1, issue 9, pp. 1-6, Nov
2012.
CONFERENCES
4. P. Udayakumar, Ranjana Vyas, O.P. Vyas, paper titled “Energy Efficient Election Protocol for
Wireless Sensor Networks”, presented in the IEEE International Conference ICCPCT-2013
Nurul Islam Center for Higher Education, KumaraCoil (TN) March 21-22, 2013, and the paper
was published in the conference proceedings.
5. P. Udayakumar, Ranjana Vyas, O.P. Vyas, “Thorough Analysis of Contention-Based MAC Protocols for
Energy Efficient Wireless Sensor Networks”, presented in the International Conference, CUTSE
University, Malaysia on 6-7 Nov 2012 and the paper was published in the conference proceedings,
pp. 218-221.
6. P. Udayakumar, Ranjana Vyas, O.P. Vyas, “Comparative Analysis of Contention-Based MAC
Protocols for Energy Efficient Wireless Sensor Networks”, presented in the International
Conference on Information and Communication Technology (ICT-2011) organized by IRNeT,
Chennai from 24-25 Dec 2011 and the paper was published in the conference proceedings,
pp. 130-133.
7. P. Udayakumar, Ranjana Vyas, “MAC protocols for Wireless Sensor Networks”, presented in the
National Conference on Innovative Trends in Management Science and Technology (ITMAST-2012)
organized by CCEM, Raipur on 8 Apr 2012 and the paper was published in the conference
proceedings, pp. 417-419.