Wireless sensor network consists of small nodes with sensing, computation and wireless communications capabilities [1],[2]. It has been applied in many applications area like military, environment, health, home and other commercial areas. In military area, Wireless sensor networks can be an integral part of military command, control, communications, computing, intelligence, surveillance, reconnaissance and targeting (C4ISRT) systems [1]. Radio Frequency Identification System (RFID) is one of the applications based on wireless sensor network [16].
Since the wireless sensor using in many area applications, the study of WSN is one of the most interesting research areas as wireless sensor network has profound effect on technological developments [3].
Wireless sensor network is different from others network like ad-hoc network and mobile network from a few perspectives:
Due to relatively large number of sensor nodes, it is not possible to build a global addressing because overhead of ID maintenance is high [2],[4].
In contrast to typical communication networks, most of the sensor networks applications require the sensed data transmit from multiple source to a particular base station. This does not prevent the data flow in another form [2],[4].
Sensor nodes are tightly constrained in terms of energy, processing, and storage capacities.
Position awareness of sensor nodes is important since data collection is normally based on the location which is not addressed by legacy protocols [2],[4].
In most application scenarios, nodes in WSNs are generally stationary after deployment except for maybe a few mobile nodes. Nodes in other traditional wireless networks are free to move, which results in unpredictable and frequent topological changes [2],[4].
Sensor networks are application-specific[2],[4].
Based on the differences discussed above, the traditional basic IP based routing protocols not suitable applied into the wireless sensor network[2],[4]. This has make the routing in the wireless sensor network become more challenging. Thus, many routing algorithm have been developed and applied in the wireless sensor network[3].
The existing routing algorithm basically can be categorized into two a few categories : Flat network routing, Hierarchical network routing, Location based routing, Negotiation based routing, Multipath-based routing, Query-based routing, QoS-based routing and Coherent-based routing [3],[4],[12].
Energy Efficient Clustering Hierarchy (EECH) is a routing algorithm that proposed on 2009 by Hu Yu, Li Wei and Kang Zhenhua. It is an algorithm that used to improve the performance of the Low-Energy Adaptive Clustering Hierarchy (LEACH). EECh is an algorithm which saving more energy, expanding the network scale and lengthening the lifetime over LEACH through the simulation using NS2 [29].
PROBLEM STATEMENT
The simulator used in EECH is NS2. This existing simulator is applied and large. Therefore, a simulator with general purpose will be developed and simulate the routing algorithm.
LEACH algorithm is the famous algorithm where most of the proposed algorithms are trying to improve the performance of LEACH.
LEACH is an algorithm with randomized, adaptive and self-configuring cluster information. It localized control for data transfers and low media access control. It also al algorithm with application specific data processing, such as data aggregation or compression. There are few limitations of LEACH. In this algorithm, the optimal number and distribution of cluster heads cannot be ensured. The cluster head is chosen randomly, nodes with less energy may be chosen (node will die faster)
In EECH, the dying speed is higher than LEACH. In proposed EECH, it takes energy efficient into account during the election of the cluster head. However, the node's location also an important issue during the cluster head election. In the future works, the node's location should be taken into the account during the cluster head election [29].
1.2 OBEJECTIVE
The objective of this project is to develop a simulator using general purpose algorithm to simulate the routing algorithm and enhanced the algorithm in order to get better performance.
2.0 LITERATURE REVIEW
LEACH
IACAR
(2009)
EECH(2009)
APTEEN
(2009)
RPCR(2009)
PEER(2008)
EECA(2009)
ILEACH
(2009)
pLEACH
(2009)
VR-LEACH
(2010)
REACH
(2010)
EADGP
(2010)
LEACH-B
(2010)
Improved-LEACH
(2010)
Multipath
Location Based Routing
QoS based
Routing
Hierarchical Network Routing
Flat Network Routing
Routing Protocol in Wireless Sensor Network
GEAR
(2008)
EERA
(2009)
DD(2000)
AMERR
(2004)
MERR
(2004)
LPGRA
(2010)
DMSTP
(2008)
PEGASIS
(2002)
Energy Aware DD
(2007)
IDD(2002)
The routing algorithm basically can be divided into a few groups and each group has different routing algorithms. A few categories are discussed as follows:
1. Flat network routing
In flat routing protocols, all nodes in the sensor network have equal roles in gathering information. All sensors perform identical roles or functionality and multi-hop routing is allowed.
i. Directed Diffusion
Directed Diffusion is one of the flat network routing protocols. The base station broadcasts the interest to the other sensors in Direct Diffusion. If there is any node having the request data, it will respond to the Base Station. It is very efficient to use data aggregation because the process has lots of redundant responses to queries [9],[14],[21],[22].
2. Hierarchical network routing
All nodes will have different roles in the network in hierarchical network routing protocols. They aim at clustering the nodes. A cluster head will be assigned. This cluster head can do dome aggregation and reduction of data in order to save energy. The nodes with higher energy level can be used to process and send information while low energy level nodes can be used to perform the sensing in the propinquity of the target [3],[4],[9],
i. Low-energy adaptive clustering hierarchy (LEACH) is one of the hierarchical routing. In order to maximize network lifetime, the idea is to select a sensor node randomly as a cluster head. There is high energy dissipation in transmission to the base station spread to many sensor nodes. The cluster head will compress the packet data after it receives the data packet and then transmit the aggregated packet to the base station [3],[4],[6],[9],[12],[15],[18].[19],[20].
ii. Power-Efficient Gathering in Sensor Information Systems (PEGASIS), a chain will be formed by sensors before sensing start. The starting point is the furthest sensor from the base station. Each sensor will find the nearest sensor which that sensor does not belongs to the chain, it will connects it to the chain. This process will be repeated until all the nodes connect to the chain. For each period, each sensor will receives a data packet from neighbor sensor of the chain and aggregates it with own data packet and transmit to the other neighbor sensor of chain until reaches the head sensor that transmits data packet to the base station [4], [9].
iii. Another protocols in Hierarchical network routing is PEER, which was proposed a few years ago. It is an improved algorithm from LEACH. In this algorithm, there is 5 phases. The first phase is the Pair joining phase where the nodes find a pair within its region. The second phase is the transmission phase where the low nodes and high nodes collects and send data. The third phase is about the individual nodes task. There will be a few individual nodes where it cannot find a pair in the pair joining phase. The fourth phase is the changing role of the pair where the low nodes and high nodes exchange the role. The fifth phase is the changing pairs if there a partner dead in a pair [17].
3. Location based routing
Location based routing also knows as directional, geometric based routing. This kind of routing protocol is based on two principals. First , every nodes knows its own and its network neighbor's positions. Second , the message source will know the position of the destination [3].
i. Geographical and Energy Aware Routing (GEAR)
In this protocols, information of event nodes are known. Every node will know its neighbors and also its location and energy information. A query command will be send to a node area near the sink. This node will then separate the command to the nodes within its area. The transmitting path will be used to send back the data. There will be two cost values used as routing cost, the actual and estimate cost. The estimate cost is the cost used for simple forwarding while the actual cost is tackle problem of holes. The next hop node with the larger value is selected [9],[12],[13].
4. QoS-based routing
The network has to balance between energy consumption and data quality. In particular, the network has to satisfy certain QoS metrics, for example, bandwidth, delay and energy.
i. Minimum Energy Relay Routing (MERR) and Adaptive MERR
These two protocols have efficiently utilized energy, less complex and have better scalability. They focused on optimal power consumption of routing in a linear wireless sensor network. AMERR has achieves optimal performance for practical deployment settings. AMERR chives optimal performance for practical deployment settings, assume the distance to the base station that allows AMERR to make better routing decision than MERR at the cost of a lower degree of locality.
MERR rapidly approaches optimal performance as sensors are more densely deployed and it only assumes that a sensor node is aware of the distances to its downstream neighbors [3].
3.0 METHODOLOGY
Before deciding the methodology used in this simulation, a detailed review of related work was done and the summary is as in the table below.
Routing protocols
Performance Technique
Control Parameters
Performance metrics
LEACH (2002)
[27]
Network Simulator
1. Number of nodes = 100
2. Base station location: x= 50, y = 175
3. Bandwidth = 1 MB/s
4. Message length = 500 bytes
5. Packet header length =25 bytes
1. Energy Dissipated
2. Optimum Number of cluster
PEER (2008)
[17]
Simulation
1. number of nodes
2. coverage area
3. length of packet
1 .power consumption
2. network lifetime
3. Average delay.
pLEACH (2009)
[19]
Simulation
1. number of nodes = 100
2. coverage area = 150m*150m
3. length of packet = 200 - 4000 bits
1. energy consumption
2. lifetime
APTEEN (2002)
[28]
NS 2
1. number of nodes = 100
2. Initial energy = 2J
3. Coverage area = 100 x 100 unit
1. Average energy Dissipated
2. Total number of nodes alive
3. Total number of data signals received at BS
4. Average Delay
IDD (2002)
[22]
Simulation in C++
1. Number of nodes
2. Initial energy
3. Coverage area
1. Energy Consumption and
2. Average Delay
Improved LEACH (2010)
[20]
Simulation in MATLAB
1. Energy in transmitting = 50nJ/bit
2. Simulation Area = 100m x 100m
3. Initial Energy per node = 0.5J
4. Data size = 4000 bytes
5. Number of nodes = 100
6. P = 0.05
7. Data fusion rate = 100 %
8. amp = 100pJ/bit.m-2
9. Base Station Location = (50,125)
1. Energy Dissipated
2. delay
3. network lifetime
Energy Aware DD (2007)
[21]
Simulation NS2
1. Number of nodes - 180
2. communication radius = 100m
3. Coverage area = 400m*400m
1. Average energy depletion per node.
2. Node death rate
3. Transmission delays
4. Network lifetime.
VR-LEACH (2010)
[23]
NS2
1. Sensing field (m2) =1000-1000 m
2. Number of sensor nodes =100
3. BS location =(50,175)
4. expected number of CH =5
5. Initial energy =2 J
6. Packet length (bytes) =4000
7. µ= 4.2ms
1. Network Lifetime
EECH (2009)
[29]
NS2
1. radio electronics energy dissipation = 50
2. transmit amplifier energy dissipation of Free-space model = 10pJ /bit /m2
3. transmit amplifier energy dissipation of Two-ray model = 0 .0013 pJ /bit /m4ï€
4. data aggregation energy dissipation = 5nJ/bit/signal
5. power dissipated in idle mode = 0W
6. power dissipated in sleep mode = 0W
7 number of clusters = 5
8. packet header size = 25Bytes
9. data packet size = 500Bytes
10. length of the simulation = 3600S
1. Network lifetime
LEACH-B (2010)
[25]
Simulation
1. Energy required in sending or receiving 1bit =50nJ/bit
2. The amount of data sent by nodes each time = 200bit.
3. The initial energy of every node: E = 0.5J
4. Compress ratio during data fusion: r = 0.7
5. Energy consumed in every bit data fusion =50pJ/bit
6. area: 100*100
7. The location of Sink: (5 , 5)
8. The percentage of cluster head: p = 0.05
9. The number nodes :n = 100
1. Energy Dissipated
2. System Lifetime
DMSTP (2008)
[9]
Simulation
1. Coverage area = 50m x 50m
2. initial energy = 0.01J
3. Base station location = (25,150)
4. packet size = 1000bits
1. Network lifetime
EERA (2009)
[26]
NS2
1. Number of nodes = 100
2. coverage area = 100 100 O
3. initial energy = 2J
1. Energy Dissipated
2. network lifetime
RPCR (2009)
[30]
OPNET
1. Number of nodes = 200,300
2. coverage area = 200m x 200 m
3. data packet size = 500 bytes
1. cluster distribution
2. energy dissipation
3. system lifetime
[31] (2009)
OMNeT++ with C++
1. Number of nodes = 200
2. Coverage area = 400m x 400m
3. transmission range of BS and Sensor nodes = 100m
4. Initial energy = 0.5 J
5. Data message length = 512 bytes
6. packet header = 25 bytes
1. System lifetime
2. Number of alive nodes
3. time of energy hole occurrence
REACH (2010)
[32]
GCC
1. Simulation area = 200 meters - 200 meters
2. Base station location = (100,100)m
3. Radio transmission range = 17 m
4. Number of nodes = 200
5. Initial energy = 3J
6. Eelec =50 nJ/bit
7. εfs =10 pJ/bit/m2
8. εmp = 0.0013 pJ/bit/m4
9. d0 = 87 m
10. EDA = 5 nJ/bit/signal
11. Data packet size = 8192 bits
12. Beacon message size = 40 bits
13. Percentage become a cluster-head =5%
1. node lifetime
2. residual energy
ILEACH (2009)
[33]
Simulation
1. Initial Energy = 0.5J
2. Base station coordinate = (25,25)
3. Number of nodes = 100,200,500
4. Communication channel bandwidth = 1Mbs
5. packet size = 2000bit
1. network lifetime
2. energy dissipated
IACAR (2009)
[34]
OMNeT++
1. Maximum communication distance = 150m
2. Single hop communication distance = 75m
3. Energy cost coefficient = 1-10-4 J/kb•m2
4. Pheromone initial value = 15
5. Pheromone constant = 3
6. Initial maximum energy = 100 J
7. Pheromone heuristic factor = 5
8. Guidance information heuristic factor = 1
9. Pheromone volatile coefficient = 0.2
10. Iteration number of algorithm = 5
1. average communication cost in cluster
2. residual energy
LPGCRA (2010)
[35]
MATLAB
1. grids = 9
2. Initial energy = 0.5 J
3. Packet length = 4000 bit
4. Control bag length = 100 bit
5. Eelec = 50Χ0.000000001 J
6. E amp = 0.0013Χ0.000000000001 J
1. number of live nodes
EADGP (2010)
[36]
Simulation
1. Number of nodes = 100
2. coverage area = 100m x 100m
3. BS coordinate = (50, 175)
4. Initial energy = 1J
5. data length = 4000 bits
6. broadcast message = 200 bits
1. network lifetime
2. Number of message received
EECA (2009)
[37]
NS-2.33
1. Number of nodes = 100
2. Coverage area = 1000m x 1000m
3. transmit range = 250m
4. packet size = 250m
5. initial energy = 30 J
1. network life
2. Average delay
From the analysis above, the performance of the routing algorithm will be done by using the developed simulator. The simulator will be developed using C#.
The control parameters used in the simulation
Number of nodes
Coverage area
Packet size
Initial energy
Base station location
The performance metrics used in evaluation :
The network lifetime
Energy consumption
3.1 SIMULATION PLATFORM
Programming Language : C#
C# is one of many .NET programming languages. It is object-oriented and allows you to build reusable components for a wide variety of application types. C# is an evolution of the C and C++ family of languages. However, it borrows features from other programming languages, such as Delphi and Java.
Visual C#.Net makes it easy to develop GUIs with powerful interfaces. On the other hand, Visual C#.Net is an object oriented language, and hence a simulation module can be implemented as classes from which many objects and functions can be derived.
4.0 GANTT CHART
Semester 1 2010/2011
Semester 2 2010/2011
WEEK
14
15
16
17
18
SEM
BREAK
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Identify Area of Research
Related work analysis
Identify Base work
Derive the problem statement
Prepare Proposal
Determine
1. performance analysis tools
2. determine the simulation techniques
3. design the model
4. define the events
5. develop the simulator
6. derive the control parameters
7. derive the performance metrics
Implement the program
Collect results
Analysis result
Thesis Writing
