Design Optimization of Energy and Delay Efficient Wireless Sensor Network with the help of Particle Swarm Optimization and Cluster based Least Spanning Tree Algorithm

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 6, Issue 6 (Jul. - Aug. 2013), PP 29-36
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Design Optimization of Energy and Delay Efficient Wireless
Sensor Network with the help of Particle Swarm Optimization
and Cluster based Least Spanning Tree Algorithm
Satyesh Sharan Singh1
, Mukesh Kumar2
, A.K. Jaiswal3
, Rohini Saxena4
, Priya5
1,5
PG Student ECE, SHIATS (Deemed-to-be-university), U.P., India
3
Professor & HOD, ECE, SHIATS (Deemed-to-be-university), U.P., India
2,4
Assistant professor, ECE, SHIATS (Deemed-to-be-university), U.P., India
Abstract: In WSN sensors are randomly deployed in the sensor field which brings the coverage problem and
limited energy resources. Hence energy and coverage problem are very scarce resources for such sensor
systems and has to be managed wisely in order to extend the life of the sensors and maximizing coverage for the
duration of a particular mission. In past a lot of cluster based algorithm and techniques were used. In this
paper we propose combination of PSO based algorithm and cluster based Least Spanning Tree algorithm,
which are very effective alone for WSN, and we also obtain life of sensor node and data transmission by LST
based PSO algorithm. These techniques effectively overcome the problems of low energy and coverage of
sensor range.
Keywords: Energy efficient clustering, Least Spanning Tree algorithm, PSO algorithm, Wireless Sensor
Networks.
I. Introduction
Advances in sensor technology, low-power electronics, and low-power radio frequency (RF) design
have enabled the development of small, relatively inexpensive and low-power sensors, called micro sensors [4]
which can be connected via a wireless network. These wireless micro sensor networks represent a new
paradigm for extracting data from the environment and enable the reliable monitoring of a variety of
environments for applications that include surveillance, machine failure diagnosis, and chemical/biological
detection. An important challenge in the design of these networks is that two key resources (communication
bandwidth and energy) are significantly more limited than in a tethered network environment. These constraints
require innovative design techniques to use the available bandwidth and energy efficiently. Energy usage is an
important issue in the design of WSNs which typically depends on portable energy sources like batteries for
power .WSNs is large scale networks of small embedded devices, each with sensing, computation and
communication capabilities. They have been widely discussed in recent years. Micro-Electro-Mechanical
System [MEMS][3] sensor technology has facilitated the development of smart sensors, these smart sensors
nodes are small devices with limited power, processing and computation resources. Smart sensors are power
constrained devices that have one or more sensors, memory unit, processor, power supply and actuator. In
WSNs, sensor nodes have constrained in term of processing power, communication bandwidth, and storage
space which required very efficient resource utilization. In WSNs the sensor nodes are often grouped into
individual disjoint sets called a cluster, clustering is used in WSNs, as it provides network scalability, resource
sharing and efficient use of constrained resources that gives network topology stability and energy saving
attributes. Clustering schemes offer reduced communication overheads, and efficient resource allocations thus
decreasing the overall energy consumption and reducing the interferences among sensor nodes. A large number
of clusters will congest the area with small size clusters and a very small number of clusters will exhaust the
cluster head with large amount of messages transmitted from cluster members.
II. Background
There are various algorithms which are used for the energy efficiency of wireless sensor network
which are improved time to time by different authors; some important and useful algorithms are shown in figure
1.1.

Optimization and Design of Energy and Delay Efficient Wireless Sensor Network with the help of
Figure.1.1 Different types of algorithm
III. The System Model
For making of an effective system we make a model which contains all prospects of the system and
also it executable. So the system model of an energy and delay efficient wireless sensor network is given
bellow:
A. Network Model
We adopt a wireless sensor architecture or model which is similar to use in the LEACH protocol with
the following properties:
 Each node performs sensing tasks periodically and always has tendency to send data to the base
station.
 A fixed base station can be located inside or outside the network sensor fields.
 All nodes are stationary and energy constrained.
 The nodes have power control capabilities to vary their transmitted power.
 All nodes are capable of operating in cluster head mode and sensing mode.
 Data fusion is used to reduce the total data message sent.
B. Radio Power Model and Characteristic Distance
For a simplified power model of radio communication, the energy consumed per second in
Transmission is[6]:
𝐸𝑡 = 𝑒𝑡 + 𝑒 𝑑 𝑟 𝑛
𝐵 … … … … … … … … … … . . … … . (1)
Where et is the energy/bit consumed by the transmitter electronics (including energy costs of imperfect
duty cycling due to finite startup time), and ed accounts for energy dissipated in the transmit op-amp (including
op-amp inefficiencies). Both et and ed are properties of the transceiver used by the nodes, r is the transmission
range used. The parameter n is the power index for the channel path loss of the antenna. This factor depends on
the RF environment and is generally between 2 and 4. B is the bit rate of the radio and is a fixed parameter in
our study.
On the receiving side, a fixed amount of power is required to capture the incoming radio signal[6]:
𝐸𝑟 = 𝑒𝑟 𝐵 … … … … … … … … … … … … … … … … … . . (2)
For currently available radio transceivers are et =50x10-9 J/bit, 𝑒𝑟 =50x10-9 J/bit, ed =100x10-12
J/bit/m2 (for n=2) and B=1Mbit/s. Since the path loss of radio transmission scales with distance in a greater-
than-linear fashion, the transmission energy can be reduced by dividing a long path into several shorter ones.
However, if the number of intermediate nodes is very large then the energy consumption per node is dominated
by the term et in equation (1) and the receiving energy consumption hence an optimum exists. Intermediate
nodes between a data source and destination can serve as relays that receive and rebroadcast data. Let us
consider multi-hop communication in a finite one dimensional network from the source to the base station
Clustering
Algorithm
Grid-schemes
PDCH
GROUP
PEGASIS
PSO based
PSO-C
PSO
CLUSTERIN
G
Least
Spanning
tree based
Miscellaneous
PEZCA
CACC
BARC
Housedroff
Clustering
Hierarchical
based
LEACH & Its
Descendant
EECS
HEED
EEUC

across a distance d using k hops. The source at x=d will generate traffic of A Erlang, so that each intermediate
node receives and transmits the same traffic, A. The routing nodes are assumed to be regularly spaced and to
consume no energy while idle. The power consumed by this communication is then simply the sum of the
transmit and receive energies multiplied by the effective bit rate, BA, and is given by
𝑃 = (𝑒𝑡 + 𝑒 𝑑 𝑟𝑖
𝑛
+ 𝑒𝑟)𝐵𝐴
𝑘
𝑖=1
… … … … … … … … … … (3)
𝑑 = 𝑟𝑖
𝑘
𝑖=1
… … … … … … … … … … … … … … … … … … . . (4)
In order to minimize P we note that it is strictly convex and use Jensen‟s inequality. Given d and k then
P is minimized when all the hop distances 𝑟𝑖 are made equal to d/k. The minimum energy consumption for a
given distance d has either no intervening hops or 𝑘 𝑜𝑝𝑡 equidistant hops where 𝑘 𝑜𝑝𝑡 is always of,
𝑘 𝑜𝑝𝑡 =
𝑑
𝑑 𝑐𝑕 𝑎𝑟
.............................................................(5)
The distance𝑑 𝑐𝑕𝑎𝑟 , called the characteristic distance, is independent of d and is given by,
𝑑 𝑐𝑕𝑎𝑟 =
𝑒 𝑡+𝑒 𝑟
𝑒 𝑑(𝑛−1)
𝑛
..........................................................(6)
The characteristic distance depends only on the energy consumption of the hardware and the path loss
coefficient (i.e. it is independent of the traffic); dchar alone determines the optimal number of hops. For typical
COTS (commercial, off-the-shelf)-based sensor nodes, dchar is about 35 meters. The introductions of relay nodes
are clearly a balancing act between reduced transmission energy and increased receive energy. Hops that are too
short lead to excessive receive energy. Hops that are too long lead to excessive path loss. In between these
extremes is an optimum transmission distance that is the characteristic distance.
In the above section I have introduced a simple energy model in which no energy was consumed while
the node was idle. This led to a characteristic distance that was independent of traffic. We now include the idle
state energy and show how the characteristic distance is modified. On one hand a short range is preferred for
energy efficient data transmission as a result of the nonlinear path loss ratio. On the other hand more redundant
nodes can be put into the sleep state to prolong the network lifetime if a long range is used in the topology
management of sensor networks. If the transmission route is divided into k grids and only one node wakes up in
each grid as relay node, as in the GAF protocol, the total energy consumption per second by k hops is[7]
𝑃 = 𝑘(𝑒𝑟 𝐵𝐴 + 𝑒𝑡 𝐵𝐴 + 𝑒 𝑑 2
𝑑
𝑘
𝑛
𝐵𝐴 + 𝑐𝑒𝑟 1 − 2𝐴 𝐵. … … . (7)
The last term 𝑐𝑒𝑟 1 − 2𝐴 B in the equation (7) represents the energy consumption when the radio
neither receives nor transmits, i.e. it is in the idle state. The energy consumption in the idle state is
approximately equal to that in the receiving state, so that the parameter c is close to 1. Note that we are
currently assuming that nodes in the sleep state consume no energy. Also, we assume that the routing node in
each grid can be located anywhere within that section and so the radio range is now twice the grid size. The
energy efficient optimum size of the virtual grid can now be derived from equation (1.7) and is given by
𝑅 𝑜𝑝𝑡 =
𝑟𝑜𝑝𝑡
2 =
𝑒𝑡 + 𝑒𝑟 𝐴 + 𝑐𝑒𝑟 1 − 2𝐴
2 𝑛 𝐴(𝑛 − 1)𝑒 𝑑
𝑛
… … … (8)
We know the energy consumed at the cluster head is much larger than that at individual sensing node.
The reason is as follows: (1) the cluster-head needs to relay all the traffic of the cluster; (2) for each data unit,
the cluster-head needs to transmit longer distance due to transmission between clusters, while the sensing nodes
just transmit data inside the cluster. In view of this, let Ep and Ec be, respectively, the current energy and
clustering energy (Ec is fixed), after a period of time, the ith
cluster-head has transmits information n1 times and
has receives information n2 times before T1(suppose the energy of the ith
cluster-head is not lower than the
threshold and the information unit is A Erlang ) .the remainder energy of the ith
cluster-head at T1 is then simply
as the equation (9).
𝐸𝑝 𝑖 = 𝐸𝑝 𝑖 − 𝐸𝑟 𝑖, 𝑘 − 𝐸𝑟 𝑖, 𝑙 − 𝐸𝑐 × 𝑛𝑖
𝑛2
𝑖=1
𝑛1
𝑘=1
. . (9)
Where i, j, k respectively denote cluster-head, ni is the clustering time in cluster i before T1.

IV. Methodology
A. Particle Swarm Optimization[PSO]
PSO is originally attributed to Kennedy, Eberhart and Shi[1] and was first intended for simulating
social behavior, as a stylized representation of the movement of organisms in a bird flock or fish school. The
algorithm was simplified and it was observed to be performing optimization. The book by Kennedy and
Eberhart describes many philosophical aspects of PSO and swarm intelligence. An extensive survey of PSO
applications is made by Poli. In computer science, particle swarm optimization (PSO)[6] is a computational
method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given
measure of quality. PSO optimizes a problem by having a population of candidate solutions, here
dubbed particles, and moving these particles around in the search-space according to simple mathematical
formulae over the particle's position and velocity. Each particle's movement is influenced by its local best
known position and is also guided toward the best known positions in the search-space, which are updated as
better positions are found by other particles. This is expected to move the swarm toward the best solutions.
The PSO [6] algorithm is an evolutionary computing technique, modeled after the social behaviour of a
flock of birds. In the context of PSO, a swarm refers to a number of potential solutions to the optimization
problem, where each potential solution is referred to as a particle. The aim of the PSO is to find the particle
position that results in the best evaluation of a given fitness function. In the initialization process of PSO, each
particle is given initial parameters randomly and is „flown‟ through the multi-dimensional search space. During
each generation, each particle uses the information about its previous best individual position and global best
position to maximize the probability of moving towards a better solution space that will result in a better fitness.
When a fitness better than the individual best fitness is found, it will be used to replace the individual best
fitness and update its candidate solution according to the following equations [1]:
𝑣𝑖𝑑 𝑡 = 𝑤 × 𝑣𝑖𝑑 𝑡 − 1 + 𝑐1 ∅1 𝑝𝑖𝑑 − 𝑥𝑖𝑑 𝑡 − 1 + 𝑐2 ∅2(𝑝 𝑔𝑑 − 𝑥 𝑔𝑑 𝑡 − 1 ) ………... (10)
Table 1 summarizes the variables in (1).
Table 1: List of variables used in PSO equations:-
v The particle velocity
x The particle position
t Time
C1,C2 Learning factor
Φ1,Φ2 Random numbers between 0 and 1
pid Particle‟s best position
pgd Global best position
w Inertia weight
B. Particle Swarm Optimization Algorithm for Cluster Setup
The operation of our protocol is based on a centralized control algorithm that is implemented at the
base station, which is a node with a large amount of energy supply. The proposed protocol operates in rounds,
where each round begins with a setup phase at which clusters are formed. This is followed by a steady state
phase in which we used a similar approach as in. At the starting of each setup phase, all nodes send information
about their current energy status and locations to the base station. Based on this information, the base station
computes the average energy level of all nodes. To ensure that only nodes with a sufficient energy are selected
as cluster heads, the nodes with an energy level above the average are eligible to be a cluster head candidate for
this round. Next, the base station runs the PSO algorithm to determine the best K cluster heads that can
minimize the cost function, as defined by:
cos 𝑡 = 𝛽 × 𝑓1 + (1 − 𝛽) × 𝑓2…………………... (11)
𝑓1 = 𝑚𝑎𝑥 𝑘=1,2,..,𝐾 𝑑∀𝑛 𝑖∈𝐶 𝑝,𝑘
(𝑛𝑖, 𝐶𝐻𝑝,𝑘 )/ 𝐶𝑝,𝑘 . ……………………..(12)

Figure 1.2 Flowchart of the PSO algorithm for cluster
setup
𝑓2 = 𝐸(𝑛𝑖)𝑁
𝑖=1 / (𝐶𝐻 𝑝,𝑘 )𝐾
𝑘=1 ………................ (13)
Where f1 is the maximum average Euclidean distance of nodes to their associated cluster heads and
Cp,k is the number of nodes that belong to cluster Ck of particle p. Function f2 is the ratio of total initial energy
of all nodes ni, i=1,2,…,N in the network with the total current energy of the cluster heads candidates in the
current round. The constant β is a user defined constant used to weigh the contribution of each of the sub-
objectives. The fitness function defined above has the objective of simultaneously minimizing the intra-cluster
distance between nodes and their cluster heads, as quantified by f1; and also of optimizing the energy efficiency
of the network as quantified by f2. According to the cost function defined above, a small value of f1and f2
suggests compact clusters with the optimum set of nodes that have sufficient energy to perform the cluster head
tasks. Fig. 1.2[1] shows the flowchart of PSO algorithm applied during the cluster setup phase. For a sensor
network with N nodes and K predetermined number of clusters, the network can be clustered as follows:
1. Initialize S particles to contain K randomly selected cluster heads among the eligible cluster head
candidates.
2. Evaluate the cost function of each particle:
i. For each node ni, i = 1,2,…,N
-Calculate distance d(ni,CHp,k) between node ni and all cluster heads CHp,k.
- Assign node ni to cluster head CHp,k where;
ii. Calculate the cost function using equations (2)to(3).
3. Find the personal and global best for each particle.
4. Update the particle‟s velocity and position using (1).
5. Limit the change in the particle‟s position value.
6. Map the new updated position with the closest (x,y) coordinates.
7. Repeat steps 2 to 6 until the maximum number of iterations is reached.
C. Least spanning tree[2]
The minimum spanning tree is a tree of a planar graph. Each edge is labelled with its weight, which
here is roughly proportional to its length. Given a connected, undirected graph, a spanning tree of that graph is a
sub graph that is a tree and connects all the vertices together. A single graph can have many different spanning

trees. We can also assign a weight to each edge, which is a number representing how unfavourable it is, and use
this to assign a weight to a spanning tree by computing the sum of the weights of the edges in that spanning
tree. A least spanning tree (LST) or minimum weight spanning tree is then a spanning tree with weight less than
or equal to the weight of every other spanning tree. More generally, any undirected graph (not necessarily
connected) has a minimum spanning forest, which is a union of minimum spanning trees for its connected
components. A spanning tree for that graph would be a subset of those paths that has no cycles but still connects
to every house. There might be several spanning trees possible. A minimum spanning tree would be one with
the lowest total cost
D. Constructing least spanning tree[2]
By Eq(9),we can compute the remainder energy, if Ep(i) is lower than Ev, then modify the information
table of the ith
cluster-head: set flag=0; broadcast information to its children, also inform the neighbour jth
cluster-head doesn‟t transmits information to it, and let ith
cluster-head i is in the sleeping state; else the ith
cluster-head may go along the next clustering or transmitting or receiving. According to Prim algorithm,
suppose undirected graph G (V, E, D), where V is the set of cluster-heads and the number is N, E is the set of
connections of cluster-head, and D is the distance of cluster-heads, the process of constructing least spanning
tree as illustrated below:
• Initialization: V1=Sink, E′=null, and V2=V-V1.
• Select a edge: which has minimum distance from Sink to one cluster-head (suppose is Vi), where Vi is directly
connected with Sink, then set, V1= {Sink, Vi}, E ′= {(Sink, Vi)}, V2=V2-V1.
• For each cluster-head Vk in V1 do :select a minimum distance d(k,j),which Vk ЄV1,Vj Є V2 and E′=(Vk, Vj) Є
E , but Vk is not Є E′, then V1=V1 Є Vj, E′={(Vk, Vj)} Є E′, V2=V2-Vj.
• If V2 is empty then end, else go to above.
The deployment of node by least spanning tree is shown in the figure 1.3(a & b).In this deployment we take one
cluster which is nearer to the sink. Now by MAC protocol we find out the weightege of different edge(path
distance). And make an incident matrix which shows the direction of data flow from node to node.
Figure 1.3(a) deployment of sensor node Figure 1.3 (b). Least spanning tree graph.
Now we apply least spanning tree algorithm and we get shortest path from each node to the node „A‟,
which is the nearest cluster head of the network from the sink. The least spanning tree graph is shown in the
figure 1.3 (b).

V. Results
The performance of the proposed protocol is evaluated using Matlab. We ran the simulations for 100
Figure1.4: Power consume by per node per hop over range parameter
nodes in a 100m x 100m network area with unequal initial energy of nodes to show the effect of the different
nodes‟ energy in the network. In figure 1.4 shows the calculated value of energy consumed by per node per hop
over the distance from the node. So we find out that with PSO clustering the power consumption is very less. In
figure 1.5 the lifetime of the node over high traffic is high in the PSO algorithm. in our proposed model it gives
approximately 4000 seconds per node over full traffic
Figure 1.5 Lifetime of node over traffic parameter
When we apply PSO algorithm for clustering and least spanning tree for data transmission then we get
many nodes live for long time when full traffic apply as shown in the figure 1.6.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 25 100 225 400 625 900 1225160020252500
Powerconsumebypernodeperhop(J)
ri(meter square)
Power Vs Range Parameter(ri)
NC
WC
WLST
WPSO
0
1000
2000
3000
4000
5000
6000
7000
8000
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
LifeTime(seconds)
Traffic Parameter α
Lifetime Vs Traffic Parameter
NC
WC
WLST
WPSO

Figure 1.6 Number of node over time
VI. Conclusion
In this paper we have presented energy and delay-aware cluster based protocol for wireless sensor networks
using particle swarm optimization (PSO) algorithm and least spanning tree. Results from the simulations and
calculation indicate that the proposed protocol using PSO and least spanning tree algorithm give a higher
network lifetime and delivers more data to the base station compared to other protocol. Furthermore, the
proposed protocol produces better clustering by evenly allocating the cluster heads and the shortest path to the
communication throughout the sensor network area. Our future work includes the implementation of secure data
transmission by distributed manner in ad- hock WSN network with the help of PSO. Comparison with other
evolutionary optimization algorithm, such as Genetic Algorithm will also be made.
References
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Spanning Tree Algorithm: A Survey International Journal Of Engineering Research And Applications (IJERA) ISSN: 2248-9622
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0
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0 50 100 150 200 250 300 350 400 450 500 550 600
NomberofnodeLive
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Node live over Time
NC
WC
WLST
WPSO

Design Optimization of Energy and Delay Efficient Wireless Sensor Network with the help of Particle Swarm Optimization and Cluster based Least Spanning Tree Algorithm

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Design Optimization of Energy and Delay Efficient Wireless Sensor Network with the help of Particle Swarm Optimization and Cluster based Least Spanning Tree Algorithm