enhancement of leach protocol using energy heterogeneity ...the cluster head selection mechanism in...

International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: [email protected], [email protected]

Volume 2, Issue 1, January – February 2013 ISSN 2278-6856

Volume 2, Issue 1 January - February 2013 Page 49

Abstract—Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions. In the research area of wireless sensor networks the routing protocols is a major issue. One of the important issues in wireless sensor network is limited battery power within sensor nodes. In addition to maximize the lifespan of sensor nodes, it is preferable to distribute the energy dissipated throughout the wireless sensor network. Hierarchical routing protocols are best known in regard to energy efficiency. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the fundamental protocol in this class. In this paper, we propose a heterogeneous-aware protocol to prolong the time interval before the death of the first node , which is important for many applications where the response from the sensor network must be reliable. This is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. The simulation results shows that Modified-LEACH yields longer stability region for higher values of extra energy brought by more powerful nodes .These results are further improved by applying genetic algorithm which is more effective in prolonging the network life time compared to LEACH. Keywords— wireless sensor networks, hierarchical routing protocol, routing protocol,Leach,genetic algorithm.

1. INTRODUCTION A Wireless Sensor Network has been recognized as one of the emerging technologies in the field of wireless communication.WSN is supposed to be made up of a large number of sensors and at least one base station. The sensors are autonomous small devices with several constraints like the battery power, computation capacity, communication range and memory. They also are supplied with transceivers to gather information from its environment and pass it on up to a certain base station, where the measured parameters can be stored and available for the end user. One of the major drawbacks with the operations of a WSN is lack of adequate energy in the battery powered nodes. Use of energy efficient routing algorithms can be useful to solve this problem. Hierarchical routing protocols are specifically designed to address the energy crisis in WSN. Heinzelman et al [1] proposed a hierarchical routing protocol, LEACH, to

improve on energy efficiency and reliable transmission on a WSN. The main concept was formation of clusters of nodes from which one node became cluster head that transmit data to the base station on behalf of others. LEACH reduces communication energy by as much as 8x compared with direct transmission and minimum transmission- energy routing [1]. The major problem with the LEACH protocol is that it requires the user to specify probability for use with the threshold function. Because the network performance is extremely sensitive to this probability, and it is very difficult to find an optimum setting from available knowledge .This project further attempt to improve performance of LEACH protocol to prolong network lifetime by extending stable region. Objective of this research work is to modify Leach protocol by considering energy level of sensor nodes in the cluster head selection mechanism in heterogeneous environment .Also finding optimal probability before the set-up phase of the first round for different locations of base station. We propose an improvement of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol to further improve its energy efficiency capabilities. The remaining part of this paper is organized as follows. We briefly review numerous existing and emerging technologies that are related to the work presented in this paper in section 2. Section 3 summarizes the suggested approach of solving the problem formulated. In section 4, we present the proposed algorithm and implementation of the solution. Our simulation result is presented in section 5. Finally, in section 6, we conclude the paper from the work described and discusses possibilities for future development.

2. RELATED WORKS This section analyses prior published work in WSN study domain. It commences with an overview of WSN technology, then an analysis of routing protocols with an in-depth coverage of hierarchical routing protocol. We begin our study with a review of basic terminology and protocols that are energy efficient as well as some proposed methods of improvement and performance. In general, routing in WSNs can be divided into flat-based routing, hierarchical-based routing, and location-based routing depending on the network structure [2].

Enhancement of LEACH Protocol Using Energy Heterogeneity Concept

Khushboo Pawar1, Vishal Pawar2, Tilotma Sharma3

1 B.E. M.tech(IT),MITS Ujjain, India

2B.E. M.E.(VLSI),SVITS Indore, India 3B.E. M.tech(IT),MITS Ujjain, India




The protocols which fall under these categories work with respect to the design constraints given for the network structure or area. Hierarchical routing is the procedure of arranging routers in a hierarchical manner [3]. The basic idea of hierarchical routing protocol is to organize sensor nodes into cluster based on the received signal strength and use local cluster-heads as routers to base station [1]. It performs local data fusion and aggregation at cluster-heads to further reduce energy consumption. Sensor nodes elect themselves to be local cluster heads with a certain probability. The non-cluster head node will join a cluster-head that requires minimum communication energy [4]. In the hierarchical routing protocol, we assume that the base station is fixed and located far away from the sensors. Also, we assume that every sensor can reach the base station directly so it will limit the applicability of the protocol. LEACH [1] is one of the first hierarchical routing approaches for sensors networks. The idea proposed in LEACH has been an inspiration for many hierarchical routing protocols [5,6,7,8,9,10,11,12]. We explore hierarchical routing protocols LEACH and IB-LEACH in this section.

2.1 Leach Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for sensor networks is proposed by W. R. Heinzelman et.al [1] which minimizes energy dissipation in sensor networks. It is very famous hierarchical routing algorithms for sensor networks which make clusters of the sensor nodes based on the received signal strength. The 5% of the total number of nodes becomes the cluster head which act as router to the sink. Energy consumption is less as transmission will only be done by cluster head. Design: LEACH organizes nodes into clusters with one node from each cluster serving as a cluster-head (CH) shown in figure 1. It randomly selects some predetermined number of nodes as cluster heads. Cluster heads then advertise themselves and other nodes join one of those cluster heads whose signal they found strongest (i.e. the CH which is nearest to them) [1].

Figure 1: cluster based mechanism of LEACH in WSN

Operation: LEACH operation is broken into rounds, with each round having a set-up phase and a steady state phase [1]. Set-up phase: During this phase, each node decides whether or not to become a cluster head (CH) for the current round. This decision is based on choosing a random number between 0 and 1, if number is less than a threshold T(s), the node become a cluster head for the current round. The threshold is defined as follows [1]:

T(s) =p/1-p(r mod (1/p)) if s €G (1)

Where p is the desired percentage of cluster heads (e.g. 0.05), r is = the current round, and G is the set of nodes that have not been cluster heads in the last 1/p rounds. The cluster head node sets up a TDMA schedule and transmits this schedule to all the nodes in its cluster, completing the setup phase which is then followed by a steady-state operation. Steady-state phase: each cluster-head waits to receive data from all nodes in its cluster and then sends the aggregated or compressed result back to a BS.

Figure 2: Time Line operation of LEACH [1] Weaknesses: Clustering is a good approach which, if implemented properly, can lead to energy efficient networking in WSNs. Despite the significant overall energy savings, however, the assumptions made by the protocol raise a number of issues as explained in [13]: LEACH assumes that all nodes can communicate with each other and are able to reach the sink (therefore, it is only suitable for small size networks), LEACH assumes that all nodes have data to send and so assign a time slot for a node even though some nodes might not have data to transmit, LEACH assumes that all nearby nodes have correlated data which is not always true, LEACH requires that all nodes are continuously listening ( this is not realistic in a random distribution of the sensor nodes, for example, where cluster-heads would be located at the edge of the network), there is no mechanism to ensure that the elected cluster-heads will be uniformly distributed over the network ( hence, there is the possibility that all cluster heads will be concentrated in one part of the network), periodic dynamic clustering carries significant overhead which may off-set energy gains derived by the clustering option.

2.2 IB-Leach The Improved And Balanced Leach Protocol (IB-LEACH) [14], is an extension of the LEACH, which improves the stable region of the clustering hierarchy and decrease probability of failure nodes using the characteristic parameters of heterogeneity.




Design: In this protocol a percentage of sensor nodes are equipped with more energy resources than the rest of the nodes. Let m be the fraction of the total number of nodes N which are equipped with a times more energy than the others and b is the fraction of the total number of nodes N which are elected Gateways. We refer to these powerful nodes as NCG nodes (node selected as normal node or cluster head or gateway), and the rest (1-m) * N as normal nodes. We assume that all nodes are distributed uniformly over the sensor field.

Figure 3 The IB-LEACH Network model [14]

Operation: Routing in IB-LEACH works in rounds and each round is divided into two phases, the Setup phase and the Steady State; each sensor knows when each round starts using a synchronized clock. Setup phase: During the set-up phase the gateways are elected and the clusters are organized. It is constituted by gateway selection algorithm and cluster selection algorithm and cluster formation algorithm. Steady State: After the set-up phase is the steady-state phase when data are transmitted from the nodes to the cluster head and on to the gateway that requires the minimum communication energy and transmit it to the BS. The duration of the steady phase is longer than the duration of the setup phase in order to minimize overhead. IB-LEACH improves the stable region of the clustering hierarchy and decrease probability of failure nodes using the characteristic parameters of heterogeneity in networks. It achieves better performance in this respect, compared LEACH in both heterogeneous and homogenous environments.

3. THE PROPOSED SCHEME In this section we introduce the proposed protocol. Proposed work introduces modified Leach to find out which sensor nodes are able to become cluster heads and how long they retains as cluster head on the basis of their energy level to prolong the network lifetime. The GA-based optimization procedure is performed to improve the results coming from modified Leach protocol for various base station placements.

3.1 Modified Leach protocol

The original version of LEACH does not take into consideration the heterogeneity of nodes in terms of their initial energy, and as a result the consumption of energy resources of the sensor network is not optimized in the presence of such heterogeneity.

In this Model, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable.

Our modified protocol improves the stable region of the clustering hierarchy process using the characteristic parameters of heterogeneity, namely the additional energy factor between advanced and normal nodes (α).

Cluster head selection is randomly done, that take into account the additional energy factor between advanced and normal nodes (α) of sensor node.

Our protocol successfully extends the stable region by being aware of heterogeneity through assigning probabilities of cluster-head election weighted by the relative initial energy of nodes.

This yields a longer stability region for higher values of extra energy.

3.2 Methodology The impacts of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered are analyzed. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources. This is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the




same amount of energy and as a result, they cannot take full advantage of the presence of node heterogeneity.

Method is applied in a sensor field of area 100×100 m.

Number of nodes in the field is 100. Initial energy of a node is 0.5 joule. Advanced nodes have α time more energy than a

normal node. Hence energy of advanced node becomes = initial

Energy× (α). Initially the dissipated energy is zero & residual

energy is the amount of initial energy in a node, hence total energy Et is the amount of residual energy.

Average distance between the cluster-head and the base station is calculated by [15].

dbs= 0.765 × (one dimension of field/2) Optimum number of clusters are calculated by[15]

Kopt =

Where, n is the number of nodes, εfs, εamp represent amplifier energy consumptions for a short distance and long distance transmission, M is one dimension of field.

The average distance between the cluster members and the cluster-head is calculated by[15]

The total energy dissipated in the network

during a round is calculated by[15] Et=

Where, Eda is the data aggregation energy. Also we calculated the average energy Ea of a

node after the particular round with the knowledge of total energy and a particular number of round numbers.[15]

Ea= 1/n(Et × (1 - r/Rmax)) Here, r is the current round and Rmax is the maximum number of rounds.

We calculated the dead statistics before assigning a cluster head, and its value renewed every new round.

The new expression for optimum probability can be calculated from different energy levels and optimum probability defined earlier, for the cluster head selection in nodes which have higher energy.

p(i)=

Here, an Advanced will becomes Cluster Head, if a Temporary number assigned to it is Less than the Probability Structure Below,

T(si)=pi/1-pi(r mod(1/pi)) if s€G eq.(1) Here, Pi is come out from new expression for optimum probability p(i)

After an advanced becomes cluster head, energy models are applied to calculate the amount of energy spent by it on that particular round and complete the round of steady state phase.

If a node will not an advanced node and discarded from the criteria above, than it goes to a set of normal node, and follow the behaviour of normal node and complete the round of steady state phase.

Each node is elected cluster-head once every 1/P rounds (election length).

On average, n x P nodes elected per round.

3.3 Genetic Algorithm based optimization A Genetic Algorithm performs fitness tests on new structures to select the best population. Fitness determines the quality of the individual on the basis of the defined criteriaIn a genetic algorithm, fitness is evaluated by the function defining the problem. The fate of an individual chromosome depends on the fitness value; the better the fitness value, the better the chance of survival. Genetic Algorithms solve design problems similar to that of natural solutions for biological design problems [16]. 1) Population: A population consists of a group of

individuals called chromosomes that represent a complete solution to a defined problem. Each chromosome is a sequence of 0s or 1s. The initial set of the population is a randomly generated set of individuals.

2) Fitness: In nature, an individual’s fitness is its ability to pass on its genetic material. This ability includes traits that enable it to survive and further reproduce [2]. In a genetic algorithm, fitness is evaluated by the function defining the problem. The fate of an individual chromosome depends on the fitness value. The chances of survival are higher for better fitness values.

3) Selection: The selection process determines which of the chromosomes from the current population will mate (crossover) to create new chromosomes. These new chromosomes join the existing population. This combined population will be the basis for the next selection. The individuals (chromosomes) with better fitness values have better chances of selection.

4) Crossover: Crossover is also known as recombination of component materials due to mating. It is a simulation of the sexual reproductive process which is responsible for the transfer of genetic inheritance.



Volume 2, Issue 1 January - February 2013 3

The outcome of crossover heavily depends on the selection of chromosomes made from the population.

5) Mutation: As a result of crossover, the new generation introduced will only have the traits of the parents. This can sometimes lead to a problem where no new genetic material is introduced in the offspring. Mutation allows new genetic patterns to be introduced in the new chromosomes. Mutation introduces a new sequence of genes into a chromosome but there is no guarantee that mutation will produce desirable features in the new chromosome.

Genetic Toolbox in MATLAB7.7 The Genetic Algorithm and Direct Search Toolbox is a collection of functions that extend the capabilities of the Optimization Toolbox and the MATLAB® numeric computing environment. The Genetic Algorithm and Direct Search Toolbox includes routines for solving optimization problems using

Genetic algorithm Direct search

These algorithms enable you to solve a variety of optimization problems that lie outside the scope of the standard Optimization Toolbox. All the toolbox functions are MATLAB M-files, made up of MATLAB statements that implement specialized optimization algorithms. Calling the Function ga at the Command Line To use the genetic algorithm at the command line, call the genetic algorithm function ga with the syntax [x fval] = ga(@fitnessfun, nvars, options) where

@fitnessfun is a handle to the fitness function. nvars is the number of independent variables for

the fitness function. options is a structure containing options for the

genetic algorithm. If you do not pass in this argument, ga uses its default options.

4. IMPLEMENTATION The implementation procedure is described. We then attempt to analyze and interpret the data gathered during implementation and present the results. 4.1 Energy Dissipation radio model Depending on the distance between the transmitter and receiver, both the free space (d2 power loss) and the multi path fading (d4 power loss) channel models are used [15, 17].

Figure 4 Radio energy dissipation model [17]

Thus, to transmit a k-bit message a distance d, the radio expends: [17]

where

In the Expression, Eelec =is the circuit energy cost for transmitting or receiving one bit data. εfs = represent amplifier energy consumptions for a short distance transmission. εamp=represent amplifier energy consumptions for a long distance transmission. d0 = threshold distance. And to receive this k-bit message, the radio expends:

4.2 Simulation Parameters The table below indicates the other parameters as set during simulation.

Table 1: Simulation Parameters Sr.No.

Parameter Value

1 Simulation Area 100m x 100m

2 Channel type Channel/wireless channel

3 Radio propagation model

Two ray ground

4 Number of nodes 100 5 CH probability 0.05

6 Energy model Battery

7 Transmitter amplifier Energy dissipation (a) Efs amp

10 pJ/bit/m2

8 Initial node power 0.5 Joule

9 Nodes distribution Nodes are randomly distributed

10 Base Station position Located at 50 x 175 The resultant network after simulation setup looked as shown in figure 5. The nodes are randomly distributed across the sensor field. Field topography show the initial field distribution of the network, where LEACH protocol is implemented. A 100m*100m field is taken and nodes are randomly placed in it. Here, the nodes are shown by a star symbol (*).




Figure 5 Field topography

4.3 Implementing LEACH in MATLAB

In this work, the following assumptions were made. Nodes are homogenous. The nodes have initial uniform energy, of 0.5

joules. Each sensor node initially has the ability to

transmit data to any other sensor node or directly to BS.

The sensor nodes are stationary. Packet size is the same for all nodes, with a

minimum packet size of 2000kbit. Nodes have unique IDs. A node belongs to only one cluster, but may

change its cluster affiliation during each round. It was assumed that the sensor node are scattered

all over the field.

The graph in figure 6 illustrates the number of rounds for which a node is alive at given time intervals for LEACH protocol.

Figure 6 Comparison of the number of alive nodes as the round proceeds

4.4 Implementing Modified LEACH in MATLAB

Steps for implementation are as following: Initializing the random sensor network and

declaring the parameters used in the field. Declaring the initial energy level of nodes and

advanced nodes have higher energy than that of normal node.

After starting the round, we declare the election probability for advanced nodes on which selection criteria of cluster head depends.

Firstly we check if there is a dead node in the sensor field, and checking this criteria after every round.

Election of cluster heads for normal nodes and advanced node are done in different loops which depends on the election probability used.

After a cluster head sent its data to sink, calculation of energy dissipated is done, through energy models considered in the project, in order to calculate how much energy dissipated after a steady state and whether a cluster head is eligible to transmits data in the next round too. This energy thoroughly depends upon the distance between base station and cluster head.

The graph in figure 7 illustrates the number of rounds for which a node is alive at given time intervals for Modified LEACH protocol.

Figure 7 Comparison of the number of alive nodes as the round proceeds

5. COMPARISON BASED ON NETWORK LIFETIME Network lifetime is defined to be the overall period of time from the beginning of the sensor network to the instance the first node runs out of its energy.




Figure 8 Comparison of the number of alive nodes as the

round proceeds

Fig 8 shows how many nodes are still alive with transmission rounds, compared with LEACH after 1200 rounds. The result shows that the network lifetime of our proposed protocol is prolonged compared to that of LEACH. The modified leach protocol performs better than LEACH, but we can see that the sensor nodes of LEACH die quicker than our proposed protocol. It is because the LEACH also ignore the real and the expectance number of cluster heads maybe different, and it decreases the efficiency of LEACH.

Table 2 comparison of network lifetimes (number of rounds) between LEACH and proposed protocols

Base

Station

(x , y)

Protocol

used

Nodes dead

1%

20%

50%

100%

(100,175)

Leach 100 150 160 180

Modified

Leach

420 460 490 700

GA-

MLeach

450 490 550 820

(150,175)

Leach 100 150 160 180

Modified

Leach

390 420 460 680

GA-

MLeach

440 460 530 800

Table 2 shows the simulation results obtained using LEACH and presented modified LEACH protocols for BS located at different positions. The initial energy for all nodes was 0.5(J) and the probability p used in LEACH is 5%, same as the settings in [1,10]. The number of rounds required when the number of dead nodes is 1%, 20%, 50%, and 100% are recorded during simulations. From our results, the proposed protocol outperforms LEACH in terms of lifetime of network.

6. CONCLUSION The core operation of a WSN is to gather and convey the collected data to a distant BS for further processing and analysis. Gathering information from a WSN in an energy effective manner is of paramount importance in order to prolong its life span. This calls for use of an appropriate routing protocol to ensure efficient data transmission through the network. In this research project, we have proposed an Amend implementation of LEACH protocol based on energy heterogeneity and optimize it through genetic algorithm. The result of simulations conducted indicates that the proposed approach is more energy efficient and hence effective in prolonging the network life time compared to LEACH.

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enhancement of leach protocol using energy heterogeneity ...the cluster head selection mechanism in...

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