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\title{Enhancing Network Lifetime in Wireless Sensor Networks Adopting Elephant Swarm Optimization}
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             \author[1]{Chandramouli  H}

             \author[2]{Dr. Somashekhar C  Desai}

             \affil[1]{  Shri Jagadishprasad Jhabarmal Tibrewala University (SJJTU), Rajasthan}

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\date{\small \em Received: 13 December 2012 Accepted: 31 December 2012 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


Enhancing the lifetime of wireless sensor networks had baffled researchers for quite some time now. The authors of this research manuscript draw inspiration from the behavior of large elephant swarms and incorporate their behavior into wireless sensor networks. The complex elephant swarm behavior is incorporated using a cross layer approach. The elephant optimization discussed in this paper enables optimized routing techniques, adaptive radio link optimization and balanced scheduling to achieve a cumulative enhanced network performance. The proposed elephant swarm optimization is compared with the popular protocol. The experimental study presented proves that the Elephant Swarm Optimization technique enhances the network life time by about 73%.

\end{abstract}


\keywords{elephant swarm, optimization, network lifetime, cross-layer design, energy efficiency, resource allocation, sensor networks.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
et us consider a topology of wireless sensor networks deployed over a specified geographical region. The sensor nodes are assumed to have homogenous energy properties and are battery operated which is the case most often than not. The sensor distribution over the geographical region is considered to be dense to achieve higher transmission data rates. Owing to dense deployments numerous links are established induce interference amongst the sensor nodes which needs to be minimized to achieve better network performance in terms of throughput. This paper introduces an Elephant Based Swarm Optimization model to enhance network life time. A cross layer approach is adopted to incorporate the elephant swarm optimization features.\par
Elephants are social animals \hyperref[b25]{[1]} and exhibit advanced intelligence \hyperref[b26]{[2]}. Elephants are often found to exist in a "fluid fission-fusion" social environment \hyperref[b27]{[3]}. Elephants characterized by their good memory, their nature to coexist and survive within a "clan" \hyperref[b28]{[4]} (a large swarm of more than 1000 elephants) socially formulated during testing times like migration and when the resources are scare. Elephants exhibit an unselfish behavior which enable them to grow and is the secret of their longevity. Keeping progress and survivability in mind the older elephants disassociate from the "clan". Elephants by nature are protective of their younger generation. Elephants communicate using varied advanced techniques which include acoustic communication, chemical communication, visual communication and tactile communication \hyperref[b29]{[5]}  \hyperref[b14]{[6]}. Their memory empowers them with recognition, identification and problem solving scenarios \hyperref[b28]{[4]}. All these features exhibited have influenced the authors to incorporate such behavior in wireless sensor networks to improve network performance.\par
The elephant swarm model is complex and to realize such behaviors in wireless sensor networks the authors have proposed to adopt a cross layer approach to incorporate the elephant swarm model. Optimizations need to be adopted at the Routing Layer, Layer and the Radio Layer of the wireless sensor node. This paper introduces a cross layer approach to incorporate the elephant swarm optimization technique which is compared with the popular protocol and its efficiency is proved in the latter section of this paper.\par
The remaining manuscript is organized as follows. Section two discusses a brief literature study conducted during the course of the research work presented here. The system modelling and the elephant swarm optimization technique using a cross layer approach is discussed in the third section of this paper. The experimental study conducted is described in the penultimate section of this paper. The conclusions drawn and the future work is presented in the last section of this paper. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
The literature review discussed in this paper emphasizes the cross layer architectures proposed by researchers to overcome the drawbacks that exist in wireless sensor networks.\par
A research work \hyperref[b15]{[7]} describes the fundamental concept of sensor networks which has been made viable by the convergence of micro electro-mechanical( D D D D D D D D )\par
systems technology, wireless communications and digital electronics. In their work initially the potential sensor networks are explored and then the dominant factors influencing the system architecture of network is obtained and in the later stage the communication architecture was outlined and the algorithms were developed for different layers of the network for system optimization. As this proposal brought certain positive results but was lacking the optimized output and having a lot of vacuum for further development.\par
The researcher in \hyperref[b18]{[8]} developed a recommender system, employing a particle swarm optimization (\par
) algorithm for learning the personal preferences of users and facilitates the tailored solutions. The system being used in this research was based on collaborative filtering approach, building up profiles of users and then using an algorithm to find profiles similar to the current user. To overcome the problem of sparse or implicated data they utilized stochastic and heuristic-based based models to speed up and improve the quality of profile matching and finally the PSO was used to optimize the results. That system was found to be outperforming Genetic Algorithm concept but the system could not play a vital role in higher data rate with cross layer architecture and especially for heterogeneous type of network.\par
In literature \hyperref[b20]{[9]} a number of fundamental cross layered resource allocation techniques at layer were considered for fading channel. This research work emphasizes on characterization of fundamental performance limits while considering the network layer, layer quality and physical layer as performance.\par
Considering the dominant network parameters like deploy, energy consumption, expansibility, flexibility and error tolerance Jin, Lizhong et al \hyperref[b21]{[10]} presented a research work that employs a cross layer protocol for wireless network. This work employs the splitting of layer and of course it performed well, but considering the higher data rate transmission this system was found to be ineffective even having more error prone.\par
In \hyperref[b23]{[11]} investigated the cross layer survivable link mapping when the traffic layers are unambiguously desired and survivability is must. In this work a forbidden link matrix is identified the masking region of the network for implementing in such conditions where some physical links are reserved exclusively for a designated service, mainly for the context of providing multiple levels of differentiation on the network use. The masking upshot is then estimated on two metrics using two sensible approaches in a real-world network, depicting that both effectiveness and expediency can be obtained.\par
The literature \hyperref[b24]{[12]} the researcher proposed a route discovery and congestion handling mechanism that employs a cross layer model including a potential role in congestion detection and its regularization. The limitation of the proposed technique was its confined data rate.\par
Hang Su \hyperref[b2]{[13]} proposes the cross layer architecture based an opportunistic MAC protocol that integrates the spectrum sensing at layer and packet scheduling at the layer. In their proposal the secondary user is equipped with two transceivers where one is tuned for dedicated control channel while another one is designed particularly for cognitive radio that can effectively use the idle radio. They propose two shared channel spectrum-sensing approach, named as the random sensing policy and the negotiation-based sensing policy so as to assist the protocols detect the availability of leftover channels. This technique has a great potential but the emphasis has been made on the efficient use of leftover frequency and thus the other parameters are not being considered.\par
In literature \hyperref[b3]{[14]} proposed a new cross layerbased protocol stated as . In this proposed cross layered technique, the communications among , Routing and Physical layers are fully exploited so as to minimize the energy consumption and multi-hop delay of the data delivery for wireless sensor networks. In precise, in that approach the carrier-sensing technology is applied at the layer so as to sense the traffic load and necessarily initiates the neighbor nodes in multi hops so that the data transmission can be realized over multi hop. Similarly, by implementing the routing layer information, the developed cross layered facilitates the receiver of the ascending hop on the path of routing that has to be effectively waken up and ultimately it results into the potential reduction in energy consumption.\par
In reference \hyperref[b4]{[15]} the (Low Energy Adaptive Clustering Hierarchy) routing protocol which is a conventional clustering communication protocol has been implemented. The proposed protocol is dominantly used in WSN. Then while there are certain limitations in LEACH, in as the nodes consume a lot of energy and the efficiency of nodes ultimately decreases because the nodes which are having low energy and are at remote from Base Station become the cluster head nodes. This work analyzes the energy model and considers three important factors: The energy for individual nodes, the number of times that the node is chosen as cluster heads and the distances between nodes and BS. In this research work the author changes the threshold function of the node so as to extend the lifetime of the network and to achieve the goal of balancing the energy of the network. This work has indicated that the implemented system can of course prolong the life span of the network, but the drawback of this work is that it does not consider the other network optimization problems like, throughput, delay, overheads etc. The techniques incorporated by researchers to enhance the network lifetime of wireless sensor networks is also considered during the course of the research presented here \hyperref[b7]{[16]} [17] \hyperref[b13]{[22]}. 
\section[{Global Journal of Computer}]{Global Journal of Computer} 
\section[{III. Elephant Swarm Optimization in}]{III. Elephant Swarm Optimization in}\par
Wireless Sensor Networks a) System Modeling of Sensor Networks\par
In this section of the paper the system modelling adopted to realize the elephant swarm optimization for wireless sensor networks is discussed.\par
Let us consider a wireless sensor nodes represented by a set which constitute a static network defined as\par
In the considered network , the wireless communication links that exist between two nodes and , a relatively high transmission power allocation scheme is considered . The high power allocation scheme causes the higher power consumption that ultimately results into numerous interferences situation between other nodes as well as degraded network life time and hence poor efficiency. The communication channel being considered over the links is nothing but Additive White Gaussian Noise ( ) channel having confined noise power level. Here, one more factor called deterministic path loss model has been assumed. If the signal to noise ratio of a communication link is represented by then the maximum data rate supported per unit bandwidth is defined as Where This considered model can be realized using modulation schemes like . The constellation size for the is and varies with time over a considered link  {\ref [23]}. The model assumes a scheduling system of communication between the nodes. The model considered assumes that there exists time slots for the medium access control layer ( ) and a unique transmission mode is applicable per slot.\par
Let us consider that a particular node transmits at a power level then the power consumption of the amplifier is defined as Where is the efficiency of power amplifier and to achieve the desired signal amplification. 
\section[{A homogenous sensor network model is considered}]{A homogenous sensor network model is considered}\par
Where indicates set of directed links. 
\section[{Let indicates the incidence matrix of the graph then we can state that}]{Let indicates the incidence matrix of the graph then we can state that}\par
We present an expression Such that and have the entries of 0 and 1.\par
As discussed is the number of time slots in individual frame of the periodic schedule. represents the set of link scheduled. These are allowed to transmit during time slot defined as and represents the power of transmission and per unit bandwidth rate respectively over link and slot . The vectors of the time slot are and .\par
is the maximum limit of allowable transmission power for the node which belongs to link. The analogous vector is . The vectors id defined as Where is the row of the matrices . Also\par
The vector is defined as where is the row of the matrices . Also\par
The initial homogenous energy of all the nodes defined as and the energy . 
\section[{Let represents power consumption of transmitter and}]{Let represents power consumption of transmitter and}\par
represents the power consumption of the receiver and is assumed to be homogenous for all the nodes. The consumed by each node is .\par
Let the sensing events that are induced in the network induce an information generation rate represented as . It can be stated that represents a vector which constitute of . The data aggregated at the sink is defined as , â??" , , â??" 0,? 1, ? ? ? , ? | | â??" ? | | 1 1 1 e 0 0 e ? 1 ? | | 1 1 1 e 0 0 e ? 1 ? | | ? ? | | ? ? | | ? , ,\par
The link gain matrix of the wireless sensor network considered is defined as\par
The power from the transmitter of the link to the receiving node on link is represented as and represents the total noise power over the operational bandwidth.\par
The represent the network lifetime when a percentage of nodes runs out of energy. This is a common criterion considered by researchers to evaluate their proposed algorithms \hyperref[b7]{[16]}  \hyperref[b8]{[17]}.\par
The maximum data rate supported for transmission over a particular Link is defined as b) Problem Formulation A cross layer approach is adopted to enhance the network lifetime of the wireless sensor network. Elephants are social animals and are said to possess strong memory of the events that occur.\par
The problem of optimizing or maximizing the life span of the network can be presented as a function defined as follows\par
The maximization function can be defined as For all time slots and , the constituting variables are , for a set 
\section[{Let us define a variable such that}]{Let us define a variable such that}\par
The elephant swarm optimization is applied to attain minimized function defined as\par
The minimization function or the elephant swarm optimization objective can be defined as\par
The model presented here considers based systems the minimization function can be defined as . ? ? ? 1 â??" ? â??" 1 1 1 ? ? 0,0 ? ? 1, ? ? , ? , , â??" ? 1, ? ? , , ? . . . . ? ? ? 1 â??" ? â??" 1 1 1 ? . . . ? ? 1 0 ? â??" 1 ? , , 0, ? 0, ? ? , ? .\par
. 
\section[{c) Cross Layer Optimization to Realize Elephant Swarm Behavior}]{c) Cross Layer Optimization to Realize Elephant Swarm Behavior}\par
The presented section of this paper elaborates the elephant swarm optimization algorithm for routing, scheduling and advanced radio layer control techniques. The elephant swam optimization is applied taking into account unconstrained scheduling on the network links. The elephant swarm optimization enables simultaneous scheduling of the sensing data on the interfering wireless communication links in the current considered scheduling time slot. The elephant swarm optimization iterates to obtain an optimal routing, power consumption and schedule to enhance the considered network lifetime. The elephant model is adopted to solve optimization objective defined in the former section of this paper.\par
Let us consider a link schedule of data defined as where . The rate of transmission that can be supported over a link can be expresses as based on approximations is defined as If the of a link is then the minimum transmission rate is defined as follows The elephant swarm optimization results arising based on the above approximations for are said to be a part of the function optimization set. Let us define a variable Then the above equation for the maximum transmission rate optimization can be defined as can be Based on the above arguments the elephant swarm optimization objective can be expressed as\par
The above defined elephant swarm optimization is applicable provided computational complexity of optimization under such circumstances can be defined as Where represents the link, the number of slots assigned on is . is the set of transmitting links and is the receiving links of the sensor node .The variable is defined as follows ? 1 
\section[{And is defined as}]{And is defined as}\par
The transmission power the link represented as is defined as It must be noticed that the power of transmission over a network link is presented asâ??" 1 . 1 . ? 1, ? ? ? , ? log ? â??", ? ? . log ? , â??" â??" 0 . ? ? ? ? , â??" â??" 0 , ? ? 1 ? ? ? ? 0 log ?\textbf{and}1, ? ? ? ,\par
In other terms the elephant swarm optimization is applicable if the links have a greater than unity. The scheduling over all the links is not adopted as the power consumption would exponentially increase. The elephant swarm optimization is applied on all the links scheduled . The\par
In this phase the following equation is solved\par
If the results obtained on solving are not suitable then the optimization is not possible. If the solutions satisfies the condition then elephant swarm route optimization and radio layer optimizations are carried out to support the required transmission rate.\par
Phase C Evaluate all the links and retain the links if the following equation is satisfied.\par
This phase eliminates all the links whose is less than unity and retaining the links having an acceptable . 
\section[{Phase D}]{Phase D}\par
Compute using the following equation and . If the optimized schedule is equitant to the existing or previous schedule then no further optimization is possible. If the optimized is not similar to the current and previous MAC layer schedule the new schedule is adopted.\par
enables to identify the maximum power utilization link so we can schedule it the additional slots available thus aiding energy conservation. 
\section[{Phase E}]{Phase E}\par
In the last phase of the elephant swarm optimization algorithm the optimal solution achieved using a cross layer approach is verified using the following definition If the solution does not satisfy the above equation then no optimization is possible owing to current network dependent reasons. If optimal solution is obtained and incorporated network performance in terms of data aggregation, improved data rates and higher network lifetimes.\par
The elephant swarm optimization is realized using a cross layer design approach to enhance network lifetime. The efficiency and the performance measure of this optimization technique is discussed in the subsequent section of this paper. 
\section[{IV.}]{IV.} 
\section[{Experemential Study}]{Experemential Study}\par
In this section of the paper we shall discuss the experimental study conducted to compare the elephant swarm optimization algorithm introduced in this paper with the popular LEACH \hyperref[b4]{[15]} protocol. The elephant Using the above definitions we can obtain the new the layer schedule represented as\par
From the above equation it is evident that the optimization is computationally heavy and increases exponentially as the links of the sensor nodes increase (i.e. for dense networks) and the slot 2 value increases. The computation complexity of the elephant swarm optimization can be reduced if the number of slots are doubled to . The two fold increase in the number of time slots enables achieving lower power consumption as the sensor nodes have numerous slot options and sleep induction is effective. Furthermore in the case of high sensing activity leading to greater data transmissions, the data to the sink is scheduled using multiple TDMA slots to enable energy conservation and accurate data aggregation.\par
The elephant swarm optimization model can be summarized in the form of the algorithm given below realized through multiple phases described below.\par
Phase A Initialize the schedule based on the data . The is initialized such that link the schedule is constructed in a manner such that all the links are provided at least a slot in .2 ? ? ? ? . . . ? 1 ? ? ? ? ? â??", ? 1.0 ? ? , 1, ? ? ? ,\par
swarm optimization model and the LEACH protocol was   The wireless sensor network test bed was considered to be spread over a terrain measuring 25 25 meters. The wireless sensor nodes deployed over the terrain are varied from 450,500,550,600,650 and 700 nodes respectively. The test bed considered sensor nodes mounted with temperature sensors having a sensing range of 3m. The radio range considered is 5m. Sensing events are induced every 0.1 seconds. Inducing such high sensing activity and considering dense networks enables high traffic injection into the test bed. High traffic injection considered in the test beds results greater data transactions resulting in rapid energy depletion in the overall network. The simulation study was conducted to observe the network life time of the test bed. The threshold of the network lifetime analysis was set to 30\% i.e. the simulation study was conducted until 30\% of the network energy depletes.\par
To prove the efficiency of the elephant swarm optimization algorithm network test beds with varied nodes densities (i.e. 450,500,550,600,650 and 700) was simulated. The total network energy depletion was initiated by inducing sensing events. Similar network topology was considered to simulate the Elephant Swarm Optimization algorithm and the popular LEACH test bed. High traffic injection considered in the test beds results greater data transactions resulting in rapid energy depletion in the overall network. The simulation study was conducted to observe the network life time of the test bed. The threshold of the network lifetime analysis was set to 30\% i.e. the simulation study was conducted until 30\% of the network energy depletes.\par
To prove the efficiency of the elephant swarm optimization algorithm network test beds with varied nodes densities (i.e. 450,500,550,600,650 and 700) was simulated. The total network energy depletion was initiated by inducing sensing events. Similar network topology was considered to simulate the Elephant Swarm Optimization algorithm and the popular LEACH Algorithm. The simulation time was recorded when the total network energy depleted by 30\%. The results obtained are shown in Figure \hyperref[fig_6]{1} given below. The results obtain show that the cross layer optimization based on the elephant swarm model exhibits a 72.58\% improvement in network lifetime when compared to the LEACH protocol.\par
The rate at which the sensor node energy decays with time is also observed ad the results obtained is shown in Figure \hyperref[fig_2]{2}. The Elephant Swarm optimization model proposed in this paper adopts a cross layer approach when compared to the LEACH The elephant swarm optimization proposed also enables communication overhead reduction even for dense wireless sensor networks. The communication overheads are reduced greatly by the reduction of the number of retransmissions of data packets and optimized routing incorporated in the elephant swarm model. The communication overhead of the LEACH protocol is about 36.6\% greater than that of the  
\section[{W I R E L E S S S E N S O R N O D E E N E R G Y D E C A Y R A T E}]{W I R E L E S S S E N S O R N O D E E N E R G Y D E C A Y R A T E}\par
ELEPHANT SWARM LEACH  
\section[{Conclusion and Future Work}]{Conclusion and Future Work}\par
In this manuscript the authors address the problem in enhancing the network lifetime of wireless sensor networks. The elephant swarm optimization technique is adopted to address the issue that exists. A cross layer approach is adopted to incorporate optimizations at the routing, radio and the MAC layers. A TDMA based MAC layer is considered and the MAC schedule is optimized in accordance to the routing and the radio link layer optimization. The system model considered is clearly discussed. The optimization function which needs to be solved using the elephant model is also discussed. The elephant swarm optimization is achieved using a phased approach discussed in this paper. The experimental evaluation conducted proves the efficiency of the proposed elephant swarm optimization technique over the popular LEACH protocol in terms of improved network lifetime, reduced sensor node energy decay rate, higher active node ratios and lower communication overheads. The overall network lifetime of the varied scenarios presented proves enhancement of about 73\% thus justifying the robustness of the proposed elephant swarm optimization technique. To prove the increase in network lifetime the ratio of the sensor nodes active at regular time intervals is noted the results obtained are shown in Table \hyperref[tab_0]{1}. These results have been obtained for varying sensor node deployment densities. The graphical analysis is presented in Figure \hyperref[fig_9]{4} of this paper given below. The results described in the table prove that the percentage of active nodes using the elephant swarm optimization is greater than the nodes alive while using the LEACH protocol. The experimental study discussed in this section of this paper proves that the elephant swarm optimization technique proposed in this paper exhibits better network performance than the popular LEACH protocol commonly used. The enhanced networks performance is proved in terms of network lifetime, communication overhead reduction, reduced energy decay in the sensor nodes and enhanced active node ration in the network.\par
proposed optimization model. The results obtained are as shown in Figure \hyperref[fig_11]{3} of this paper. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}E}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}©E}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_2}2 ©}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}E}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-6.png}
\caption{\label{fig_4}2 ©}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-7.png}
\caption{\label{fig_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-8.png}
\caption{\label{fig_6}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-10.png}
\caption{\label{fig_9}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-11.png}
\caption{\label{fig_10}E}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-12.png}
\caption{\label{fig_11}Figure 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.13991769547325103\textwidth}P{0.10493827160493827\textwidth}P{0.3183127572016461\textwidth}P{0.2868312757201646\textwidth}}
Number of Sensor Nodes\tabcellsep Sim Time (S)\tabcellsep Elephant Swarm Active Node Ratio\tabcellsep Leach Active Node Ratio\\
450\tabcellsep 293\tabcellsep 99.77777778\tabcellsep 79.55555556\\
500\tabcellsep 335\tabcellsep 99.8\tabcellsep 80.6\\
550\tabcellsep 240\tabcellsep 99.81818182\tabcellsep 77.27272727\\
600\tabcellsep 193\tabcellsep 99.83333333\tabcellsep 80.66666667\\
650\tabcellsep 222\tabcellsep 99.84615385\tabcellsep 77.53846154\\
700\tabcellsep 209\tabcellsep 99.85714286\tabcellsep 76.42857143\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 			\footnote{© 2013 Global Journals Inc. (US) Year} 			\footnote{© 2013 Global Journals Inc. (US) Year} 			\footnote{© 2013 Global Journals Inc. (US)} 		 		\backmatter  			 
\subsection[{Congestion Control and Endurance Routing}]{Congestion Control and Endurance Routing}\par
The future of this work can be considered to compare the elephant swarm optimization technique with other swarm optimization techniques like particle swarm, ant swarm and a few other evolutionary computing based optimization techniques and prove its robustness. 23. G. J. Foschini and J. Salz, "Digital communications over fading radio channels," Bell Systems Technical J., pp. 429-456, Feb. 1983. 
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\end{document}
