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\title{MSEP-E: Enhanced Stable Election Protocol with Multihop Communication}
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\begin{document}

             \author[1]{Raju  Pal}

             \affil[1]{  JIIT Noida}

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\date{\small \em Received: 15 December 2013 Accepted: 2 January 2014 Published: 15 January 2014}

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\begin{abstract}
        


In this paper, we have implemented cluster based novel multihop stable election protocol extended (MSEP-E) which does multihop communication between CHs and sensor nodes towards the sink. The sensor nodes and CHs which are nearer to sink send data directly to the sink while the nodes which are farther from the sink send data to its nearest hop towards the sink. Multihop communication is often required when communication range of sensor nodes is limited or number of sensor nodes is very large in the network. Evaluation and comparison reveals that MSEP-E protocol utilizes less power and attain greater network lifetime compared to stable election protocol extended (SEP-E).

\end{abstract}


\keywords{Multihop, SEP, SEP-E, MSEP-E.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
wireless sensor network (WSN) in its simplest form can be defined as \hyperref[b0]{[1,}\hyperref[b1]{2,}\hyperref[b2]{3]} a network of (possibly low-size and low-complex) devices denoted as nodes that can sense the environment and communicate the information gathered from the monitored field (e.g. an area or volume) through wireless links; the data is forwarded, possibly via multiple hops relaying, to a sink (sometimes denoted as controller or monitor) that can use it locally, or is connected to other networks (e.g. the Internet) through a gateway. The nodes can be stationary or moving. They can be aware of their location or not. They can be homogeneous or heterogeneous.\par
In cluster based approach, solely some of sensor nodes (CHs) in particular WSN are permitted to transmit sensed data towards the sink. The primary issue is that, this allows sensor nodes to sense and transmit the sensed information (in data packet form) to CHs directly, instead of routing through its neighbor and then all data is aggregated by CHs and sent over to the sink. In clustering, it is evident that the CH nodes will be over-loaded with the long-range communication to sink. Since energy dissipation during communication is proportionate to the square of distance to the sink from sending sensor node, energy of C H nodes exhausts drastically and hence the lifetime of the network get significantly reduced. One solution to this is to rotate the role of a CH among over all the sensor nodes as proposed in low-energy adaptive clustering hierarchy (LEACH) \hyperref[b3]{[4]}, power-efficient gathering in sensor information systems (PEGASIS) \hyperref[b4]{[5]}, and hybrid energy efficient distributed clustering (HEED) \hyperref[b5]{[6]}. However, these protocols have shown poor performance in heterogeneous environment because the low-energy nodes will die more quickly than the high-energy ones. In \hyperref[b6]{[7]}, G. maragdakis, I. matta and A. bestavros proposed stable election protocol (SEP) in which every sensor node in a heterogeneous two-level hierarchical network independently elects itself as a cluster head based on its initial energy relative to that of other nodes. In \hyperref[b19]{[8]}, F. A. Aderohunmu, J. D. Deng have proposed enhanced stable election protocol (SEP-E) which introduce three level heterogeneity in SEP. Similarly, many authors have proposed new clustered routing schemes to address the issues of heterogeneity  {\ref [9, 10, and 11]}.\par
In this paper, we have further extended the SEP-E by using multihop communication between the sensor nodes. The nodes which are nearer to the sink send data directly to it rather than sending data to the CHs to reduce the load on CHs. The CH which is farthest from the sink sends data to the nearest CH towards the sink. Similarly all CHs send their data to the CH which is nearer to it compared to the sink. This communication scenario is depicted in Figure \hyperref[fig_0]{1}.\par
The rest of the paper is organized as follows. Section 2 includes a detailed survey of the related research. Section 3 exhibits the detail of the proposed scheme. Simulation results and its discussion are presented in Sections 4 and 5. Finally, Section 6 concludes the paper. 
\section[{II.}]{II.} 
\section[{Related Works}]{Related Works}\par
Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption \hyperref[b11]{[12]}. Low Energy Adaptive Clustering Hierarchy (LEACH), a clustering based protocol that utilizes randomized rotation of local cluster base station (cluster-heads) to evenly distribute the energy load among the sensors in the network was proposed in \hyperref[b3]{[4]}. These sensors organize themselves into clusters using a probabilistic approach to randomly elect themselves as heads in an epoch. During the setup phase, when clusters are being created, each node decides whether to become a CH for the current round. This decision is based on a predetermined fraction of nodes and the threshold T(s), which is given by the following equation:(??) = ? ?? ?? ???? 1??? ?????? ×(?? ?????? ( 1 ?? ?????? ? )) ???? ?? ? ?? 0 ?????????????????\textbf{(1)}\par
Where p\textunderscore optthe predetermined percentage of CHs and r is is the count of current round. The G is the set of sensor nodes that have not been CHs in the last 1/p\textunderscore opt rounds. Using this threshold, each node will be a CH at some round within 1/p\textunderscore opt rounds. After 1/p\textunderscore opt rounds, all nodes are once again eligible to become CHs. In this way, the energy concentration on CHs is distributed.\par
However, LEACH protocol is not heterogeneityaware, in the sense that when there is an energy imbalance between these nodes in the network, the sensors die out faster than they normally should have if they were to maintain their energy uniformly. In real life situation it is difficult for the sensors to maintain their energy uniformly, thus, introducing energy imbalances. LEACH assumes that the energy usage of each node with respect to the overall energy of the system or network is homogeneous. Conventional protocols such as Minimum Transmission Energy (MTE) and Direct Transmission (DT) \hyperref[b12]{[13]} do not also assure a balanced and uniformly use of the sensor's respective energies as the network evolves.\par
Stable Election Protocol (SEP), was proposed in \hyperref[b6]{[7]}, a heterogeneous aware protocol, based on weighted election probabilities of each node to become cluster head according to their respective energy. This approach ensures that the cluster head election is randomly selected and distributed based on the fraction of energy of each node assuring a uniform use of the nodes energy. In the SEP, two types of nodes (two tier in-clustering) and two level hierarchies were considered. Enhanced stable election protocol (SEP-E), was proposed in \hyperref[b19]{[8]}. Using a heterogeneous three-tier node setting in a clustering algorithmic approach, nodes elect themselves as cluster heads based on their energy levels, retaining more uniformly distributed energy among sensor nodes.\par
In clustered WSNs, two typical methods are used to aggregate data. In the first method data is aggregated after it has been collected from all member nodes before the inter-cluster communication occurs and in the second method data is aggregated over each passing hop \hyperref[b13]{[14,}\hyperref[b14]{15]}. In \hyperref[b14]{[15,}\hyperref[b15]{16]}, the authors have presented multihop routing algorithm for inter-cluster communication. This algorithm is based on multi-hop routing, which works on the principle of divide and conquer, and performs well in terms of load balance and energy efficiently as compared to LEACH.\par
In \hyperref[b16]{[17]}, the authors have studied LEACH scheme and proposed two new schemes (i.e., energy-LEACH and multihop LEACH). Energy-LEACH improves the CH selection method and Multihop LEACH (M-LEACH) \hyperref[b17]{[18]} improves the communication mode from single-hop to Multihop between CH and BS. Both the schemes have better performance than LEACH scheme. In this paper we enhance the SEP-E proposed in \hyperref[b19]{[8]}, by introducing multihop communication to prolong the network lifetime and stability of the network. 
\section[{III. Radio Energy and Network Model a) Radio energy model}]{III. Radio Energy and Network Model a) Radio energy model}\par
Radio Energy Model used is based on \hyperref[b3]{[4,}\hyperref[b18]{19]}. Energy model for the radio hardware energy dissipation where the transmitter dissipates energy to run the radio electronics and the power amplifier, and the receiver dissipates energy to run the radio electronics. Here both the free space (d2 power loss) and the multipath fading (d4 power loss) channel models have been used, depending on the distance between the transmitter and receiver. Power control can be used to invert this loss by appropriately setting the power amplifier-if the distance is less than a threshold do, the free space model is used; otherwise, the multipath model is used. Thus, to transmit an l-bit message a distance, the radio expends the data is communicated and ?? ?? is the distance threshold for swapping amplification models, which can be calculated as ?? ?? = ??? ???? ?? ???? ?\par
As it can be seen, the transmitter expends energy to run the radio electronics and power amplifier, while the receiver only expends energy to run the radio electronics. We consider both free space (n = 2, ?? ?????? =?? ???? ) and two-ray multipath (n = 4, ?? ?????? =?? ???? ) models to approximate signal attenuation as a function of the distance between transmitters and receivers. 
\section[{b) Network Model}]{b) Network Model}\par
Our network model is composed of three types of nodes deployed uniformly in a square region, including normal nodes, advanced nodes, and a few super nodes (Figure  {\ref 2}). The selection probability of each node to become a CH is weighted by the initial energy of a node relative to that of the normal node in the network. We assume each sensor node transmits sensing data to the BS through a selected CH by using multihop communication approach. All the CHs are selected periodically by different weighted probability. If CH is farther from the sink it sends the data to another CH which is nearer to the sink. Similarly each member node sends data directly to sink if they are nearer to the sink compared to its associated CH. Assumptions:\par
? All the sensor nodes uniformly dispersed within a square field ? All sensor nodes and sink are stationary after the deployment. 
\section[{? Multihop communication towards sink.}]{? Multihop communication towards sink.}\par
? A WSN consists of heterogeneous nodes in terms of node energy. ? All the sensor nodes are of equal significance.\par
? CHs perform data aggregation.\par
? The sink has enough energy in comparison with the other nodes in the network.\par
IV. 
\section[{Msep-E}]{Msep-E}\par
In this section we proposed an extension of enhanced stable election protocol by introducing multihop communication between the nodes. When we consider a general sensor network that may be deployed over a large region, the energy spent in the power amplifier related to distance may dominate to such an extent that using multi-hop mode may be more energy efficient than single-hop mode. 
\section[{Setup phase}]{Setup phase}\par
Let us assume the case where a percentage of the population of sensor nodes is equipped with more energy resources than the normal sensor nodes in the network. Suppose ?? ?? is the initial energy of each normal node. The energy of each advance node is then ?? ?? (1 + ??)and each super node is then ?? ?? (1+?). The total initial energy of the new heterogeneous network setting is?? * ?? ?? (1 + ?? * ?? + ?? ?? * ??)) \hyperref[b19]{[8]}. 
\section[{Figure 2 : Heterogeneous WSN model}]{Figure 2 : Heterogeneous WSN model}\par
Where ?? is the number of nodes, ?? is the proportion of advanced nodes to the total number of nodes ?? with energy more than the rest of the nodes and mo is the proportion of super nodes. So, the total energy of the system is increased by a factor of1 + ?? * ?? + ?? ?? * ??).\par
Our probability setting p\textunderscore nrm, p\textunderscore adv and p\textunderscore sup and the threshold T(??????), T(sup), ??(??????) for normal, advanced and super nodes respectively remains the same as in \hyperref[b19]{[8]}. The cluster head have been selected based on the threshold value.\par
Once the nodes have elected themselves to be cluster heads they broadcast an advertisement message (ADV). Each non cluster-head node decides its cluster for this round by choosing the cluster head that requires minimum communication energy, based on the received signal strength of the advertisement from each cluster head. After each node decides to which cluster it belongs, it informs the cluster head by transmitting a join request message (Join-REQ) back to the cluster head. After receiving all the messages from the nodes that would like to be included into the cluster and based on the number of nodes in the cluster, the cluster head creates and announces a TDMA schedule, assigning each node a time slot when it can transmit. Each cluster communicates using different CSMA codes to reduce interference from nodes belonging to other clusters.  
\section[{Multihop communication mechanism}]{Multihop communication mechanism}\par
Once the clusters are formed, each member node sends data messages in its time slot at the idle a)\par
state of a frame. In order to avoid collisions during communication, a kind of CSMA model is set up. Instead of transmitting the processed data to the CH directly, every node decides whether to choose another node as the next hop or not based on above pseudo code. Similarly, each CH decides whether to transmit the data to the BS directly or to send them to the next hop. When a CH has data to send to the BS (i.e., at the end of its frame), it must sense the channel to see if anyone else is transmitting data, if so, the CH waits to transmit the data.\par
The pseudo code for our multihop communication scheme described as follows: // non-CH nodes communication (Association to the CH) For i=1: When the sensor nodes are deployed in regions of dense vegetation or uneven terrain, it may be beneficial to use multi-hop communication among the nodes in the cluster to reach the cluster head. As it is possible for nodes to remain disconnected from the network due to a cluster head not being in range, each node is able to request another connected node to become a cluster head. This occurs after a timeout period and is done through a normal advertisement message.\par
V. 
\section[{Performance Matrices}]{Performance Matrices}\par
The following matrices are used to evaluate the performance of MSEP-E and SEP-E in different network scenarios. 
\section[{a) Network lifetime}]{a) Network lifetime}\par
Network lifetime strongly depends on the lifetimes of single nodes that constitute the WSNs. The lifetime of the network basically depends on two major factors: (i) how much energy it consumes over rounds and (ii) how much energy is available for its use (Total Residual Energy). The definition of the network lifetime is determined by the kind of service it provides. In many cases, it is necessary that all the sensor nodes stay alive as long as possible. Since the network performance decreases as soon as a single node dies. In this scenario, it is important to know when the first node dies (FND). Furthermore, sensor nodes can be placed in proximity to each other. Therefore, adjacent nodes could record the same or identical data in the network. Hence, the death of a single or few nodes does not affect the performance of the network. In this case, the metric half node dies (HND) denotes an estimated value for the half life period of a network. Finally, the metric last node dies (LND) defines network lifetime as the time until all nodes have been drained of their battery energy. This metric is very rarely used in clustering algorithms. Since more than one node is necessary to perform the clustering technique. Hence, in this paper, we use two metrics (i.e., FND and HND) for the evaluation of different algorithms Stability: This is the time interval when the first node of the network dies. 
\section[{b) Throughput}]{b) Throughput}\par
We measure the total rate of data sent over the network, the rate of data sent from cluster heads to the sink as well as the rate of data sent from the nodes to their cluster heads. 
\section[{VI. Simulation Results and Discussion}]{VI. Simulation Results and Discussion}\par
In this section, we evaluate the performance of MSEP-E via MATLAB simulations. We compare it with SEP-E in the same heterogeneous setting, where the   {\ref 2}). We extra initial energy of advanced nodes and super nodes is uniformly distributed over the sensor field. We use denote a normal node with "o", an advanced node with "*", a super node with "\par
?", and the BS with "×". The simulation parameters are mentioned in Table  {\ref 1}. fter a node drain its energy it dies, and it cannot communicate with other nodes any more. We run the simulation on above setting and found that MSEP-E outperforms the existing SEP-E protocol. Figure 3 clearly indicate that MSEP-E prolong the lifetime of the network. Since SEP-E adopts single-hop communication for both non-CH and CH communication, it shows poor performance in large network areas because all the sensor nodes have to consume more battery energy to perform the long haul communication whereas MSEP-E adopts multi-hop communication, hence consumption of battery energy is less.  The comparison of total residual energy on different rounds is shown in Table \hyperref[tab_1]{2} and Figure 4 demonstrates the overall increase in total residual energy per round. Initially all nodes have 0.1 joule (J) of energy and total energy of the network is 17 J. After 200 rounds total residual energy of the network will be 12.7954 J and 9.0296 J for MSEP-E and SEP-E respectively. This indicate that SEP-E consume more energy approx 8 J then MSEP-E (approx 5J) after 200 rounds. Similarly, Table \hyperref[tab_1]{2} depicts the residual energy of both protocols after 500, 1000 and 1200 rounds. Hence, the energy consumption is very less when we use multihop communication.  Figure  {\ref 5} : Throughput of the network Figure  {\ref 5} illustrates the throughput of the network. The amount of data messages received at the sink will increase over number of rounds as compared with SEP-E since the lifetime of the network increased and we have more number of alive nodes. Hence the throughput of MSEP-E is increased by 3.5\%. Table \hyperref[tab_3]{3} and Figure 6 shows that stability of the network is increased by 20\%. In SEP-E first node died (FND) at 277 th round while in MSEP-E first node died at 333 th round. The half life of the network also increased by 88\%. The 50\% of the nodes died (HND) at 360th round and 680th round for SEP-E and MSEP-E respectively. The last node of the network (LND) died at 1135th round and 1383th round for SEP-E and MSEP-E respectively. This shows that overall performance and lifetime of the network significantly increased in the case of MSEP-E. 
\section[{Table 1 : Simulation parameters}]{Table 1 : Simulation parameters} 
\section[{VII.}]{VII.} 
\section[{Conclusion}]{Conclusion}\par
It has thus, been concluded that MSEP-E protocol consumes less energy as it uses energy minimizing techniques like multihop communication, clustering and data aggregation. The multi-hop communication approach is adopted for both non cluster head and cluster head nodes communication towards the sink. Simulation results indicate that MSEP-E can greatly balance energy consumption of an entire network and thus extends the network lifetime and stability of WSN. MSEP-E can be considered for applications such as health monitoring where energy utilization is critical. \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
     \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.26066666666666666\textwidth}P{0.374\textwidth}P{0.21533333333333335\textwidth}}
\%\tabcellsep \multicolumn{2}{l}{Number of}\\
Nodes\tabcellsep \multicolumn{2}{l}{rounds}\\
\tabcellsep SEP-\tabcellsep MSEP-\\
\tabcellsep E\tabcellsep E\\
1\%\tabcellsep 277\tabcellsep 333\\
25\%\tabcellsep 311\tabcellsep 401\\
50\%\tabcellsep 360\tabcellsep 680\\
100\%\tabcellsep 1335\tabcellsep 1383\end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
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