A Hybrid Weight-Based Clustering Algorithm for Wireless Sensor Networks

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1 Open Access Library Jornal A Hybrid Weight-Based Clstering Algorithm for Wireless Sensor Networks Cheikh Sidy Mohamed Cisse, Cheikh Sarr * Faclty of Science and Technology, University of Thies, Thies, Senegal sidymohamed@gmail.com, * csarr979@hotmail.com Received May 05; accepted May 05; pblished 8 May 05 Copyright 05 by athors and OALib. This work is licensed nder the Creative Commons Attribtion International License (CC BY). Abstract Clstering in wireless sensor network (WSN) is an efficient way to strctre and organize the network. The clster head (CH) forms dominant set in the network responsible for the creation of clsters, maintenance of the topology and data aggregation. A clster head manages the resorce allocation to all the nodes belonging to its clster. In this paper, we propose a novel distribted clstering approach called Hybrid Weight-based Clstering Algorithm (HWCA). HWCA considers the neighborhood, the distance from the base station combined with the consmed energy as a hybrid metric to elect clster head. The time reqired to identify the clster head does not depend on the nmber of node and can be compted in a finite nmber of iterations. Or soltion also aims to provide better performance sch as maximizing the life time, redcing the nmber of lost frames in order to satisfy application reqirements. Simlation reslts show that HWCA improves the network lifetime and redces the nmber of lost frames compared with other similar approaches. Keywords Wireless Sensor Network (WSN), Clster Head, Clster, Election, Weight, Hybrid Metric Sbject Areas: Network Modeling and Simlation. Introdction A Wireless Sensor Network (WSN) is an ad hoc network with a large nmber of nodes that are smart microsensor able to collect, transmit data (like heat, hmidity, vibration...) and convert them into digital qantities atonomosly []. Each of these micro-sensor is basically eqipped with a sensing device to collect data from the environment, a processing nit to do some operations on data, a transceiver to send and receive collected, and an energy sorce to provide the reqired energy to operate (sally a battery) []. The obtained data are transmitted over mltiple hops to a sink also called base station (BS) []. The end ser can access the data via Internet so * Corresponding athor. How to cite this paper: Cisse, C.S.M. and Sarr, C. (05) A Hybrid Weight-Based Clstering Algorithm for Wireless Sensor Networks. Open Access Library Jornal, : e574.

2 that the data are processed. The concept of dividing the geographical region to be covered into small zones has been presented implicitly as clstering in the literatre [4]. The clstering techniqe means partitioning network nodes into grops called clsters, giving to the network a hierarchical organization. The groping of sensor nodes into clsters has been widely prsed by the research commnity in order to achieve the network scalability objective [5]. Each sensor node in a clster collects information and transmits them directly to the base station which can qickly deplete their energy that will redce the network lifetime. To solve this problem, a node is elected as clster head of the grop. Its role is to aggregate data from other nodes in the clster and then transmits these data to the base station with a long distance commnication. Therefore, it is obvios that clster head shold have more resorces than other nodes. The aim of this paper is to propose a distribted algorithm to select these clster head nodes. The election is based on a hybrid metric taking into accont three parameters: the one hop neighborhood, the amont of energy consmed and the distance from nodes to the base station. The rest of the paper is organized as follows: Section presents relative works for clstering on wireless sensor networks. In Section, we propose the Hybrid Weight-based Clstering Algorithm (HWCA). Simlation reslts are presented in Section 4 while conclsions are offered in Section 5.. State of Art Literatre proposes several techniqes for clster formation and clster head selection. All soltions aim to identify a sbset of nodes within the network and bind it a leader. The first soltions are based on a single metric to elect a node as clster head. The evoltion of these algorithms has proven that the combination of several metric is more efficient for better performance... Algorithms Based on a Single Metric for Clster Head Election The HCC (High-Connectivity Clstering) algorithm proposed in [6] se connectivity for the selection of clster head. The athors of this algorithm consider that a node having a higher degree (nmber of neighbors) has better connectivity and is therefore preferable to be elected as clster head. Ths, the node with the highest degree in its one-hop neighborhood becomes clster head. However, de to the criterion of degree, HCC bilds very dense clsters in small nmbers of iterations. In fact, clsters are very sensitive to the mobility of nodes. This reslts lead to freqently reconstrctions of clster strctre. HCC operates in synchronos mode and ses TDMA access method to avoid collisions. The athors of [7] have proposed a distribted clstering algorithm called LEACH for hierarchical roting algorithms in wireless sensor networks. The clster head election is based on generation of a random nmber and assigns this role to different nodes according to a Rond-Robin policy to ensre fair energy dissipation between nodes. The ronds have approximately the same time interval previosly determined [8]. In order to redce the amont of information transmitted to the base station, the clster head aggregates data captred by the member nodes that belong to its own clster, and send an aggregated packet to the base station. If the CH dies, all other nodes that belong to the clster will not be able to commnicate the information they have captred dring the crrent rond. Each rond consists of two phases. The first named Set-Up Phase dring which the network self-organizes into clsters and clster head are formed. The second phase called Stdy-State-Phase, dring which the data are collected and roted to the base station... Algorithms Based on a Mltiple Metrics for Clster Head Election The se of a single metric to elect clster head is not wise to generate stability on clster head formation. Ths, clstering algorithms that combine mltiple metrics for the clster head election have been proposed in the literatre. The Hybrid Energy Efficient Distribted clstering protocol (HEED) [9] has been proposed. It extends the basic scheme of LEACH by sing residal energy as primary parameter and network topology featres (e.g. node degree, distances to neighbors) as secondary parameters to break tie between candidate clster head. For clster selection HEED achieves to power balancing. The clstering process is divided into a nmber of iterations, and in each iteration, nodes that are not covered by any clster head doble their probability of becoming OALibJ DOI:0.46/oalib.0574 May 05 Volme e574

3 a clster head. Since these energy-efficient clstering protocols enable every node to independently and probabilistically decide on its role in the clstered network, they cannot garantee optimal set of clster head. The MWBCA algorithm (Mlti-Weight Based Clstering Algorithm) [0] combines three metric in calclating the weight of a node: the residal energy, the length of time the node remained clster head and the nmber of neighbor. In this algorithm, nodes with the higher remaining power have the opportnity to be CH. MWBCA ses probability parameter, information power remaining on a node and the average residal energy of neighboring nodes to select the CH. WCA (Weighted Clstering Algorithm) [] is a clstering algorithm for Mobile Ad Hoc Networks based on the combination of for metrics to calclate the weight of anode: the difference of degree D, the sm of the distances between node and each of its neighbors P, average relative mobility M and the time anode remains clster head. Each metric is weighted by a coefficient w i and the weight of a node is obtained by the following formla: W = w + wd + wm + wp 4 with w+ w + w + w4 =. A clster head can handle ideally M sensor nodes. M is already fixed and represents the size of a clster threshold, otherwise the optimal nmber of nodes arond a clster head. Indeed, the athors of this algorithm claim that a clster with more than M nodes becomes overloaded and rapidly depletes its resorces. With this algorithm, the node with the smallest weight is selected as clster head. Note also that node with less mobility is always best placed to be elected as clster head. BLAC (Battery-Level Aware Clstering) proposed in [] is a novel Battery-Level Aware clstering family of schemes. BLAC considers the battery-level combined with another metric to elect the clster head. BLAC comes in for distribted and local variants. BLAC aims to keep as many nodes alive as long as possible. The role of clster head is played by every node in trns in order to balance the energy consmption. All variants are similar to Density-based [] bt the metrics sed differ. BLAC combines the remaining energy B( ) with batt ( ) 0 B = where battcap is another metric. The remaining energy of node is defined as followed: ( ) battcap the initial capacity of the node battery (initially the same for every node), batt ( ) is the crrent battery level of node. This algorithm limits the remaining power between 0 and 0 to limit the freqent changes in the metric to avoid a non-stable clster hierarchy. The for variants of BLAC are BLAC-bg, BLAC-bs, BLAC-rg and BLAC-rs. BLAC-bg and BLAC-bs apply the same algorithm bt differ in the metric they se. BLAC-bg for Battery-Level Aware Clstering Battery degree is based on node degree. This variant ses a one-hop neighborhood to bild the network, so it stabilizes qickly. Any single change has a direct impact on neighbors and so on degree. BLAC-bs for Battery Level Aware Clstering Battery density ses the density ρ(). This variant comptes the clstering strctre with -hop information bt the stability is improved becase a single node has less impact on its neighbors. Battery-Level Aware Clstering RNG degree (BLAC-rg) and Battery-Level Aware Clstering RNG density (BLAC-rs) variants are variations of the first and the second ones. The biggest difference is that the algorithm rns in two steps. Before compting its metric (degree or density), a relative neighborhood graph is compted in order to keep only an interesting sbset of nodes. This allows memory storage saving and the se of less compting capacity for the clstering comptation. HWCA is the a distribted clstering algorithm which combines a hybrid metric composed by one hop neighborhood, consmed energy and distance from base station. Yet, nodes natrally change roles over time based on vale of the hybrid metric to provide better performance. HWCA also provides efficient clster head with no predefined size in order to match the nderlying network topology and to be reliable to small topology changes. Unlike soltions from literatre, HWCA bilds dynamic metric efficient clsters in a distribted way. Its main goal is to provide better performance (like for instance network lifetime, delivery ratio, etc.).. HWCA: A Hybrid Weight Based Clstering Algorithm In order to minimize the energy consmption of each micro-sensor and to generally increase the lifespan of the network, we propose the HWCA algorithm whose originality is based on a hybrid metric combining the neighborhood, distance from nodes to the BS and the energy consmed by each node. The HWCA algorithm also provides an energy balance throgh the network nodes. Indeed, the nodes then change natrally clster head according to the vale of the metric. In or algorithm a clstering organization is rn over the network to carry OALibJ DOI:0.46/oalib.0574 May 05 Volme e574

4 better performance. Each node sends its data to its clster head. Once all data are gathered, the clster head aggregates them and sends them to a base station. In this section, we present first or new hybrid metric and secondly the clster head election algorithm based on this metric... A New Hybrid Metric We se for clster head election of a node a combined weight metric m ( ) that takes into accont three system parameters: N( ) the nmber of nodes in the one-hop neighborhood of node. dist (, b ) the eclidian distance from node to the base station b. Ec ( ) the energy consmed by node. We also se the following notations: N the maximm nmber of nodes that a clster head can handle ideally. This is to ensre that clster head are not over-loaded and the efficiency of the system is maintained at the expected level. L the maximm distance between two nodes that represents the diagonal of the sqare. E the initial energy of node. Therefore we defined the combined metric for each node as: max ( ) For easily identification we se: Eqation () becomes: ( ) m ( ) (, ) c ( ) N L E( ) N dist b E = α + α + α. () ( ) N =, N max (, ) dist b = and L max ( ) ( ) E = E c. m ( ) = α + α + α. () The three system parameters, and are affected with a certain weighting factors chosen according to the system needs. For example, for improving network lifetime, energy control is very important in WSN networks, ths the weight of the corresponding parameter can be made larger. The flexibility of changing the weight factors helps s applying or algorithm to varios networks. A node has better chance to become clster head if it s metric m ( ) is the smallest in the clster meaning that we mst minimize, and. The first component contribting towards m ( ) in efficient neighborhood fnctioning. In fact when node has fewer neighbor nodes, it carries less information to the base station and becomes smaller. The second component contribting towards m ( ) in energy consmption dring transmission process. It is known that more power is reqired to commnicate to a larger distance. Therefore when node is nearer from the base station, it needs less power to transmit and is smaller. The third and last component contribting towards m ( ) in efficient energy consmption. Indeed, when node has not consmed too mch energy, the vale of becomes smaller. After vales of all the components are identified, we compte the weighted factors α, α and α. The contribtion of each individal weighing factor represents its importance relatively to the others one. We consider that in wireless sensor network the most important factor relies to the energy consmption, the second one relies to the power transmission and the last one to the neighborhood. Therefore we choose α = 0., α = 0. and α = 0.6. Note that these weighing factors are chosen sch that α+ α + α =. These vales are arbitrary at this time and shold be adjsted according to the system reqirements... Clster Head Election Algorithm Based on the preceding calclation of metric ( ) m of a node, we propose a new distribted algorithm that elects a set of node as clster head. We sppose no mobility in or algorithm even if the analysis can be extended to mobility scheme. However, node mobility wold make clstering very challenging since the node membership will dynamically change, forcing clsters to evolve over time. The clster head election procedre is invoked dring system activation and for a service time and also when a clster head dies or the existing do- OALibJ DOI:0.46/oalib May 05 Volme e574

5 minant set can no longer cover the entire network. We model a wireless sensor network as a graph G = ( V, E) where V is the set of sensors and E is the set of wireless links v between each pair of sensors and v which are in radio range of each other. We se the following notations: N( ) the neighborhood of a node. m ( ) the metric of node as compted in Eqation (). CH ( ) the identity of the clster head of node. mch ( ) the metric of the clster head of node. L ( ) the list of neighbors nodes v that chose as clster head (for each v N( ), v L( ) if CH ( v) = ). The goal of Algorithm is to determine for each node its clster head. Once the metric of each node is compted, HWCA rns algorithm at each node as follow: initially, each node is its own clster head and there are no other nodes that have chosen as clster head (instrction to ). Then, for each node v in the neighborhood of, if both metric of and metric of clster head of is higher than metric of v (instrction 4 to ) and node is not clster head of another node (instrction ), node chooses v as clster head (instrction 4). In or algorithm we can notice that if a node doesn t have any other node in its neighborhood, it stays as clster head since the initialization phase and send directly his data to the base station. When a clster head receives data from a reglar node, it stores them ntil it needs to send its own data and then sends the aggregated data to the base station... An Illstrative Example We demonstrate or weighted clstering algorithm with the example shown in Figre. A node can hear broadcast beacons from nodes which are within its transmission range. An edge between two nodes signifies that the nodes are neighbors of each other. Initially, all nodes are clster head with a specific metric. For example CH = and node has a metric of (, ) and is its own clster head with metric initialized to ( ( ) mch ( ) = ). Figre shows the achieved clster head selection algorithm. Note that two clster head can be immediate neighbors. Table explains the final sitation of each node on the network. We sppose that after a certain time, node y and v die. The clster head reelection procedre is depicted in Figre and Table explain the final sitation of residal node on the network. 4. Simlations To evalate the performances of HWCA, we perform some simlations nder the NS- simlator (NS-.5 ). We compare HWCA to LEACH becase of its energy efficiency concern. In order to observe different behaviors for LEACH, two vales of the p parameter are sed 5% and 0% (p is the average nmber of clster head in the network). Algorithm. HWCA algorithm rns at each node. OALibJ DOI:0.46/oalib May 05 Volme e574

6 CH(t)=t mch(t)=6 t,6 CH(v)=4 mch(v)=4 v,4 y,9 CH()= mch()=, CH(z)=z mch(z)= z, CH(y)=y mch(y)=9 x,5 CH(x)=x mch(x)=5 Simple node Clsterhead Figre. Initial state. CH(t)= mch(t)=6 t,6 CH(v)=z mch(v)= v,4 y,9 CH()= mch()=, CH(z)=z mch(z)= z, CH(y)=t mch(y)=6 x,5 CH(x)= mch(x)= Simple node Clsterhead Figre. Clster head creation. Table. Sitation of nodes in the network. Node CH Explication v y x z t Node v selects z as CH becase it has the lowest metric from its neighborhood. Therefore v becomes a simple node. Node y selects t as CH becase it has the lowest metric from its neighborhood. Therefore y becomes a simple node. Node x selects as CH becase it has the lowest metric from its neighborhood. Therefore x becomes a simple node. z z Node z becomes CH becase it has the lowest metric from its neighborhood. Node becomes CH becase it has the lowest metric from its neighborhood. t t Even if node t does not have the lowest metric from its neighborhood (m() < m(t)), it becomes a CH becase another node for instance y has already chose t as CH. OALibJ DOI:0.46/oalib May 05 Volme e574

7 CH(t)= mch(t)= t,6 CH()= mch()=, CH(z)= mch(z)= z, x,5 CH(x)= mch(x)= Simple node Clsterhead Figre. Clster reelection after the death of node y and v. Table. Sitation of nodes in the network after death of nodes y and v. Node CH Explication x Node x selects as CH becase it has the lowest metric from its neighborhood. Therefore x becomes a simple node. z z Node z has no more neighbors therefore it stays CH. Node becomes CH becase it has the lowest metric from its neighborhood. t Node t changes its state from CH to a simple node becase no node chooses t as clster head and node has the lowest metric from its neighborhood In order to se a realistic model for transmitting and receiving costs we consider the Texas Instrments CC40 ZigBee wireless modle as sensor network. These sensor nodes consme 0.77 mw when idle, 5.46 mw for receiving (Rx), and. mw for transmitting (Tx). Data traffic is also simlated and each node generates 56 kb/s of data and sends them to its clster head. When a clster head receives data from a child, it stores them ntil it needs to send its own data and then sends the aggregated data to its base station. The nmber of nodes is 0 either 50 while the initial energy of each one is set to 5 J. Nodes are placed on a grid. 4.. Network Lifetime We define the network lifetime as the time ntil all nodes die [6]. Figre 4 and Figre 5 illstrate the benefits of HWCA while regarding the nmber of alive nodes over the time. Reslts show clearly that with HWCA, the network lifetime is extended by respectively 5% and 40% compared to LEACH p = 5% and p = 0% for 0 nodes as show on Figre 4. On Figre 5, with 50 sensor nodes in the network, this lifetime extension remains at respectively % and 5% compared to LEACH p = 5% and p = 0%. These positive reslts cold be explained by the fact that in HWCA, when clster head are dead or their hybrid metric has increased, other nodes take their role till dying as well, and so on till there is no remaining alive node. This dynamic changing role reslts in an efficient energy balance over sensor nodes that extend globally the network lifetime. Nevertheless, LEACH performances greatly depend on the nmber of clster head that have been set p (throgh the parameter p). The more clster head, the more nodes forwarding data and ths the shorter lifetime. OALibJ DOI:0.46/oalib May 05 Volme e574

8 Figre 4. Network lifetime for 0 nodes. 4.. Delivery Ratio Figre 5. Network lifetime for 50 nodes. Figre 6 and Figre 7 display the delivery ratio of every algorithm with regards to time for 0 and 50 nodes. Delivery ratio is compted as the amont of data received by the base station divided by the amont of data sent throgh the whole network. We observe that the different variants of LEACH (p = 5% and p = 0%) lose more data than HWCA algorithm regardless of the nmber of clster-heads when the network becomes denser (Figre 7) even if this vale is better for LEACH p = 0% when the network is not too load (Figre 6). Indeed, in LEACH, nodes withot clster-head cannot send data so their data are lost. Even when the nmber of clster-heads increases, some data are still lost becase nodes need to share the medim with other nodes in the same clster and ths may not have enogh time to transmit all data. OALibJ DOI:0.46/oalib May 05 Volme e574

9 Figre 6. Delivery ratio for 0 nodes. Figre 7. Delivery ratio for 50 nodes. 5. Conclsion and Ftre Works In this paper, we have introdced a new clstering algorithm. HWCA combines the neighborhood, the distance from nodes to the BS and the energy consmed by each node as a hybrid metric. HWCA also balances energy consmption throgh the network as nodes dynamically change their role (from simple node to clster head and vice-versa) depending on the vale of the metric and the clster head selection algorithm. The algorithm is distribted and modifications de to network dynamics are handled locally, allowing scalability. Reslts show that or proposition improves network lifetime with p to 40% and delivery ratio. For ftre work we will extend the comparison of HWCA with other algorithms and plan to rn experimentations in real world. References [] Al-Karaki, J.N., Ul-Mstafa, R. and Kamal, A.E. (004) Data Aggregation in Wireless Sensor Networks-Exact and OALibJ DOI:0.46/oalib May 05 Volme e574

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