A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks

Abstract

Clustering techniques have emerged as a popular choice for achieving energy efficiency and scalable performance in large scale sensor networks. Cluster formation is a process whereby sensor nodes decide which cluster head they should associate with among multiple choices. Typically this cluster head selection decision involves a metric based on parameters including residual energy and distance to the cluster head. This decision is a critical embarkation point as a poor choice can lead to increased energy consumption, thus compromising network lifetime. In this paper we present a novel energy efficient cluster formation algorithm based on a multi-criterion optimization technique. Our technique is capable of using multiple individual metrics in the cluster head selection process as input while simultaneously optimizing on the energy efficiency of the individual sensor nodes as well as the overall system. The proposed technique is implemented as a distributed protocol in which each node makes its decision based on local information only. The feasibility of the proposed technique is demonstrated with simulation results. It is shown that the proposed technique outperforms all other well known protocols including LEACH, EECS and HEED resulting in a significant increase in network life.

Introduction

Wireless sensor networks (WSNs) have emerged as the-state-of-the-art technology in gathering data from remote locations by interacting with physical phenomena and relying on collaborative efforts by large numbers of low cost devices [1]. Typically, a WSN comprises of hundreds or thousands of low cost sensor nodes. Each sensor node has an embedded processor, a wireless interface for communication, a non replenish-able source of energy, and one or more on-board sensors such as temperature, humidity, motion, speed, photo, and piezoelectric detectors [2]. Once deployed, sensor nodes collect the information of interest from their on-board sensors, performs local processing of these data including quantization and compression, and forward the data to a base station (BS) directly or through a neighbouring relay node. The ability to have direct interaction with physical phenomena resulted in the development of a vast number of applications for wireless sensor networks such as, military, commercial, intrusion detection and industrial, healthcare and disaster and rescue operations [3].

Most deployments of WSNs require unattended operation; therefore, sensor nodes have to rely on batteries for communication and information gathering. Sensor nodes are significantly constrained in available resources including storage, computational capacity, however energy accounts for the most restrictive of all factors because it affects the operational lifetime of WSN. It is a well established fact [4] that wireless communication is the major source of energy drainage in WSN. Therefore, energy efficient communication protocols and topology architectures are highly desirable. In recent years clustering has emerged as a popular approach for organizing the network into a connected hierarchy [5]. By using clustering, nodes are organized into small disjoint groups called clusters. Each cluster has a coordinator referred to as cluster head (CH) and a number of member nodes. Clustering results in a hierarchical network in which CHs form the upper level and member nodes form the lower level. In contrast to flat architectures, clustering provides distinct advantages with respect to energy conservation by facilitating localized control and reducing the volume of inter-node communication. Moreover, the coordination provided by the CH allows sensor nodes to sleep for extended period thus allowing significant energy savings. Despite many advantages of clustering in WSNs such as network scalability, localized route set up, bandwidth management, the fundamental objective centers around energy conservation.

Cluster formation is a process whereby sensor nodes decide with which CH they should associate among multiple choices. After the CHs are elected, the non-CH nodes are faced with the task of selecting a CH from a number of possible candidates based on the criterion of optimal energy use. For a sensor node, selecting the CH based on a single objective can lead to poor energy use because the nearest CH may be located at a greater distance from base station than the other CHs. Thus for that particular node this may not be the best choice. In addition, factors like residual energy and node degree may also be of importance when making a decision. For example, as shown in Fig. 1, a sensor node i receives advertisement messages form three different CHs A, B and C. The parameters important to the choice of CH are included in the advertisement message.

If node i were to make a decision based on a single parameter it could result in a bad choice over all. For example selecting based on highest residual energy will lead to choice A which is at a greater distance from the base station as compared to B and C, thus resulting in more energy expenditure. Similarly making a choice based on shortest distance from self results in B which has the least residual energy. Hence, when a node makes a decision about associating with the CH, it is necessary that as many parameters of local and global significance as possible should be considered. This premise forms the basis of the work in this paper when tackling the multiple criteria that influence energy efficiency. We use a multi-criterion optimization technique in this decision process to minimize the energy used both in control and data transmission phases.

Multi-criterion optimization (MCOP) or multi-objective decision making is an engineering design method used in large scale complex systems to optimize the efficiency of several subsystems [6]. Typically, MCOP deals with problems that have several conflicting and possibly non-commensurable criteria which should be simultaneously optimized. The solutions to such problems find their roots in techniques involving linear programming, objective functions and Pareto optimality. Here MCOP is used in a novel fashion in a cluster formation scheme for WSNs. The motivation behind the MCOP-based cluster formation technique is to maximize network lifetime by selecting the best CH for a group of sensor nodes by considering multiple criteria such as distance of node to the CH, distance between CH and sink and residual energy.

The rest of the paper is organized as follows. Section 2 presents an overview of related work. Section 3 outlines the assumption for the network model. Section 4 presents the CH election process followed by a description of message types in Section 5. Our multi-criterion optimization technique is presented in Section 6. Section 7 presents results of energy consumption based on the mathematical model. Simulation results are presented in Section 8. Finally, the main conclusions and directions for future research are presented in Section 9.