DASCA: A Degree and Size based Clustering Approach for Wireless Sensor Networks

Sensor networks require scalable solutions to tackle huge number of sensor nodes. The scalable solutions have to be energy efficient and robust in order that reliability and network lifetime is increased. hierarchical (cluster based) models are known to be promising solutions to the problem of scalability. In this paper we propose a distributed clustering strategy which restricts the number of nodes in each cluster, S and also limits the number of next hop neighbours of a node in a cluster, D (admissible degree). The clustering algorithm is simulated and compared with two other clustering algorithms, HEED and LEACH. Simulation results depict that the energy drain in our algorithm is much lesser than the other two algorithms, thereby providing a longer lifetime when compared to the other algorithms. The proposed algorithm provides for almost 5 times increase in the lifetime of the network, when compared to the other two algorithms

I. Introduction

Recent technological improvements have made the deployment of small, inexpensive, low-power, distributed devices, capable of local processing and wireless communication a reality. Such devices are called as sensors. Unlike traditional networks, sensor networks are characterized by dense deployment. The position of sensor nodes need not be engineered or predetermined. This allows random deployment in inaccessible terrains or disaster relief operations. On the other hand, this also means that sensor network protocols and algorithms must possess self-organizing capabilities. Since the amount of available energy is also limited, the sensor network architectures and applications, as well as deployment strategies, must be developed with low energy consumption as one of the important requirements. This motivates the need for energy efficient solutions to some of the problems such as routing, communication, deployment, medium access control and security. Sensor networks have many applications. Examples include environmental monitoring, habitat monitoring, seismic detection, military surveillance, inventory tracking and smart spaces.