Resource Efficient Survivable Clustering for Wireless Sensor Networks

In this paper, we address both lifetime and survivability issues in wireless sensor networks (WSNs). We present an easy-to-implement clustering method called DED (distributed, energy-efficient, and dual-homed clustering) which promises robustness for WSNs without relying on the redundancy of sensors, i.e., without depending on node density. DED uses the information already gathered during clustering to determine additional alternate routes from sources to observers, thus incurring low message overhead. It does not make any assumptions about network dimension, node capacity, or location-awareness; and terminates in a constant number of iterations. The correctness of the algorithm is proved analytically. Simulation results demonstrate that our approach is effective both in prolonging the network lifetime and in providing survivability.