PALES: A Predictive Approach for the election of semantic cluster LEaders in wireless Sensor networks

Abstract

Recently, semantic clustering has been proposed to save energy in wireless sensor networks. Semantic clustering organizes the topology in clusters composed of semantically correlated nodes whose leader (collector) is periodically elected. Since collectors’ energy depletion is faster than the inner cluster nodes, suitable election mechanisms are required to avoid the energy hole problem. A potential drawback in adopting traditional election mechanisms is their reactive features since they usually wait for the leader nodes death to then elect a new leader. This behavior may cause holes in the network. The semantic clustering has presented better energy efficiency than other classical clustering methods. We propose PALES to reduce power consumption of Wireless Sensor and Actuator Network (WSAN) through a predictive election of semantic collectors using the ARIMA method. PALES extends an existing decentralized semantic clustering mechanism, inheriting its properties of self-adaptation, self-reconfiguration, and self-organization through a collaborative process. Results show that the PALES election increased up to 73% the collector battery saving in comparison to reactive election methods.