Abstract : The use of wireless sensor networks (WSNs) has increased rapidly over the last years. Due to their low resources, sensors come along with new issues regarding network security and energy consumption. Focusing on the network availability, previous studies proposed to protect clustered network against denial of service attacks with the use of traffic monitoring agents on some nodes. Those control nodes have to analyze the traffic inside a cluster and to send warnings to the cluster-head whenever an abnormal behavior (e.g., high packets throughput, or non-retransmission of packets) is detected. But if the control nodes (cNodes) die out of exhaustion, they leave the network unprotected. To better fight against attacks, we try to enhance this solution by renewing periodically the election process. Furthermore, we propose three energy-aware and secure methods to designate the cNodes in a hierarchically clustered WSN. The first one is a simple self-election process where nodes randomly designate themselves. It leads to a better load balancing than a static method (i.e., with no renewal), but we argue that we can obtain better results by considering the remaining energy of the nodes at cNodes selection time. Hence the second algorithm is purely based on the residual energy of the sensors. We discuss limitations of this deterministic process concerning security and cluster coverage, and suggest workarounds. These improvements lead us to the third mechanism. It is based on residual energy too, but it includes a democratic election process in which nodes in the cluster vote to optimize the cNode role attribution. Results obtained from simulation experiments with the ns-2 tool are provided to analyze the energy repartition in the network and to compare the three selection algorithms. All experimental outcomes show improvements of the load balancing in the network, while maintaining good detection coverage, in regard to static selection. Furthermore, the analysis of the respective performances of the three mechanisms is used as a basis to establish recommendations regarding the use cases of those methods.
