Quantification of node misbehavior in wireless sensor networks: A social choice-based approach

Abstract

This work focuses on the quantification of node misbehavior in wireless sensor networks (WSNs). Misbehaving nodes are common within WSNs which are once detected, are penalized and in some cases eliminated from the network. However, node misbehavior might be relative i.e., a node may exhibit maliciousness or selfishness only to a specific set of nodes and may function normally for the rest. In these cases, a complete elimination of the node from the network is unfair. This work mitigates the aforesaid problem and mathematically evaluates the extent of misbehavior of a node through the proposed Metric of Misbehavior (MoM). Based on the Theory of Social Choice, the proposed algorithm considers the misbehaving nodes as the voting alternatives and the normally behaving nodes as the voters. Based on majority ranking of social choice, eventually MoM is obtained for every alternative in a fair manner.