Bounded Length Least-Cost Path Estimation in Wireless Sensor Networks Using Petri Nets

This paper proposes an iterative algorithm for estimating the least-cost paths in a wireless sensor network (WSN) that is modeled by a Petri net. We assume that each link between two sensor nodes (each transition in the net) is associated with a positive cost to capture its likelihood (e.g., in terms of the transmission time, distance between nodes, or the link capacity). Given the structure of the Petri net and its initial/final state, we aim at finding the paths (with length less than or equal to a given bound L) that could lead us from the initial state to the final state while having the least total cost. We develop an iterative algorithm that obtains the least-cost paths with complexity that is polynomial in the length L.