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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. The authors 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, they aim at finding the paths (with length less than or equal to a given bound L) that could lead them from the initial state to the final state while having the least total cost.
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