Fuzzy Random Expected Value Model based Multi-objective Optimization Routing for Wireless Sensor Networks

To simultaneously optimize the delay, reliability and energy of a path in the special twotiered wireless sensor networks, a novel routing model based on fuzzy random expected value theory is proposed, in which fuzzy random multi-objective optimization theory is adopted, and fuzzy random variables are introduced to describe both fuzziness and randomness of delay, reliability of wireless links and residual energy of nodes. By using fuzzy random simulation for computing the expected values, we design a hybrid routing algorithm for solving the model based on genetic algorithm and Pareto optimal solution. Simulation results show that the proposed algorithm can find multiple optimal paths at a time, which can achieve a longer network lifetime comparing with the typical shortest path algorithm.