Fast Convergence in Stochastic Routing for Wireless Sensor Networks: An Analytical Perspective
Improving convergence rate is one of the major challenges in stochastic routing for wireless sensor networks. Randomized approach of node selection results in a higher convergence time which is not desirable for delay-sensitive applications. The authors proposed a novel routing algorithm that performs better than the existing methods in terms of the convergence rate. In this paper, they analyze the basic concept behind their proposed routing algorithm, improving the rate of routing out of the neighborhood for any given node, which will improve the convergence rate. Furthermore, they derive bounds for transition probabilities which will improve the convergence rate with reference to the random walking strategy and formulate a closed-form expression for optimal transition probability selection.