A robust uncertainty-aware cluster-based deployment approach for WSNs: Coverage, connectivity, and lifespan

Predicting the expected performance of Wireless Sensor Networks (WSNs) is the key to their successful deployment. This paper investigates the following fundamental problem: how to judiciously plan the physical and the logical topologies of a WSN so that performance demands including network connectivity, sensing coverage quality, reliability, and lifetime are all satisfied with the least possible cost. To handle the uncertainty related to sensor connectivity and coverage, we devise a probabilistic-based communication cost model, and we exploit the belief functions theory to define a generic evidence fusion scheme that captures several characteristics of real-world applications. The uncertainty-aware cluster-based WSNs deployment problem is formulated as a multi-objective binary nonlinear and non-convex optimization problem, and an efficient heuristic using genetic algorithms is investigated. Using both simulations and testbed-based experiments, we show that the proposed deployment approach can fulfill the user performance needs, which confirms that the deployment of real-world fusion-based WSNs with predictable performance is possible.