Randomized Self-stabilizing Algorithms for Wireless Sensor Networks

Wireless sensor networks (WSNs) pose challenges not pre- sent in classical distributed systems: resource limitations, high failure rates, and ad hoc deployment. The lossy nature of wireless communication can lead to situations, where nodes lose synchrony and programs reach arbitrary states. Traditional approaches to fault tolerance like replication or global resets are not feasible. In this work, the concept of self-stabilization is applied to WSNs. The majority of self-stabilizing algorithms found in the literature is based on models not suitable for WSNs: shared memory model, central daemon scheduler, unique processor identifiers, and atomicity. This paper proposes problem-independent transformations for algorithms that stabilize under the central daemon scheduler such that they meet the demands of a WSN. The transformed algorithms use randomization and are probabilistically self-stabilizing. This work allows to utilize many known self-stabilizing algorithms in WSNs. The proposed transformations are evaluated using simulations and a real WSN.