Self-Repairing Clusters for Time-E±cient and Scalable Actor-Fault-Tolerance in Wireless Sensorand Actor Networks
A new solution for fault-tolerance in wireless sensor and actor networks (WSAN) is proposed. The solution deals with fault-tolerance of actors, contrary to most of the literature that only considers sensors. It considers real-time communication, and ensures the execution of tasks with low latency despite fault occurrence. A simpli¯ed MAMS (multiple-actor multiple-sensor) model is used, where sensed events are duplicated only to a limited number of actors. This is di®erent from the basic MAMS model and semi-passive coordination (SPC), which use data dissemination to all actors for every event. Although it provides high level of fault-tolerance, this large dissemination is costly in terms of power consumption and communication overhead. The proposed solution relies on the construction of self-repairing clusters amongst actors, on which the simpli¯ed MAMS is applied. This clustering enables actors to rapidly replace one another whenever some actor breaks down, and eliminates the need of consensus protocol execution upon fault detection, as required by the current approaches to decide which actor should replace the faulty node, which. The extensive simulation study carried out with TOSSIM in di®erent scenarios shows that, the proposed protocol reduces the latency of replacing faulty actors compared to current protocols like SPC. The reduction of the overall delay for executing actions reaches 59%, with very close fault-tolerance (action execution success rate). The di®erence for this metric does not exceed 8% in the worst case. Scenarios of di®erent network sizes con¯rm the results and demonstrate the protocol's scalability.
wireless sensor and actuator network, Fault-tolerance, Clustering Algorithms