Programming Wireless Sensor Networks in a Self-Stabilizing Style

Wireless sensor networks operate in an unstable environment and thus are subject to arbitrary transient faults. Self-stabilization is a promising technique to add tolerance against transient faults in a self-contained non-masking way. A core factor for the applicability of a given self-stabilizing algorithm is its convergence time. This paper analyses the average stabilization time of three algorithms commonly regarded as central building blocks for wireless sensor networks. The analysis is accomplished with SelfWISE, a framework providing programming abstractions for self-stabilizing algorithms. The performed analysis considers the target models as well as network size and density. This demonstrates the usability of SelfWISE for evaluating self-stabilizing algorithms under a wide range of models.