Generating Optimal Scheduling for Wireless Sensor Networks by Using Optimization Modulo Theories Solvers

Wireless Sensor Networks (WSNs) serve as the basis for today’s Internet of Things applications. A WSN consists of a number of spatially distributed sensor nodes, which cooperatively monitor physical or environmental conditions. In order to ensure the dependability of WSN functionalities, several reliability and security requirements have to be fulfilled. By applying a Satisfiability Modulo Theories (SMT) formalization for WSNs, a sleep/wake-up scheduling that respects those requirements can be generated by using SMT solvers. It is also important to prolong the lifetime of a WSN as much as possible. The recent success of Optimization Modulo Theories (OMT) approaches makes us able to generate for a WSN a sleep/wake-up scheduling that is optimal in terms of energy efficiency. This makes this kind of WSN optimization an excellent application for OMT solvers. To the best of our knowledge, no scientific work has ever reported any usage of OMT solvers for WSN optimization. In this paper, we introduce an OMT formalization of the aforementioned optimization problem for WSNs, provide a single-hop WSN simulation environment with one of the most common wireless sensor node types, propose several OMT benchmarks extracted from the WSN simulation, and report experiments with three different OMT solvers: Z3, OptiMathSAT, and Symba.