A Genetic Programming Approach to Cost-Sensitive Control in Wireless Sensor Networks

Abstract

In some wireless sensor network applications, multiple sensors can be used to measure the same variable, while differing in their sampling cost, for example in their power requirements. This raises the problem of automatically controlling heterogeneous sensor suites in wireless sensor network applications, in a manner that balances cost and accuracy of sensors. We apply genetic programming (GP) to this problem, considering two basic approaches. First, we construct a hierarchy of models, where increasing levels in the hierarchy use sensors of increasing cost. If a model that polls low cost sensors exhibits too much prediction uncertainty, the burden of prediction is automatically transferred to a higher level model using more expensive sensors. Second, we train models with cost as an optimization objective, called non-hierarchical models, that use conditionals to automatically select sensors based on both cost and accuracy. We compare these approaches in a setting where the available budget for sampling is considered to remain constant, and in a setting where the system is sensitive to a fluctuating budget, for example available battery power. We show that in both settings, for increasingly challenging datasets, hierarchical models makes predictions with equivalent accuracy yet lower cost than non-hierarchical models.