Market-based computational task assignment within autonomous wireless sensor networks

In recent years, improved wireless technologies have enabled the low-cost deployment of large numbers of sensors for a variety of applications across different engineering disciplines. Because of the computational resources (processing capability, storage capacity, etc.) distributed throughout these sensing networks, it is often possible to perform advanced data analysis tasks autonomously and in-network, eliminating the need for the post-processing of sensor data. With new parallel algorithms being developed for in-network computation, it has become necessary to create a framework in which the computational resources available throughout a wireless sensing network can be best utilized in the midst of competing computational requirements. In this study, a Pareto-optimal market-based method is developed in order to autonomously distribute various computational tasks with competing objectives and/or resource demands across available network resources. This method is experimentally validated on a network of wireless sensing prototypes.