Minimum-energy reprogramming with guaranteed quality-of-sensing in software-defined sensor networks

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies make it practical to realize software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. In SDSNs, wireless sensor nodes can be dynamically reprogrammed for different sensing tasks via the over-the-air-programming technique. For a given sensing task, it is usually required to guarantee certain quality-of-sensing, e.g., coverage ratio. Intuitively, the more sensors are deployed with a program, the higher quality-of-sensing of the corresponding task can be achieved. However, this is at the expense of high reprogramming energy consumption. In this paper, we investigate how to design an energy-efficient reprogramming strategy with guaranteed quality-of-sensing for a sensing task. To this end, two issues will be tackled: 1) the subset of sensors that shall be reprogrammed, i.e., reprogramming sensor selection and 2) the program distribution routing. They are jointly considered and formulated as an integer linear programming (ILP) problem, based on which an algorithm with low computation complexity is then proposed. The high efficiency of our algorithm is validated by extensive simulation studies.