Ensuring Energy Efficiency When Dynamically Assigning Tasks in Virtualized Wireless Sensor Networks

Traditional non-virtualized Wireless Sensor Networks (WSNs) suffer from high deployment and maintenance costs, mainly because their applications are embedded in sensor nodes. Virtualization technologies address these challenges by allowing multiple sensing tasks to run over the same deployed WSN infrastructure. However, virtualization comes at an energy-delay cost, making it both essential and challenging to allocate physical and/or virtual resources efficiently to applications with different sensing tasks, especially for delay-sensitive applications. Our goal is to address the challenge of task assignment in virtualized WSNs while minimizing the overall energy consumption and meeting the given deadlines. After formulating the problem as an Integer Linear Programming (ILP), we propose a scalable heuristic. We evaluate the performance of our proposed heuristic in different scenarios and compare it with the optimal solution as well as a recent work from literature. The results indicate that our proposed heuristic leads close-to-optimal solutions with good performance in terms of execution time. It shows that the proposed DTA solution can not only achieve up to a 97% reduction of the execution time for small-scale scenarios, as compared to the optimal solution, but it also outperforms the existing benchmarks in terms of successful task execution rate by 100%.