Joint task allocation approaches for hierarchical wireless sensor networks

In current wireless sensor network (WSN) applications, especially in Internet of Things or in-network processing scenarios, the WSN nodes are required not only to execute local processing individually but also to collaborate for completing the periodical global tasks. Due to the complexity of joint task allocation problem, most of the existing approaches are restricted to distribute either only the local or global tasks. This work overcomes this limitation. It firstly proposes a static joint task allocation algorithm (SJTA) based on binary integer linear programming. Secondly, this work further presents a dynamic joint task allocation algorithm (DJTA) using linear programming, in order to achieve more balanced task distribution. Simulation results show that DJTA and SJTA significantly outperform previous approaches. They extend the network lifetime by up to 10 times longer than no scheduling strategy.