Market-Based Adaptive Task Scheduling for Sensor Networks

This paper designs a microeconomic system for task scheduling in wireless sensor networks by virtue of the migration of mobile agents. Further, it proposes a market-based adaptive task scheduling algorithm, named MATS, which schedules tasks to the set of optimal nodes by adjusting the energy price of nodes and modifying the nodes' roles. The simulation in the scenario of target tracking demonstrates that MATS is adaptable to the changing network conditions and realizes an efficient allocation of energy resources by regulating the energy consumption of nodes. Compared with the static scheduling algorithm, MA TS impressively promotes the energy efficiency at the trivial cost of tracking accuracy