  Edge computing has emerged as a prospective paradigm to meet ever-increasing
computation demands in Mobile Target Tracking Wireless Sensor Networks
(MTT-WSN). This paradigm can offload time-sensitive tasks to sink nodes to
improve computing efficiency. Nevertheless, it is difficult to execute dynamic
and critical tasks in the MTT-WSN network. Besides, the network cannot ensure
consecutive tracking due to the limited energy. To address the problems, this
paper proposes a new hierarchical target tracking structure based on Edge
Intelligence (EI) technology. The structure integrates the computing resource
of both mobile nodes and edge servers to provide efficient computation
capability for real-time target tracking. Based on the proposed structure, we
formulate an energy optimization model with the constrains of system execution
latency and trajectory prediction accuracy. Moreover, we propose a long-term
dynamic resource allocation algorithm to obtain the optimal resource allocation
solution for the ac- curate and consecutive tracking. Simulation results
demonstrate that our algorithm outperforms the deep Q-learning over 14.5% in
terms of system energy consumption. It can also obtain a significant
enhancement in tracking accuracy compared with the non-cooperative scheme.
