Abstract:

An energysaving filtering mechanism (EFM) by mining the value redundant loads in networks according to distinctive valuable grade in applicationoriented wireless sensor network (WSN) is proposed. The reinforcement learning theory, which relies on online estimation of the data value, is adopted to  evaluate the data value online and drive the adaptive optimization decision on contention window in medium access control. Furthermore, on the premise of quality of service (QoS) in datagathering, the transmission of value redundancy loads can be effectively inhibited in networks to realize the energy-saving gathering mechanism based on mining the intension of value in transmission loads. Finally, the simulation results show that EFM can effectively reduce total energy cost in WSN via decreasing a large amount of redundant flow in network, enhance QoS of data gathering, and outperform some other classical data collection schemes in execution efficiency.