Research On Charging Scheduling For Wireless Rechargeable Sensor Networks In Complex Environment

Wireless sensor network is a new type of information acquisition and processing technology because of its flexible deployment,easy expansion,high self-organization and other advantages,which has been widely used in forest fire detection,ecological environment monitoring,military fields,etcs.However,constrained by the small size of sensor boards,a sensor's battery is usually capacity-limited,non-replaceable,and unreusable.Thus,the limited energy capacity problem is widely regarded as the main bottleneck that impedes the widespread deployment of WSN.Fortunately,the recent breakthrough of wireless power transfer(WPT)technology makes it possible to solve the problem by wirelessly transferring energy between chargers and sensors,and this enables sensors to work perpetually.Supported by WPT technology,wireless rechargeable sensor network(WRSN)has become an important research topic.Most prior arts focus on system performance enhancement in the ideal environment that ignores the impacts of obstacles.This contradicts with practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission.This paper concentrates on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints.Firstly,after multiple experiments,it is found that the charging efficiency can even be improved when obstacles are in certain locations.Thus,Fresnel Zones theory is used to illustrate the enhanced effect.Then a new theoretical charging model is proposed based on Fresnel diffraction model.Next,in order to reduce the computation complexity,a spatial discretization method is proposed.This paper transforms the original problem into the maximization of a submodular function and proposes a cost-efficient algorithm to solve it.Lastly,test-bed experiments and simulations are conducted to verify the effectiveness and reliability of the algorithms.It is proved that the proposed algorithms outperform baseline algorithms by 14.8% on average.