Research On Maximizing The Wireless Charging Utility In Internet-of-things

With the popularization and application of the Internet of Things(Io T),the dream decades ago of the intelligent life has moved closer to reality.Smart applications supported by Io T have entered people's daily lives from smart home to autonomous driving.During the past years,there has been an increase in the number of Io T devices and developing a sustainable energy supply in Io T becomes a major challenge.The emergence of wireless charging technology is expected to address the energy shortage in Io T.It can effectively charge Io T devices by deploying a mobile charger in the target network according to certain established rules.In practical applications,the energy carried by the charger is limited.So,reasonable charging planning needs to be carried out according to different goals.Existing research mainly focuses on the theoretical charging model,while ignoring the actual factors that affect the charging utility.In order to study the utility of wireless charging in practical scenarios,this thesis designs and implements a testbed for mobile wireless charging.Then Some experiments on the utility of charging were performed and the results will provide a basis for wireless charging scheduling.This thesis considers selecting sensor nodes according to the spatial characteristic in space phenomenon monitoring tasks.For the problem of maximizing wireless charging utility with routing constraint,this thesis proposes a greedy algorithm based on submodularity optimization that can achieve a near-optimal solution in polynomial time.Compared with the randomly selected algorithm,the results prove its effectiveness.