UAV for Wireless Power Transfer in IoT Networks: A GMDP approach

Unmanned aerial vehicles (UAVs) are a promising technology employed as moving aggregators and wireless power transmitters for IoT networks. In this paper, we consider an UAV-IoT wireless energy and data transmission system and the decision-making problem is investigated. We aim at optimizing the nodes’ utilities by defining a good packet delivery and energy transfer policy according to the system state. We formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues. As the MDP formalism achieves its limits when the neighbors’ interactions are considered, we formulate the problem as a Graph-based MDP (GMDP). We then propose a Mean-Field Approximation (MFA) algorithm to find a solution. The simulation results show that our framework achieves a good analysis of the system behavior.

[1]  Walid Saad,et al.  Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage , 2016, IEEE Communications Letters.

[2]  Geoffrey Ye Li,et al.  Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks , 2016, IEEE Communications Surveys & Tutorials.

[3]  Zhu Han,et al.  Wireless Networks With RF Energy Harvesting: A Contemporary Survey , 2014, IEEE Communications Surveys & Tutorials.

[4]  Daji Qiao,et al.  Prolonging Sensor Network Lifetime Through Wireless Charging , 2010, 2010 31st IEEE Real-Time Systems Symposium.

[5]  Abdellatif Kobbane,et al.  Mobile delay‐tolerant networks with energy‐harvesting and wireless energy transfer cooperation , 2018, Concurr. Comput. Pract. Exp..

[6]  Prasun Sinha,et al.  Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks , 2008, SenSys '08.

[7]  Jie Xu,et al.  UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization , 2017, IEEE Transactions on Wireless Communications.

[8]  Bill N. Schilit,et al.  Enabling the Internet of Things , 2015, Computer.

[9]  Miao Pan,et al.  Efficient data collection for wireless rechargeable sensor clusters in Harsh terrains using UAVs , 2014, 2014 IEEE Global Communications Conference.

[10]  Jesús Cid-Sueiro,et al.  An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions , 2015, IEEE Journal on Selected Areas in Communications.

[11]  Walid Saad,et al.  Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications , 2017, IEEE Transactions on Wireless Communications.

[12]  Jin Chen,et al.  Power Control in UAV-Supported Ultra Dense Networks: Communications, Caching, and Energy Transfer , 2017, IEEE Communications Magazine.

[13]  K. R. Venugopal,et al.  Searching for the IoT Resources: Fundamentals, Requirements, Comprehensive Review, and Future Directions , 2018, IEEE Communications Surveys & Tutorials.

[14]  Antonio Iera,et al.  Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications , 2016, Sensors.

[15]  Frank Y. Li,et al.  An On-Demand Energy Requesting Scheme for Wireless Energy Harvesting Powered IoT Networks , 2018, IEEE Internet of Things Journal.

[16]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[17]  Geoffrey Ye Li,et al.  An Overview of Sustainable Green 5G Networks , 2016, IEEE Wireless Communications.

[18]  Shahriar Mirabbasi,et al.  Wireless Energy Harvesting for Internet of Things , 2014 .