RF Energy Harvesting and Information Transfer Implementation in The Internet of Things



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With the growing number of connected devices (e.g., smartphones, sensors, actuators and cameras) in the fifth generation (5G) of mobile technology, the massive Internet of Things (IoT) is expected to address a wide range of characteristics and demands, with more radio frequency (RF) bands to support multiple frequency transmission by 2020. Such pervasive and dense RF signals are now opening a new field that aims to provide a reliable power supply to energy-constrained devices and to increase portable device lifetime in 5G networks, commonly referred to as RF energy harvesting. RF energy harvesting is the process by which radiative electro-magnetic waves are captured, converted, stored, and used to operate usually low-energy consumption devices ranging from wearable electronics to sensor networks. This dissertation intends to identify novel architectures and algorithms to integrate RF energy harvesting and data transfer in IoT devices of the 5G networks. It is of two-fold. Firstly, the thesis demonstrates the feasibility to supply endless and uninterrupted energy to low-power wireless sensor networks - whether they are from a dedicated source or not - by designing real hardware prototypes and conducting experiments. Secondly, it proposes several mathematical optimization algorithms for energy and data transfer in general IoT instruments under different scenarios. Some of the algorithms put forth are designed to carefully select the device, while others enable devices to cognitively select a wireless channel from within the 5G spectrum to harvest energy and transfer reliable and uninterrupted data to an access point. The main contributions of the dissertation are summarized as follows.

  • A summary of the basic concepts, classifications, and state-of-art hardware designs using wireless energy harvesting. Then, the underlying reasons to select RF energy harvesting as a method to provide power and extend the lifetime of wireless sensor networks.

  • The development of RF energy harvesting systems for wireless sensor networks using off-the shelf hardware kits customized for this purpose. Demonstrations and experimental results are also presented.

  • The design of algorithms that aim to maximize energy harvesting and information transfer, using mathematical optimization frameworks such as dynamic programming and reinforcement learning. Numerical results are shown to prove the optimality of these algorithms, assuming different scenarios.

Driven by technology developments and socio-economic transformations, it is expected that with 5G, instant information will be just a touch away, and that everything will be connected. The dissertation ends by discussing the opportunities and challenges in the research, design, and engineering of energy harvesting solutions for 5G networks.



Internet of Things, RF energy harvesting, Wireless energy transfer, Wireless sensor networks, Dynamic programming, Markov decision process, Reinforcement learning


Portions of this document appear in: Sangare, Fahira, Yong Xiao, Dusit Niyato, and Zhu Han. "Mobile charging in wireless-powered sensor networks: Optimal scheduling and experimental implementation." IEEE Transactions on Vehicular Technology 66, no. 8 (2017): 7400-7410. And in: Sangare, Fahira, Ali Arab, Miao Pan, Lijun Qian, Suresh K. Khator, and Zhu Han. "RF energy harvesting for WSNs via dynamic control of unmanned vehicle charging." In 2015 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1291-1296. IEEE, 2015. And in: Sangare, Fahira, Duy HN Nguyen, Yong Xiao, and Zhu Han. "Opportunistic sensing for joint energy harvesting and channel access." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 560-564. IEEE, 2016. And in: Sangare, Fahira, and Zhu Han. "Joint optimization of cognitive RF energy harvesting and channel access using Markovian multi-armed bandit problem." In 2017 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 487-492. IEEE, 2017. And in: Sangare, Fahira, and Zhu Han. "RF Energy Harvesting Networks: Existing Techniques and Hardware Technology." In Wireless Information and Power Transfer: A New Paradigm for Green Communications, pp. 189-239. Springer, Cham, 2018.