WIRELESS SYSTEMS WITH ENERGY HARVESTING CAPABILITIES: SENSING AND COMMUNICATIONS TRADE-OFFS
Energy harvesting (EH) solutions offer a promising framework for future wireless sensing systems. Instead of completely relying on a fixed battery or power from the grid, nodes with EH capabilities can collect energy from the environment, such as solar power or power from mechanical vibrations. In addition to enabling energy autonomous sensing systems, EH capabilities also offer prolonged network life-times and enhanced mobility for the nodes in the network.
One of the key issues in the design of EH systems is the intermittent nature of the energy supply. In a traditional device, the energy that can be used for any sensor task whether it is sensing or communications has either a fixed known value for each task or there is a total energy constraint. In contrast, for an EH node, the energy available for each task depends on the energy used in previous transmissions and energy that may be available in the future.
Our approach: We adopt a cross-layer approach where the sensing and communications tasks for a sensing system with EH capabilities are optimized jointly.
My EU Marie Skłodowska-Curie Project: "GRENHAS: Green and Smart Communications with Energy Harvesting: A Signal Processing Approach".
Publications:
A. Özçelikkale, T. McKelvey, and M. Viberg, “Remote Estimation of Correlated Sources under Energy Harvesting Constraints”, IEEE Transactions on Wireless Communications, vol. 17, pp. 5300–5313, Aug. 2018
A. Özçelikkale, T. McKelvey, and M. Viberg, ``Transmission strategies for remote estimation with an Energy Harvesting Sensor'', IEEE Transactions on Wireless Communications, vol. 16, no. 7, pp. 4390-4403, July 2017
R. Du, A. Özçelikkale, C. Fischione, and M. Xiao, ``Towards Immortal Wireless Sensor Networks by Optimal Energy Beamforming and Data Routing”, IEEE Transactions on Wireless Communications, vol. 17, pp. 5338–5352, Aug. 2018.
A. Özçelikkale, M.Koseoglu, M. Srivastava and Anders Ahlén, "Deep reinforcement learning based energy beamforming for powering sensor networks", IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2019 (See also here)
A. Özçelikkale, M.Koseoglu,and M. Srivastava, “Optimization vs. reinforcement learning for wirelessly powered sensor networks", IEEE International Workshop on Signal Processing Advances in Wireless Communications, (SPAWC) June 2018. (Invited Paper)
A. Özçelikkale, T. McKelvey, and M. Viberg, ``Performance Bounds for Remote Estimation with an Energy Harvesting Sensor'', Proc. IEEE Int. Symp. Information Theory (ISIT), pp. 460-464, July 2016.
A. Özçelikkale, T. McKelvey, and M. Viberg, ``Transmission strategies for remote estimation under energy harvesting constraints'', Proc. European Conference on Signal Processing (EUSIPCO), pp. 572-576, Aug. 2016.
R. Du, A. Özçelikkale, C. Fischione, and M. Xiao, “Optimal Energy Beamforming and Data Routing for Immortal Wireless Sensor Networks", 2017 IEEE International Conference on Communications (ICC), 2017.