Dynamic Wireless Information and Power Transfer Scheme for Nano-Empowered Vehicular Networks

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 7; pp. 4088 - 4099
• Main Authors: Feng, Li, Ali, Amjad, Iqbal, Muddesar, Ali, Farman, Raza, Imran, Siddiqi, Muhammad Hameed, Shafiq, Muhammad, Hussain, Syed Asad
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Energy harvesting; energy-efficiency optimization; intelligent transportation systems; Mixed integer; nano-communication; Nano-empowered vehicular networks; Nanosensors; Nonlinear programming; Optimization; Power management; Power plants; Resource allocation; Resource management; Sensors; terahertz band; Terahertz frequencies; Vehicle dynamics; Vehicles; Wireless communication; wireless power transfer; Wireless power transmission; Wireless sensor networks
• DOI: 10.1109/TITS.2020.3020254
• EISSN: 1558-0016
• ISSN: 1524-9050

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.