Energy-Efficient Joint Power Allocation and User Selection Algorithm for Data Transmission in Internet-of-Things Networks

• Published in: IEEE internet of things journal Vol. 7; no. 9; pp. 8736 - 8747
• Main Authors: Ansere, James Adu, Han, Guangjie, Bonsu, Kusi Ankrah, Peng, Yan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data communication; Data transmission; Downlink; Electronic devices; Energy consumption; Energy conversion efficiency; Energy efficiency; Energy efficiency maximization; Exploding wires; Internet of Things; Internet of Things (IoT); Iterative algorithms; Iterative methods; joint power allocation and user selection (JPAUS); Kuhn–Munkres (KM) algorithm; Mixed integer; Networks; Nonlinear programming; Optimization; Power management; Quality of service; Resource allocation; Resource management; Robustness (mathematics); Wireless communications
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2995387

The Internet-of-Things (IoT) system is a novel networking technology that connects smart communication devices through Internet-enabled infrastructure to enhance wireless communications. The explosive growth of IoT devices connectivity has increased energy consumption drastically that raises economic and physical environment concerns. To meet the challenges posed by high energy consumption, energy efficiency has become an urgent need for IoT networks recently. This article examines resource allocation and formulates the joint optimization problem for power allocation and user selection subject to the maximum transmit power and different Quality-of-Service (QoS) requirements, to achieve an improved energy efficiency performance in IoT networks under channel uncertainty. Furthermore, the formulated optimization problem is mixed-integer nonlinear programming (MINLP) with no practical solutions. Due to the nonconvexity and NP-hardness of the MINLP problem, the primal optimization problem is transformed into a convex problem and solved optimally for power allocation and user selection, by applying the Lagrangian dual decomposition method and the Kuhn-Munkres algorithm, respectively. An efficient joint iterative algorithm is proposed to maximize energy efficiency performance with guaranteed convergence within few numbers of iterations. The numerical results validate the robustness of the proposed algorithm and significantly show its superior performance as compared with the baseline algorithms.