Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter

Masood, Zaki; Musa, Ardiansyah; Choi, Yong-Hoon

doi:10.3390/s21237857

Cited by 14 publications

(11 citation statements)

References 53 publications

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“…Different technologies can be implemented and executed to measure energy parameters. Smart metering technologies [1][2][3][4][5][6][7][8] are suitable for power quality check, measurements of active and reactive power, optimization of grid control, and power consumption monitoring. Supervisory Control And Data Acquisition (SCADA) systems [9,10] are able to integrate measurement systems by controlling parameters in real time.…”

Section: Sensor Technologies and Energy Metering Systemsmentioning

confidence: 99%

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Massaro¹,

Starace²

2022

Sensors

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Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

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Section: Sensor Technologies and Energy Metering Systemsmentioning

confidence: 99%

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Massaro¹,

Starace²

2022

Sensors

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“…Even given that, realizing 5G or B5G IoT networks is still challenging due to the limited energies for the IoT devices equipped with batteries. To alleviate this problem, simultaneous wireless information and power transfer (SWIPT) are proposed to effectively and conveniently extend the lifetime of IoT devices, and employed in many related works [ 3 , 4 , 5 , 6 , 7 ]. In fact, SWIPT is a key technique in 5G and B5G because power allocation and interference management are still the crucial issues to be addressed in the communication networks [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory

Liu

Lin

et al. 2022

Sensors

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Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.

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“…The Resource Management (RM) strategy was proposed using time switching and EH to achieve optimal performance with minimized computation time depending on the number of devices connected to the IoT [7]. In another study, the authors focused on transmit power and EH of Radio Frequencies (RF) using time slot interchange to maximize Energy Efficiency (EE) and improve the battery life of IoT devices [8]- [10]. Harvesting time achieves near-optimal EE and reduces Computational Complexity (CC) based on the optimization of RM.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Resource Management Using Multiagent Double Deep Q-Networks to Guarantee Strict Reliability and Low Latency in IoT Network

Salh

Ngah

Hussain

et al. 2022

IEEE Open J. Commun. Soc.

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With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation applications and apply stringent high reliability and low latency requirements, closely related to strict delay demands. These requirements support massive network connectivity for multiple Internet of Things devices. Hence, in this paper, we optimize energy efficiency and achieve quality-of-service requirements by mitigating co-channel interference, performing efficient power control of transmitters, and harvesting energy using timeslot exchanges. Due to a nonconvex optimization problem, we propose an iterative algorithm for power allocation and time slot interchange to reduce the computational complexity. To achieve a high degree of ultra-reliability and low latency with quality-of-service-aware instantaneous reward under massive connectivity, we efficiently employ multiagent reinforcement learning by addressing the intelligent resource management problem via a novel Double Deep Q Network. The network prioritizes experience replay to exploit the best policy and maximize accumulative rewards. It also learns the optimal policy and enhances learning efficiency by maximizing its reward function to make decisions with high intelligence and guarantee strict ultra-reliability and low latency. The simulation result shows that the Double Deep Q Network with prioritized experience replay can guarantee stringent ultra-reliability and low latency. As a result, the cochannel interference between transmission links and the high-power consumption density associated with the massive connectivity of the Internet of Things devices are mitigated.INDEX TERMS Internet of things, beyond fifth-generation, energy efficiency, massive connectivity.

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Energy-Efficient Optimal Power Allocation for SWIPT Based IoT-Enabled Smart Meter

Cited by 14 publications

References 53 publications

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory

Intelligent Resource Management Using Multiagent Double Deep Q-Networks to Guarantee Strict Reliability and Low Latency in IoT Network

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