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Smart city article details
| Title | Next-Gen Wsn Enabled Iot For Consumer Electronics In Smart City: Elevating Quality Of Service Through Reinforcement Learning-Enhanced Multi-Objective Strategies |
|---|---|
| ID_Doc | 39227 |
| Authors | Pratap Singh S.; Kumar N.; Saleh Alghamdi N.; Dhiman G.; Viriyasitavat W.; Sapsomboon A. |
| Year | 2024 |
| Published | IEEE Transactions on Consumer Electronics, 70, 4 |
| DOI | http://dx.doi.org/10.1109/TCE.2024.3446988 |
| Abstract | The data transfer volume is massive in next-generation Wireless Sensor Networks (6G-enabled WSNs) in smart city with consumer electronics-based high communication density, especially for multimedia data. Deploying multiple IoT nodes on such networks makes the process complex and challenging. In such cases, quality of Service (QoS) is critical as it ensures critical network performance and leverages improved end-user experience. There have been some existing heuristic/meta-heuristic works to address the QoS in next-generation WSNs; however, they are sensitive to their parametric values due to a lack of expert knowledge. Some are less robust and less adaptable in dynamic networks due to poorer balanced exploration of the solution space, exploitation of known semi-optimal/optimal solutions, and inefficient resource utilization in constrained environments such as edge devices. The suggested consumer electronics-based research presents an innovative solution, 'RL-MODE,' which incorporates Reinforcement Learning-Enhanced Multiobjective Optimisation Algorithms to address QoS management difficulties in edge-enabled WSN-IoT systems. The proposed methodology optimises competing objectives simultaneously, such as minimising energy use and latency while maximizing throughput and coverage, all while keeping the resource-constrained nature of edge devices in mind. The proposed RL-MODE Algorithm comprises Multiobjective Differential Evolution (MODE) Algorithm and a new Reinforcement Learning (RL) adaption technique to develop Pareto-optimal solutions by analysing the complicated linkages between input parameters, edge resources, and QoS parameters. Simulations and experiments with Next-Gen WSN-IoT applications show the effectiveness of the proposed method. This not only improves QoS in WSN-IoT applications, but it also increases resource utilisation and scalability in edge computing settings. © 2024 IEEE. |
| Author Keywords | Consumer electronics; maximized throughput; multi-objective differential evolution; Next-Gen WSN-IoT; quality of service; reduced energy usage; reduced latency; reinforcement learning; smart city |
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