{"title":"面向可持续智慧城市的新型负载驱动位置和功耗感知eo IoT-WSN聚类和路由协议","authors":"Khalid A. Darabkh;Muna Al-Akhras","doi":"10.1109/JIOT.2025.3574636","DOIUrl":null,"url":null,"abstract":"The Internet of Things (IoT) has impacted sectors like smart cities and healthcare by enabling real-time data collection through wireless sensor networks (WSNs). However, challenges, such as limited battery life, connectivity disruptions, and the separate management of clustering and routing, hinder performance and energy efficiency. Existing cluster-based IoT-WSN routing protocols often suffer from issues, such as random or nonenergy-aware cluster head (CH) selection, imbalanced energy consumption, imperfect load balancing, and a lack of integration between clustering and routing. These shortcomings reduce overall network efficiency. To address these challenges, we propose the load-driven, location- and power-aware equilibrium optimizer (EO)-based clustering and routing protocol (LDLP-EO-CRP), which integrates clustering and routing to optimize CH selection and balance energy consumption. LDLP-EO-CRP is designed for sustainable smart city applications, including smart waste management, energy-efficient street lighting, air quality monitoring, and intelligent traffic management. By enabling IoT systems to operate reliably over extended periods, LDLP-EO-CRP supports continuous, real-time data collection and monitoring, contributing to citywide sustainability goals and enhancing urban resilience. The proposed protocol divides the sensing area into hexagonal clusters, adapting to changes in network size, node distribution, and density. Each cluster is managed by a CH selected using the EO algorithm, which considers key factors through its fitness function, namely, the remaining energy of sensor nodes, their distances from the sink, loads, and proximities to neighboring nodes. LDLP-EO-CRP incorporates a novel relay-based data forwarding technique, where each CH transfers data hop-by-hop to the sink, based on the loads and proximities of candidate relay nodes. Simulation results demonstrate that LDLP-EO-CRP outperforms existing protocols, extending network lifespan, increasing throughput, reducing delay, and optimizing energy usage.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 15","pages":"31945-31961"},"PeriodicalIF":8.9000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Load-Driven Location- and Power-Aware EO-Based IoT-WSN Clustering and Routing Protocol for Sustainable Smart Cities\",\"authors\":\"Khalid A. Darabkh;Muna Al-Akhras\",\"doi\":\"10.1109/JIOT.2025.3574636\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Internet of Things (IoT) has impacted sectors like smart cities and healthcare by enabling real-time data collection through wireless sensor networks (WSNs). However, challenges, such as limited battery life, connectivity disruptions, and the separate management of clustering and routing, hinder performance and energy efficiency. Existing cluster-based IoT-WSN routing protocols often suffer from issues, such as random or nonenergy-aware cluster head (CH) selection, imbalanced energy consumption, imperfect load balancing, and a lack of integration between clustering and routing. These shortcomings reduce overall network efficiency. To address these challenges, we propose the load-driven, location- and power-aware equilibrium optimizer (EO)-based clustering and routing protocol (LDLP-EO-CRP), which integrates clustering and routing to optimize CH selection and balance energy consumption. LDLP-EO-CRP is designed for sustainable smart city applications, including smart waste management, energy-efficient street lighting, air quality monitoring, and intelligent traffic management. By enabling IoT systems to operate reliably over extended periods, LDLP-EO-CRP supports continuous, real-time data collection and monitoring, contributing to citywide sustainability goals and enhancing urban resilience. The proposed protocol divides the sensing area into hexagonal clusters, adapting to changes in network size, node distribution, and density. Each cluster is managed by a CH selected using the EO algorithm, which considers key factors through its fitness function, namely, the remaining energy of sensor nodes, their distances from the sink, loads, and proximities to neighboring nodes. LDLP-EO-CRP incorporates a novel relay-based data forwarding technique, where each CH transfers data hop-by-hop to the sink, based on the loads and proximities of candidate relay nodes. Simulation results demonstrate that LDLP-EO-CRP outperforms existing protocols, extending network lifespan, increasing throughput, reducing delay, and optimizing energy usage.\",\"PeriodicalId\":54347,\"journal\":{\"name\":\"IEEE Internet of Things Journal\",\"volume\":\"12 15\",\"pages\":\"31945-31961\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Internet of Things Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11016709/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11016709/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
A Novel Load-Driven Location- and Power-Aware EO-Based IoT-WSN Clustering and Routing Protocol for Sustainable Smart Cities
The Internet of Things (IoT) has impacted sectors like smart cities and healthcare by enabling real-time data collection through wireless sensor networks (WSNs). However, challenges, such as limited battery life, connectivity disruptions, and the separate management of clustering and routing, hinder performance and energy efficiency. Existing cluster-based IoT-WSN routing protocols often suffer from issues, such as random or nonenergy-aware cluster head (CH) selection, imbalanced energy consumption, imperfect load balancing, and a lack of integration between clustering and routing. These shortcomings reduce overall network efficiency. To address these challenges, we propose the load-driven, location- and power-aware equilibrium optimizer (EO)-based clustering and routing protocol (LDLP-EO-CRP), which integrates clustering and routing to optimize CH selection and balance energy consumption. LDLP-EO-CRP is designed for sustainable smart city applications, including smart waste management, energy-efficient street lighting, air quality monitoring, and intelligent traffic management. By enabling IoT systems to operate reliably over extended periods, LDLP-EO-CRP supports continuous, real-time data collection and monitoring, contributing to citywide sustainability goals and enhancing urban resilience. The proposed protocol divides the sensing area into hexagonal clusters, adapting to changes in network size, node distribution, and density. Each cluster is managed by a CH selected using the EO algorithm, which considers key factors through its fitness function, namely, the remaining energy of sensor nodes, their distances from the sink, loads, and proximities to neighboring nodes. LDLP-EO-CRP incorporates a novel relay-based data forwarding technique, where each CH transfers data hop-by-hop to the sink, based on the loads and proximities of candidate relay nodes. Simulation results demonstrate that LDLP-EO-CRP outperforms existing protocols, extending network lifespan, increasing throughput, reducing delay, and optimizing energy usage.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.