Sustainable Computing-Informatics & Systems最新文献_第4页

LESP:A fault-aware internet of things service placement in fog computing LESP：雾计算中的故障感知物联网服务布局

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-02-13 DOI: 10.1016/j.suscom.2025.101097

Hemant Kumar Apat , Bibhudatta Sahoo

{"title":"LESP:A fault-aware internet of things service placement in fog computing","authors":"Hemant Kumar Apat , Bibhudatta Sahoo","doi":"10.1016/j.suscom.2025.101097","DOIUrl":"10.1016/j.suscom.2025.101097","url":null,"abstract":"<div><div>The rapid advancement of 5G networks enables increase adoption of Industrial Internet of Things (IIoT) devices which introduces variety of time-sensitive applications requires low-latency, fault-tolerant, and energy-efficient computing environments. Fog computing infrastructure that extends cloud computing capabilities at the network edge to provide computation, communication, and storage resources. Due to the limited computing capacity of the Fog node, it restricts the number of tasks executed. The other key challenges are the risk of hardware and software failure during task execution. These failures tend to disrupt the configuration of fog computing nodes, affecting the reliability and availability of services. As a result, this can negatively impact the overall performance and service level objectives. The fault-tolerant-based IoT service placement problem in the fog computing environment primarily focuses on optimal placement of IoT services on fog and cloud resources with the objective of maximizing fault tolerance while satisfying network and storage capacity constraints. In this study, we compared different community-based techniques Girvan-Newman and Louvain with Integer Linear Programming (ILP) for fault tolerance in fog computing using the Albert-Barabási network model. In addition, it proposed a novel Louvian based on eigenvector centrality service placement (LESP) to improve conventional Louvian methods. The proposed algorithm is simulated in iFogSim2 simulator under three different scenario such as under 100, 200 and 300 nodes. The simulation results exemplify that LESP improves fault tolerance and energy efficiency, with an average improvement of approximately 20% over Girvan-Newman, 25% over ILP, and 12.33% over Louvain. These improvements underscore LESP’s strong efficiency and capability in improving service availability across a wide range of network configurations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101097"},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain integrated multi-objective optimization for energy efficient and secure routing in dynamic wireless sensor networks 区块链集成多目标优化的动态无线传感器网络节能和安全路由

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-02-12 DOI: 10.1016/j.suscom.2025.101101

Vidhya Sachithanandam , D. Jessintha , Hariharan Subramani , V. Saipriya

{"title":"Blockchain integrated multi-objective optimization for energy efficient and secure routing in dynamic wireless sensor networks","authors":"Vidhya Sachithanandam , D. Jessintha , Hariharan Subramani , V. Saipriya","doi":"10.1016/j.suscom.2025.101101","DOIUrl":"10.1016/j.suscom.2025.101101","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) form the backbone of many key use cases, from environmental monitoring to healthcare to smart cities. But their use case is limited in terms of energy, latency, scalability, and security. To combat such problems, the paper suggests a new algorithm, the Energy-based Multi-Objective Donkey Smuggler Optimization Algorithm (EM-DSOA). This approach combines multi-aspect optimization and a thin blockchain protocol, making it a one-stop shop to optimize WSN’s efficiency, security, and stability. EM-DSOA as proposed optimizes energy utilization with dynamic clustering and adaptive routing with safe data transfer via blockchain integration. The approach is compared against current best practices like Multi Weight Chicken Swarm Based Genetic Algorithm (MWCSG) and Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization (AHCS-GWO) by simulation examples of different network densities. The results are marked by significant improvement with energy efficiency of 99.13 %, packet loss reduction of 91 percent and throughput increase of 1000 %. The model likewise has very low end-to-end latency, which is perfect for real-time workloads. The study points out that EM-DSOA can be scalable and flexible, with a high performance across diverse and changing scenarios. With an eye towards energy efficiency, low latency and secure communications in the one, the proposed model takes WSN optimization to a new level of knowledge. This is a work that’s not only up to the challenge of technology now but it also serves as a solid basis for future IoT and smart city deployments and will provide long-term, secure networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101101"},"PeriodicalIF":3.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel sustainable green transportation: A neutrosophic multi-objective model considering various factors in logistics 新颖的可持续绿色运输：考虑物流中各种因素的中性多目标模型

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-02-08 DOI: 10.1016/j.suscom.2025.101096

Kalaivani Kaspar, Palanivel K.

{"title":"Novel sustainable green transportation: A neutrosophic multi-objective model considering various factors in logistics","authors":"Kalaivani Kaspar, Palanivel K.","doi":"10.1016/j.suscom.2025.101096","DOIUrl":"10.1016/j.suscom.2025.101096","url":null,"abstract":"<div><div>Growing environmental concerns are driving the logistics operations in industry towards sustainable practices, known as green logistics. Optimizing transportation for solid goods are facing challenges to handle complex issues, though traditional methods are often focusing only on single objective like minimizing cost or maximizing the profit. However, to overcome all the possible challenges based on recent requirements, the multi-objective solid transportation problems (MOSTPs) will handle effectively by considering environmental factors like carbon emissions alongside cost and travel time. This research study contributes to the development of robust and eco-friendly transportation solutions by providing a framework for handling uncertainties in MOSTPs. Further, the model influenced in the neutrosophic set (NS) theory, which is an emerging tool to address inherent uncertainties in real-world data associated with environmental impacts and resource limitations. The NS theory incorporates truth-membership, indeterminacy, and falsity-membership functions, allowing for effective modeling of ambiguity. This model presents a Multi-Objective Fixed Charge Solid Transportation Problem (MOFCSTP) using a bi-polar single-valued neutrosophic set to handle all these uncertainties related to green sustainable transportation. Further, different approaches for achieving optimal solutions are explored, including Neutrosophic Compromise Programming Approach (NCPA), M-Pareto Optimal Solution Approach (M-POSA), Weighted Sum Method (WSM), Neutrosophic Goal Programming (NGP), Neutrosophic Global Criterion Method (NGCM), and Fuzzy Goal Programming (FGP). Lastly, the obtained results are then discussed and compared with sensitivity analysis, which is conducted to evaluate the strengths and limitations of each method to justify the effectiveness of the model.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101096"},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning at the edge in Industrial Internet of Things: A review 工业物联网边缘的联邦学习：综述

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-02-07 DOI: 10.1016/j.suscom.2025.101087

Dinesh kumar sah, Maryam Vahabi, Hossein Fotouhi

{"title":"Federated learning at the edge in Industrial Internet of Things: A review","authors":"Dinesh kumar sah, Maryam Vahabi, Hossein Fotouhi","doi":"10.1016/j.suscom.2025.101087","DOIUrl":"10.1016/j.suscom.2025.101087","url":null,"abstract":"<div><div>The convergence of Federated learning (FL) and Edge computing (EC) has emerged as an essential paradigm, particularly within the Industrial Internet of Things (IIoT) to enable the intelligent decision making. This work diligently examines the current state-of-the-art research at the intersection of FL, EC, and IIoT. An extensive review of the literature explores the diverse applications and challenges associated with this integration. The challenges range from privacy preservation and communication overhead to resource allocation. The incorporation of edge devices at which ensuring the federated learning in distributed manner helps to minimize energy consumption in IIoT, ultimately leads to a sustainable computing environment. By exploring the existing literature and research advancements, our goal is to highlight existing Edge-IoT software and hardware platforms and assess their usability in addressing challenges. In addition, we review existing recent frameworks, methodologies, and models employed to address these challenges, focusing on key performance matrices and its domain such as application, networking, and learning. We highlight the achievements and potential of FL and EC and underscore the need for tailored solutions to suit the unique demands of IIoT. Furthermore, we identify some of the major challenges as opportunities for future research, requires interdisciplinary collaboration and innovative algorithmic solutions. This work can help navigate through the challenges and unlock the full potential, contributing to the advancement of future IIoT applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101087"},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing economic and environmental performance of energy communities: A multi-objective optimization approach with mountain gazelle optimizer 提高能源社区的经济和环境绩效：利用山瞪羚优化器的多目标优化方法

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-02-06 DOI: 10.1016/j.suscom.2025.101098

Hong Zheng , Zhixin Wu

{"title":"Enhancing economic and environmental performance of energy communities: A multi-objective optimization approach with mountain gazelle optimizer","authors":"Hong Zheng , Zhixin Wu","doi":"10.1016/j.suscom.2025.101098","DOIUrl":"10.1016/j.suscom.2025.101098","url":null,"abstract":"<div><div>This research explores three distinct configurations of energy communities, collectives of local consumers utilizing renewable electrical and thermal energy. The study aims to enhance economic outcomes while addressing climate change and meeting energy demands through advanced strategies. The optimization framework focuses on refining the design, capacity, and efficiency of energy conversion and storage systems, balancing investment and operational costs with greenhouse gas emissions (GhGE) across their lifecycle. Two innovative demand-side management (DSM) strategies are introduced: a downstream pricing-based demand response program (DRP) and an upstream DSM model aligning electricity demand with locally available renewable energy. The study employs a multi-objective modeling approach using the novel mountain gazelle optimizer (MGO), which integrates fuzzy theory and Pareto optimization to minimize costs and emissions. Results demonstrate significant benefits of the proposed DSM strategies. DSM 2 enhances self-consumption rates by approximately 17 % for individual prosumers (IP) and 14–17 % for energy communities, while DSM 1 effectively reduces grid exchanges by 9 % for prosumers and up to 17 % for energy communities. The optimization framework also facilitates a more balanced distribution of generation and demand, alleviating grid stress. These findings underscore the potential of integrated DSM strategies and multi-objective optimization in advancing the performance and sustainability of renewable energy systems, offering diverse advantages in self-consumption and grid interaction.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101098"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy consumption and workload prediction for edge nodes in the Computing Continuum 计算连续体中边缘节点的能量消耗和工作负载预测

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-28 DOI: 10.1016/j.suscom.2025.101088

Sergio Laso , Pablo Rodríguez , Juan Luis Herrera , Javier Berrocal , Juan M. Murillo

{"title":"Energy consumption and workload prediction for edge nodes in the Computing Continuum","authors":"Sergio Laso , Pablo Rodríguez , Juan Luis Herrera , Javier Berrocal , Juan M. Murillo","doi":"10.1016/j.suscom.2025.101088","DOIUrl":"10.1016/j.suscom.2025.101088","url":null,"abstract":"<div><div>The Computing Continuum paradigm provides developers with a distributed infrastructure for deploying applications through the network, improving performance and energy consumption. However, to maintain applications’ efficiency, their deployment in the Computing Continuum has to be continuously adapted to the varying load of different nodes of the network. In practice, existing support frameworks allow developers to automatically identify how to deploy applications based on the infrastructure status. However, as the application takes time to be deployed, the chosen deployment is outdated once it is applied through the network, as workloads change over time. To address this practical engineering challenge and plan deployments that foresee changes in energy consumption and workload, predictive solutions are needed. To fulfill this need, this work presents the Microservice Energy consumption and Workload Forecaster (MEWF), a prediction system that leverages artificial intelligence techniques to precisely predict CPU usage and energy consumption in varying circumstances. Our practical evaluation over multiple real microservices shows that MEWF improves prediction precision by up to 55% w.r.t. state-of-the-art benchmarks, enabling efficient resource management and demonstrating significant value for real-world deployments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101088"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secured Energy Efficient Chaotic Gazelle based Optimized Routing Protocol in mobile ad-hoc network 基于安全节能混沌瞪羚的移动自组织网络优化路由协议

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-20 DOI: 10.1016/j.suscom.2025.101086

Gajendra Kumar Ahirwar, Ratish Agarwal, Anjana Pandey

{"title":"Secured Energy Efficient Chaotic Gazelle based Optimized Routing Protocol in mobile ad-hoc network","authors":"Gajendra Kumar Ahirwar, Ratish Agarwal, Anjana Pandey","doi":"10.1016/j.suscom.2025.101086","DOIUrl":"10.1016/j.suscom.2025.101086","url":null,"abstract":"<div><div>In this research, a Secured Energy Efficient Chaotic Gazelle based Optimized Routing Protocol (SE<sup>2</sup>CG-ORP) is proposed to enhance the security for routing. The Feistel Structured Tiny Encryption Scheme (FS_TES) performs encryption after the data packets are initially created to enhance their secrecy and security. The nodes are then grouped using the K-Means Clustering technique to reduce network communication lag. The Type-II Fuzzy-C-Means technique considers high energy, trust value, and node centrality when selecting the cluster leader. The chosen cluster head sends the data packets to the base station using the Secured Energy Efficient Chaotic Gazelle-based Optimized Routing Protocol (SE2CG-ORP). Here, the residual energy and node distance parameters are satisfied using the Chaotic Gazelle Optimization (CGO) method to identify the most effective route for data transmission. The proposed model is compared to several current models in the results section using a variety of performance metrics, including PDR, residual energy, throughput, encryption and decryption times, delays, and network lifespan. By varying the number of rounds, the proposed approach obtained 62 Mbps, 96.65 %, and 92.07 % of throughput, residual energy, and PDR. Moreover, 0.77 ms of delay is obtained by varying the number of nodes. The PDR value of 79 % and the network lifespan of 1473.63 h were acquired by varying the number of nodes. The consumed energy of the network is 44.59 J, while the encryption and decryption times are 1831.36 ms and 1641.48 ms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101086"},"PeriodicalIF":3.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing IoT network lifetime through an enhanced hybrid energy harvesting system 通过增强的混合能量收集系统优化物联网网络寿命

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-18 DOI: 10.1016/j.suscom.2025.101081

Sirine Rabah , Aida Zaier , Sandra Sendra , Jaime Lloret , Hassen Dahman

{"title":"Optimizing IoT network lifetime through an enhanced hybrid energy harvesting system","authors":"Sirine Rabah , Aida Zaier , Sandra Sendra , Jaime Lloret , Hassen Dahman","doi":"10.1016/j.suscom.2025.101081","DOIUrl":"10.1016/j.suscom.2025.101081","url":null,"abstract":"<div><div>The growing need for sustainable and renewable energy sources has become critical with the Internet of Things (IoT) advancement. IoT relies on low-power, battery-operated devices, but the limited lifespan of these batteries requires frequent recharging or replacement, which is costly and time-consuming. Researchers have proposed energy harvesting systems that capture sustainable ambient energy from the environment to address this issue. This paper presents a hybrid system for harvesting sustainable energy from solar and wind sources. The system features a boost converter controlled by a novel hybrid method combining the Honey Badger Algorithm (HBA) and Harris Hawks Optimization (HHO). This method maximizes power extraction from solar and wind sources, enhancing overall system efficiency. Additionally, the system includes an innovative energy management algorithm that selects the most powerful input source while protecting the storage battery from overcharging or complete depletion, thereby extending its lifespan. The proposed design is validated through MATLAB/Simulink simulations. The HHO–HBA MPPT is compared with existing MPPT methods, evaluating efficiency, battery charge curves, and IoT network energy status. Simulation results show that the proposed approach significantly increases network longevity, offering a cost-effective and sustainable solution for the energy needs of Wireless Sensor Network (WSN)-IoT devices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101081"},"PeriodicalIF":3.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system 基于RBF神经网络的高效自适应光伏系统能量收集

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-17 DOI: 10.1016/j.suscom.2025.101083

Mohand Akli Kacimi , Celia Aoughlis , Toufik Bakir , Ouahib Guenounou

{"title":"Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system","authors":"Mohand Akli Kacimi , Celia Aoughlis , Toufik Bakir , Ouahib Guenounou","doi":"10.1016/j.suscom.2025.101083","DOIUrl":"10.1016/j.suscom.2025.101083","url":null,"abstract":"<div><div>This paper deals with a topical topic, the maximum energy harvest of standalone PV system under varying conditions. It introduces a new approach based on the use of artificial intelligence and machine learning to overcome the usual weaknesses of conventional Maximum Power Point Tracking (MPPT) techniques and to improve solutions to meet growing energy demand and further promote sustainable development. The proposal consists of using Radial Basis Function Neural Network (RBFNN) tuned by a PSO algorithm as MPPT controller. The main aim of this combination (RBFNN-PSO) is to achieve the best compromise between the control accuracy and complexity, while using a simple optimization algorithm. This aim is motivated by the potential of the neural networks to learn from any tasks and to generalize the acquired knowledge to other situation never seen before. The proposal reaches a high efficiency and high energy harvesting with a yield greater than 99 %. The performed comparative study with other intelligent techniques from literature prove the superiority and the promising potential of the introduced approach. The developed work presented in this paper is developed with MatLab/Simulink environment.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101083"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An energy efficient location aware geographic routing protocol based on anchor node path planning and optimized Q-learning model 一种基于锚节点路径规划和优化q -学习模型的节能位置感知地理路由协议

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-01-17 DOI: 10.1016/j.suscom.2025.101084

K. Bhadrachalam , B. Lalitha

{"title":"An energy efficient location aware geographic routing protocol based on anchor node path planning and optimized Q-learning model","authors":"K. Bhadrachalam , B. Lalitha","doi":"10.1016/j.suscom.2025.101084","DOIUrl":"10.1016/j.suscom.2025.101084","url":null,"abstract":"<div><div>A wireless sensor network (WSN) is made up of many nodes that can send sensed data to the base station or sink directly or through intermediary nodes. However, geographically based routing requires accurate sensor node location data. The precise localization of dispersed sensors within a designated region is a critical problem in WSN development. This study proposes a new location-aware geographic routing protocol, which is based on the Q-learning model and anchor node path planning. Initially, the location of an unknown node is detected using an Integrated Received Signal Strength Indicator (RSSI) and Cosine rule-based path planning model. After detecting the unknown nodes, each node is forwarded through a HELLO message to identify the routing neighbour nodes. Then, the Optimal Osprey Q-Learning (O<sup>2</sup>QL) model is used in multi-objective optimization to choose the best path routing. Then, the Q-learning model's reward function is responsible for both end-to-end latency and energy consumption. Moreover, the Q-learning parameters of the suggested protocol can be adaptively updated to accommodate the high process degrees found in WSNs. Simulations have been conducted to prove the efficacy of the method based on different metrics. The proposed approach has been compared with the existing recently introduced routing protocols in WSN. As a result, the proposed location-aware energy-efficient geographic routing techniques show performance in terms of average end-to-end delay of nodes (2.88), packet loss ratio of nodes (0.058), residual energy of nodes (0.199), average energy consumption of nodes (1.53) and packet delivery rate of nodes (98.96).</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101084"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0