{"title":"Load-balancing model using game theory in edge-based IoT network","authors":"Zaineb Naaz , Gamini Joshi , Vidushi Sharma","doi":"10.1016/j.pmcj.2025.102041","DOIUrl":null,"url":null,"abstract":"<div><div>To manage increasing volume of IoT data, edge computing offers scalable solutions, but increasing data loads can overwhelm edge nodes, depleting resources and extending processing times. This paper proposes a load-balancing model using game theory (LMGT) in edge computing-assisted IoT networks by considering nodes lifetime as their primary resource to reduce IoT task execution times, especially for time-sensitive tasks. Simulation results demonstrate that LMGT outperforms existing methods—Preference-Based Stable Mechanism (PBSM), Centralized, Min-Min, and Max-Min—in terms of execution time reductions achieving improvements of, on average, 40 %, 56 %, 91 %, and 93 %, respectively, across various combinations of edge and IoT nodes. Furthermore, the proposed scheme ensures a more uniform distribution of data load across edge nodes compared to the existing schemes.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"109 ","pages":"Article 102041"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pervasive and Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574119225000306","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
To manage increasing volume of IoT data, edge computing offers scalable solutions, but increasing data loads can overwhelm edge nodes, depleting resources and extending processing times. This paper proposes a load-balancing model using game theory (LMGT) in edge computing-assisted IoT networks by considering nodes lifetime as their primary resource to reduce IoT task execution times, especially for time-sensitive tasks. Simulation results demonstrate that LMGT outperforms existing methods—Preference-Based Stable Mechanism (PBSM), Centralized, Min-Min, and Max-Min—in terms of execution time reductions achieving improvements of, on average, 40 %, 56 %, 91 %, and 93 %, respectively, across various combinations of edge and IoT nodes. Furthermore, the proposed scheme ensures a more uniform distribution of data load across edge nodes compared to the existing schemes.
期刊介绍:
As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies.
The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.