{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T21:48:27Z","timestamp":1771883307550,"version":"3.50.1"},"reference-count":47,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p> Due to their increasing computational power and energy-efficient hardware, today\u2019s smart mobile devices (SMDs) are replacing desktops and laptops as casual computing devices. Moreover, a cluster of such powerful SMDs can garner substantial high-performance computing (HPC). Such an HPC is achieved by utilizing publicly owned SMDs in mobile crowd computing (MCC). Here, a large computing-intensive task is divided and scheduled for the available SMDs for execution, and the results are recollected. This approach provides an economical and sustainable HPC. However, battery-powered constrained energy is a great hindrance to achieving this goal. Therefore, in the MCC, it is crucial to minimize the overall energy consumption to complete the task. This can be achieved to some extent by optimizing task scheduling to the appropriate SMDs. However, considering only energy efficiency might lead to an enormous load imbalance among SMDs, i.e., the most energy-efficient SMDs would be overloaded most of the time. Considering this, in this paper, we present a modified particle swarm optimization (PSO)-based scheduling algorithm to minimize the overall energy consumption among a set of SMDs designated to execute a set of MCC tasks while maintaining a satisfactory load balance level. Extensive simulations with both synthetic and real data sets are carried out to analyze and validate the proposed method. The work was compared with popular heuristic (minimum completion time (MCT), MinMin, MaxMin, and preconditioned progressive iterative approximation (PPIA)) and metaheuristic (genetic algorithm (GA)) optimization algorithms, which yields significant improvements over others in terms of the considered objectives. In addition, an analysis of variance (ANOVA) test is conducted to provide further evidence regarding the distinctiveness of the proposed algorithm. <\/jats:p>","DOI":"10.1177\/00375497241298346","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T10:02:56Z","timestamp":1734429776000},"page":"159-176","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Particle swarm optimization-based load balance-aware energy-efficient scheduling for mobile crowd computing"],"prefix":"10.1177","volume":"101","author":[{"given":"Pijush Kanti Dutta","family":"Pramanik","sequence":"first","affiliation":[{"name":"School of Computer Applications & Technology, Galgotias University, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8218-1383","authenticated-orcid":false,"given":"Tarun","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Indian Institute of Information Technology Ranchi, India"}]},{"given":"Prasenjit","family":"Choudhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, National Institute of Technology Durgapur, India"}]}],"member":"179","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"bibr1-00375497241298346","first-page":"1","volume-title":"4th international conference on advanced computing and communication systems (ICACCS\u20142017)","author":"Pramanik PKD"},{"key":"bibr2-00375497241298346","first-page":"67","volume-title":"5th HCT information technology trends (ITT)","author":"Tamimi H"},{"key":"bibr3-00375497241298346","unstructured":"Oh W. India will overtake US to become world\u2019s second largest smartphone market by 2017, 1 July, https:\/\/www.strategyanalytics.com\/strategy-analytics\/news\/strategy-analytics-press-releases\/strategy-analytics-press-release\/2015\/07\/01\/India-will-overtake-US-to-become-world's-second-largest-smartphone-market-by-2017#.VuHPKPl97IX (2015 accessed 11 March 2016)."},{"key":"bibr4-00375497241298346","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-4666-8662-5.ch005"},{"key":"bibr5-00375497241298346","doi-asserted-by":"publisher","DOI":"10.1145\/3428151"},{"key":"bibr6-00375497241298346","first-page":"166","volume-title":"Crowdsourcing and knowledge management in contemporary business environments","author":"Pramanik 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