{"title":"Heuristic Algorithms for Scheduling Resources in Time-Constrained Wireless Sensor Networks","authors":"Y. Kim, Yu Wang, B. Park, H. Choi","doi":"10.1109/PLATCON.2015.16","DOIUrl":null,"url":null,"abstract":"This paper proposes a heuristic algorithm for scheduling multiple channels and multiple timeslots in a time-constrained industrial wireless sensor network. The objective is to minimize the end-to-end (e2e) delay. The proposed algorithm is compared with the meta-heuristics in terms of the given e2e delay bound, where it is common in time-constrained industrial networks. Not surprisingly, the meta-heuristics is more accurate in finding a global optimum or sub-optimal values than the heuristic approach at the expense of significant run times and programming effort. The proposed greedy heuristic approach has advantages such as reflection of design context in decision-making and fast communication between stakeholders.","PeriodicalId":220038,"journal":{"name":"2015 International Conference on Platform Technology and Service","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Platform Technology and Service","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLATCON.2015.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper proposes a heuristic algorithm for scheduling multiple channels and multiple timeslots in a time-constrained industrial wireless sensor network. The objective is to minimize the end-to-end (e2e) delay. The proposed algorithm is compared with the meta-heuristics in terms of the given e2e delay bound, where it is common in time-constrained industrial networks. Not surprisingly, the meta-heuristics is more accurate in finding a global optimum or sub-optimal values than the heuristic approach at the expense of significant run times and programming effort. The proposed greedy heuristic approach has advantages such as reflection of design context in decision-making and fast communication between stakeholders.