{"title":"网格划分:2.5D地形下户外摄像头物联网部署的一种高效贪婪方法","authors":"K. Veenstra, K. Obraczka","doi":"10.1109/ICCCN49398.2020.9209624","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce Distributed Grid Partition, a distributed greedy deployment algorithm for outdoor IoT camera networks. The proposed algorithm optimizes visual network coverage over 2.5D terrain. The main idea behind Distributed Grid Partition is that each deployment node tries to find the best vantage point in its neighborhood that will maximize the network’s overall visual coverage. It does so by using information from its immediate neighbors. In order to achieve a favorable cost-performance trade-off, Distributed Grid Partition uses height as a proxy for visual coverage, or fitness, avoiding expensive fitness computations. In addition, each node’s contribution to network fitness is determined without knowledge of the overall network using the concept of \"Wonderful Life Utility\". Our experimental results show that Distributed Grid Partition results in deployments with superior coverage-cost performance when compared to other distributed optimization algorithms as well as a centralized greedy set cover heuristic.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Grid Partition: an Efficient Greedy Approach for Outdoor Camera IoT Deployments in 2.5D Terrain\",\"authors\":\"K. Veenstra, K. Obraczka\",\"doi\":\"10.1109/ICCCN49398.2020.9209624\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce Distributed Grid Partition, a distributed greedy deployment algorithm for outdoor IoT camera networks. The proposed algorithm optimizes visual network coverage over 2.5D terrain. The main idea behind Distributed Grid Partition is that each deployment node tries to find the best vantage point in its neighborhood that will maximize the network’s overall visual coverage. It does so by using information from its immediate neighbors. In order to achieve a favorable cost-performance trade-off, Distributed Grid Partition uses height as a proxy for visual coverage, or fitness, avoiding expensive fitness computations. In addition, each node’s contribution to network fitness is determined without knowledge of the overall network using the concept of \\\"Wonderful Life Utility\\\". Our experimental results show that Distributed Grid Partition results in deployments with superior coverage-cost performance when compared to other distributed optimization algorithms as well as a centralized greedy set cover heuristic.\",\"PeriodicalId\":137835,\"journal\":{\"name\":\"2020 29th International Conference on Computer Communications and Networks (ICCCN)\",\"volume\":\"144 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 29th International Conference on Computer Communications and Networks (ICCCN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCN49398.2020.9209624\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCN49398.2020.9209624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grid Partition: an Efficient Greedy Approach for Outdoor Camera IoT Deployments in 2.5D Terrain
In this paper, we introduce Distributed Grid Partition, a distributed greedy deployment algorithm for outdoor IoT camera networks. The proposed algorithm optimizes visual network coverage over 2.5D terrain. The main idea behind Distributed Grid Partition is that each deployment node tries to find the best vantage point in its neighborhood that will maximize the network’s overall visual coverage. It does so by using information from its immediate neighbors. In order to achieve a favorable cost-performance trade-off, Distributed Grid Partition uses height as a proxy for visual coverage, or fitness, avoiding expensive fitness computations. In addition, each node’s contribution to network fitness is determined without knowledge of the overall network using the concept of "Wonderful Life Utility". Our experimental results show that Distributed Grid Partition results in deployments with superior coverage-cost performance when compared to other distributed optimization algorithms as well as a centralized greedy set cover heuristic.