{"title":"低占空比移动传感器网络中的快速响应邻居发现算法","authors":"Anquan Zhang, Dongming Xu","doi":"10.1145/3573942.3573984","DOIUrl":null,"url":null,"abstract":"With the rapid development of the Internet of Things, wireless sensor network, one of its important supporting technologies, has attracted more and more attention. We will work in the low duty cycle wireless sensor network, called low duty cycle wireless sensor network. Neighbor discovery is the most initial but essential work in low duty cycle wireless sensor networks. Although some neighbor discovery algorithms can also achieve neighbor discovery, the average discovery delay is long, and it is difficult to achieve the ability to respond quickly. How to make the nodes in the network quickly realize neighbor discovery is a difficult problem in current research. This paper proposes a group-based fast-response neighbor discovery algorithm (GBFR, in short). At the beginning of the time period, the nodes search for their neighbors by sending a short beacon message, so that the nodes group in pairs. By exchanging neighbor work schedules, nodes know ahead of time some other grouped potential neighbors. Combining the relative distance-based algorithm and node movement, it can selectively recommend suitable neighbors so that nodes can wake up actively and determine whether they are neighbors, thereby speeding up neighbor discovery, reducing communication energy consumption, and improving network life. In this paper, a large number of simulation experiments show that the algorithm has achieved good results in reducing the discovery delay and network energy consumption.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Fast Response Neighbor Discovery Algorithm in Low-Duty-Cycle Mobile Sensor Networks\",\"authors\":\"Anquan Zhang, Dongming Xu\",\"doi\":\"10.1145/3573942.3573984\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of the Internet of Things, wireless sensor network, one of its important supporting technologies, has attracted more and more attention. We will work in the low duty cycle wireless sensor network, called low duty cycle wireless sensor network. Neighbor discovery is the most initial but essential work in low duty cycle wireless sensor networks. Although some neighbor discovery algorithms can also achieve neighbor discovery, the average discovery delay is long, and it is difficult to achieve the ability to respond quickly. How to make the nodes in the network quickly realize neighbor discovery is a difficult problem in current research. This paper proposes a group-based fast-response neighbor discovery algorithm (GBFR, in short). At the beginning of the time period, the nodes search for their neighbors by sending a short beacon message, so that the nodes group in pairs. By exchanging neighbor work schedules, nodes know ahead of time some other grouped potential neighbors. Combining the relative distance-based algorithm and node movement, it can selectively recommend suitable neighbors so that nodes can wake up actively and determine whether they are neighbors, thereby speeding up neighbor discovery, reducing communication energy consumption, and improving network life. In this paper, a large number of simulation experiments show that the algorithm has achieved good results in reducing the discovery delay and network energy consumption.\",\"PeriodicalId\":103293,\"journal\":{\"name\":\"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3573942.3573984\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3573942.3573984","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Fast Response Neighbor Discovery Algorithm in Low-Duty-Cycle Mobile Sensor Networks
With the rapid development of the Internet of Things, wireless sensor network, one of its important supporting technologies, has attracted more and more attention. We will work in the low duty cycle wireless sensor network, called low duty cycle wireless sensor network. Neighbor discovery is the most initial but essential work in low duty cycle wireless sensor networks. Although some neighbor discovery algorithms can also achieve neighbor discovery, the average discovery delay is long, and it is difficult to achieve the ability to respond quickly. How to make the nodes in the network quickly realize neighbor discovery is a difficult problem in current research. This paper proposes a group-based fast-response neighbor discovery algorithm (GBFR, in short). At the beginning of the time period, the nodes search for their neighbors by sending a short beacon message, so that the nodes group in pairs. By exchanging neighbor work schedules, nodes know ahead of time some other grouped potential neighbors. Combining the relative distance-based algorithm and node movement, it can selectively recommend suitable neighbors so that nodes can wake up actively and determine whether they are neighbors, thereby speeding up neighbor discovery, reducing communication energy consumption, and improving network life. In this paper, a large number of simulation experiments show that the algorithm has achieved good results in reducing the discovery delay and network energy consumption.