{"title":"BLEnd: Practical Continuous Neighbor Discovery for Bluetooth Low Energy","authors":"C. Julien, Chenguang Liu, A. Murphy, G. Picco","doi":"10.1145/3055031.3055086","DOIUrl":null,"url":null,"abstract":"Identifying \"who is around\" is key in a plethora of smart scenarios. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. In this paper, we take a system approach to continuous neighbor discovery: starting with the concrete technology of Bluetooth Low Energy (BLE) we build a protocol, called BLEnd, tailored to its constraints. Moreover, we also consider the very real effects of packet collisions, to our knowledge a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications; in this respect, the slotless operation of BLEnd offers richer alternatives than state-of-the-art protocols. Developers specify the minimum discovery probability, the target discovery latency, and the maximum expected node density; these are used by an optimizer tool to parameterize the BLEnd implementation towards maximum lifetime. This paper shows that BLEnd not only achieves the user-specified goals, but does so more efficiently than analogous configurations of competing protocols.","PeriodicalId":228318,"journal":{"name":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3055031.3055086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 48
Abstract
Identifying "who is around" is key in a plethora of smart scenarios. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. In this paper, we take a system approach to continuous neighbor discovery: starting with the concrete technology of Bluetooth Low Energy (BLE) we build a protocol, called BLEnd, tailored to its constraints. Moreover, we also consider the very real effects of packet collisions, to our knowledge a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications; in this respect, the slotless operation of BLEnd offers richer alternatives than state-of-the-art protocols. Developers specify the minimum discovery probability, the target discovery latency, and the maximum expected node density; these are used by an optimizer tool to parameterize the BLEnd implementation towards maximum lifetime. This paper shows that BLEnd not only achieves the user-specified goals, but does so more efficiently than analogous configurations of competing protocols.