Knowledge Commons of Institute of Automation,CAS
Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks; Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks; Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks | |
Diao, Jin1; Zhao, Deng1; Wang, Junping2; Nguyen, Hien M.3; Tang, Jine4; Zhou, Zhangbing1,5 | |
发表期刊 | IEEE SENSORS JOURNAL ; IEEE SENSORS JOURNAL ; IEEE SENSORS JOURNAL |
ISSN | 1530-437X ; 1530-437X ; 1530-437X |
2019-09-15 ; 2019-09-15 ; 2019-09-15 | |
卷号 | 19期号:18页码:8303-8316 |
摘要 | Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques. ;Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques. ;Sensing network of the Internet of Things (IoT) has become the infrastructure to facilitate the near real-time monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This paper proposes an energy-efficient boundary detection mechanism in IoT sensing networks. Specifically, a sleeping mechanism is adapted to detect the relatively coarse boundary through applying the convex hull algorithm, where only the relay nodes are activated. Leveraging the analysis of the relation for corresponding boundary nodes, the boundary area around a boundary node is categorized as three types of sub-areas with the descending possibility of event occurrence, i.e., the most possible, possible, and impossible areas. An optimized greedy algorithm is adapted to selectively activate certain numbers of one-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all one-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. The experimental results demonstrate that this technique can achieve a better detection accuracy, while reducing energy consumption to a large extent, than the state of art's techniques. |
关键词 | Boundary detection continuous objects IoT sensing networks energy efficiency Boundary detection continuous objects IoT sensing networks energy efficiency Boundary detection continuous objects IoT sensing networks energy efficiency |
DOI | 10.1109/JSEN.2019.2919580 ; 10.1109/JSEN.2019.2919580 ; 10.1109/JSEN.2019.2919580 |
关键词[WOS] | WIRELESS ; ALGORITHM ; TRACKING ; WIRELESS ; ALGORITHM ; TRACKING ; WIRELESS ; ALGORITHM ; TRACKING |
收录类别 | SCI ; SCI ; SCI |
语种 | 英语 ; 英语 ; 英语 |
资助项目 | National Natural Science Foundation of China[61772479] ; National Natural Science Foundation of China[61702232] ; National Natural Science Foundation of China[61662021] ; National Natural Science Foundation of China[61772479] ; National Natural Science Foundation of China[61772479] ; National Natural Science Foundation of China[61772479] ; National Natural Science Foundation of China[61702232] ; National Natural Science Foundation of China[61702232] ; National Natural Science Foundation of China[61702232] ; National Natural Science Foundation of China[61662021] ; National Natural Science Foundation of China[61662021] ; National Natural Science Foundation of China[61662021] |
WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics ; Engineering ; Instruments & Instrumentation ; Physics ; Engineering ; Instruments & Instrumentation ; Physics |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied |
WOS记录号 | WOS:000481964500056 ; WOS:000481964500056 ; WOS:000481964500056 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ; IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ; IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 否 ; 否 ; 否 |
七大方向——子方向分类 | 机器学习 ; 机器学习 ; 机器学习 |
国重实验室规划方向分类 | 认知机理与类脑学习 ; 认知机理与类脑学习 ; 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 ; 否 ; 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27620 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Zhou, Zhangbing |
作者单位 | 1.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Lab Precis Sensing & Control Ctr, Inst Automat, Beijing 100190, Peoples R China 3.Duy Tan Univ, Sch Elect & Elect Engn, Da Nang 550000, Vietnam 4.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China 5.TELECOM SudParis, Comp Sci Dept, F-91001 Evry, France |
推荐引用方式 GB/T 7714 | Diao, Jin,Zhao, Deng,Wang, Junping,et al. Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks, Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks, Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks[J]. IEEE SENSORS JOURNAL, IEEE SENSORS JOURNAL, IEEE SENSORS JOURNAL,2019, 2019, 2019,19, 19, 19(18):8303-8316, 8303-8316, 8303-8316. |
APA | Diao, Jin,Zhao, Deng,Wang, Junping,Nguyen, Hien M.,Tang, Jine,&Zhou, Zhangbing.(2019).Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks.IEEE SENSORS JOURNAL,19(18),8303-8316. |
MLA | Diao, Jin,et al."Energy-Efficient Boundary Detection of Continuous Objects in IoT Sensing Networks".IEEE SENSORS JOURNAL 19.18(2019):8303-8316. |
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