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
Source PublicationIEEE SENSORS JOURNAL ; IEEE SENSORS JOURNAL ; IEEE SENSORS JOURNAL
ISSN1530-437X ; 1530-437X ; 1530-437X
2019-09-15 ; 2019-09-15 ; 2019-09-15
Volume19Issue:18Pages:8303-8316
Abstract

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.

KeywordBoundary 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
DOI10.1109/JSEN.2019.2919580 ; 10.1109/JSEN.2019.2919580 ; 10.1109/JSEN.2019.2919580
WOS KeywordWIRELESS ; ALGORITHM ; TRACKING ; WIRELESS ; ALGORITHM ; TRACKING ; WIRELESS ; ALGORITHM ; TRACKING
Indexed BySCI ; SCI ; SCI
Language英语 ; 英语 ; 英语
Funding ProjectNational Natural Science Foundation of China[61662021] ; National Natural Science Foundation of China[61702232] ; 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[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 Research AreaEngineering ; Instruments & Instrumentation ; Physics ; Engineering ; Instruments & Instrumentation ; Physics ; Engineering ; Instruments & Instrumentation ; Physics
WOS SubjectEngineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied ; Engineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS IDWOS:000481964500056 ; WOS:000481964500056 ; WOS:000481964500056
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ; IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ; IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IS Representative Paper否 ; 否 ; 否
Sub direction classification机器学习 ; 机器学习 ; 机器学习
planning direction of the national heavy laboratory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
Paper associated data否 ; 否 ; 否
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27620
Collection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
Corresponding AuthorZhou, Zhangbing
Affiliation1.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
Recommended Citation
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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