Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring
As a novel cyber-physical-social network paradigm, the Internet of Things (IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion, and external attacks, the emergence of coverage holes would remarkably degrade the netwo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8046017/ |
| _version_ | 1818411676624486400 |
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| author | Lingzhi Yi Xianjun Deng Minghua Wang Dexin Ding Yan Wang |
| author_facet | Lingzhi Yi Xianjun Deng Minghua Wang Dexin Ding Yan Wang |
| author_sort | Lingzhi Yi |
| collection | DOAJ |
| description | As a novel cyber-physical-social network paradigm, the Internet of Things (IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion, and external attacks, the emergence of coverage holes would remarkably degrade the network performance and quality of service. For overcoming the drawbacks resulting from the coverage holes, this paper focuses on how to locally detect coverage holes by exploiting one-hop neighboring sensors' cooperation based on the novel confident information coverage model (CIC), which is formulated as the localized confident information coverage hole detection (LCICHD) problem. For handling the CICHD problem, we devise a family of heuristic CIC holes detection schemes including the LCHD, LCHDRL, random and randomRL. Both the LCHD and LCHDRL schemes locally determine coverage status of each subregion and take the sensor communication ability into consideration. While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection. After acquiring the coverage status of each partitioned local subregion, the coverage hole boundary will be extracted by image processing techniques. For comparison, both the Random and RandomRL schemes arbitrarily select sensors within the sensing field to detect CIC holes, and the RandomRL scheme takes the sensors' residual lifetime into consideration during the hole detection process. Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the LCHDRL algorithm is more practical and efficient compared with the other three peer solutions. |
| first_indexed | 2024-12-14T10:35:12Z |
| format | Article |
| id | doaj.art-85ee12ec50ba4530863ee89146963f22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:35:12Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-85ee12ec50ba4530863ee89146963f222022-12-21T23:05:58ZengIEEEIEEE Access2169-35362017-01-015186651867410.1109/ACCESS.2017.27542698046017Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution MonitoringLingzhi Yi0https://orcid.org/0000-0003-0071-1016Xianjun Deng1Minghua Wang2Dexin Ding3Yan Wang4School of Environmental Protection and Safety Engineering, University of South China, Hengyang, ChinaDepartment of Communications Engineering, University of South China, Hengyang, ChinaDepartment of Communications Engineering, University of South China, Hengyang, ChinaSchool of Environmental Protection and Safety Engineering, University of South China, Hengyang, ChinaDepartment of Communications Engineering, University of South China, Hengyang, ChinaAs a novel cyber-physical-social network paradigm, the Internet of Things (IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion, and external attacks, the emergence of coverage holes would remarkably degrade the network performance and quality of service. For overcoming the drawbacks resulting from the coverage holes, this paper focuses on how to locally detect coverage holes by exploiting one-hop neighboring sensors' cooperation based on the novel confident information coverage model (CIC), which is formulated as the localized confident information coverage hole detection (LCICHD) problem. For handling the CICHD problem, we devise a family of heuristic CIC holes detection schemes including the LCHD, LCHDRL, random and randomRL. Both the LCHD and LCHDRL schemes locally determine coverage status of each subregion and take the sensor communication ability into consideration. While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection. After acquiring the coverage status of each partitioned local subregion, the coverage hole boundary will be extracted by image processing techniques. For comparison, both the Random and RandomRL schemes arbitrarily select sensors within the sensing field to detect CIC holes, and the RandomRL scheme takes the sensors' residual lifetime into consideration during the hole detection process. Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the LCHDRL algorithm is more practical and efficient compared with the other three peer solutions.https://ieeexplore.ieee.org/document/8046017/Confident information coverage hole detectioninternet of thingscyber-physical-social networking |
| spellingShingle | Lingzhi Yi Xianjun Deng Minghua Wang Dexin Ding Yan Wang Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring IEEE Access Confident information coverage hole detection internet of things cyber-physical-social networking |
| title | Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring |
| title_full | Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring |
| title_fullStr | Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring |
| title_full_unstemmed | Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring |
| title_short | Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring |
| title_sort | localized confident information coverage hole detection in internet of things for radioactive pollution monitoring |
| topic | Confident information coverage hole detection internet of things cyber-physical-social networking |
| url | https://ieeexplore.ieee.org/document/8046017/ |
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