SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services
Edge computing changed the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy breach. However, advances in deep learning enabled Internet of Things (IoTs) to onload tasks and run cognitive tasks locally. This research int...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Information Management Data Insights |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096824000715 |
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| author | Samaa Elnagar Kweku Muata Osei Bryson Manoj Thomas |
| author_facet | Samaa Elnagar Kweku Muata Osei Bryson Manoj Thomas |
| author_sort | Samaa Elnagar |
| collection | DOAJ |
| description | Edge computing changed the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy breach. However, advances in deep learning enabled Internet of Things (IoTs) to onload tasks and run cognitive tasks locally. This research introduces a decentralized-control edge model where computation and decision-making are moved to the IoT level. The model aims at decreasing communication and computation dependance on the edge which affect efficiency and latency. The model also limits data transfer to the edge to avoid security and privacy risks. Decentralized control is a key to many business applications that prioritizes safety, real-time response, and privacy such as ridesharing monitoring and industrial operations. To examine the model, we developed SAFERIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current monitoring systems require costly infrastructure and continuous network connectivity. However, SAFRIDES uses optimized deep learning models that run locally on IoTs to detect and record violations in ridesharing. The system achieved the lowest latency among current solution, while minimizing data sharing and maintaining privacy. Moreover, decentralized edge computing empowers IoTs and upgrades their functionality from sensing to independent decision-making. |
| format | Article |
| id | doaj-art-9b796edc4803412f9dc5b2140fca08c4 |
| institution | OA Journals |
| issn | 2667-0968 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-9b796edc4803412f9dc5b2140fca08c42025-08-20T01:59:31ZengElsevierInternational Journal of Information Management Data Insights2667-09682024-11-014210028210.1016/j.jjimei.2024.100282SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring servicesSamaa Elnagar0Kweku Muata Osei Bryson1Manoj Thomas2Howard University, Information systems and supply chain, Washington, DC, USA; Corresponding author.Virginia Commonwealth University, Information systems, Richmond, VA, USAInformation systems, University of Sydney, Sydney, AustraliaEdge computing changed the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy breach. However, advances in deep learning enabled Internet of Things (IoTs) to onload tasks and run cognitive tasks locally. This research introduces a decentralized-control edge model where computation and decision-making are moved to the IoT level. The model aims at decreasing communication and computation dependance on the edge which affect efficiency and latency. The model also limits data transfer to the edge to avoid security and privacy risks. Decentralized control is a key to many business applications that prioritizes safety, real-time response, and privacy such as ridesharing monitoring and industrial operations. To examine the model, we developed SAFERIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current monitoring systems require costly infrastructure and continuous network connectivity. However, SAFRIDES uses optimized deep learning models that run locally on IoTs to detect and record violations in ridesharing. The system achieved the lowest latency among current solution, while minimizing data sharing and maintaining privacy. Moreover, decentralized edge computing empowers IoTs and upgrades their functionality from sensing to independent decision-making.http://www.sciencedirect.com/science/article/pii/S2667096824000715Decentralized controlEdge computingDeep learningInternet of ThingsRidesharingMonitoring |
| spellingShingle | Samaa Elnagar Kweku Muata Osei Bryson Manoj Thomas SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services International Journal of Information Management Data Insights Decentralized control Edge computing Deep learning Internet of Things Ridesharing Monitoring |
| title | SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services |
| title_full | SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services |
| title_fullStr | SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services |
| title_full_unstemmed | SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services |
| title_short | SAFERIDES: Application of decentralized control edge-computing to ridesharing monitoring services |
| title_sort | saferides application of decentralized control edge computing to ridesharing monitoring services |
| topic | Decentralized control Edge computing Deep learning Internet of Things Ridesharing Monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S2667096824000715 |
| work_keys_str_mv | AT samaaelnagar saferidesapplicationofdecentralizedcontroledgecomputingtoridesharingmonitoringservices AT kwekumuataoseibryson saferidesapplicationofdecentralizedcontroledgecomputingtoridesharingmonitoringservices AT manojthomas saferidesapplicationofdecentralizedcontroledgecomputingtoridesharingmonitoringservices |