Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things
The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including te...
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/37 |
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| author | Sumaiah Algarni Fathi E. Abd El-Samie |
| author_facet | Sumaiah Algarni Fathi E. Abd El-Samie |
| author_sort | Sumaiah Algarni |
| collection | DOAJ |
| description | The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks. |
| format | Article |
| id | doaj-art-b78d0186d4d64788a221d83e0cbed714 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-b78d0186d4d64788a221d83e0cbed7142025-01-24T13:33:38ZengMDPI AGFuture Internet1999-59032025-01-011713710.3390/fi17010037Energy-Efficient Distributed Edge Computing to Assist Dense Internet of ThingsSumaiah Algarni0Fathi E. Abd El-Samie1Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaThe Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks.https://www.mdpi.com/1999-5903/17/1/37distributed computingIoTfog computingresource utilizationmultiple access edge computingreliability |
| spellingShingle | Sumaiah Algarni Fathi E. Abd El-Samie Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things Future Internet distributed computing IoT fog computing resource utilization multiple access edge computing reliability |
| title | Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things |
| title_full | Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things |
| title_fullStr | Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things |
| title_full_unstemmed | Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things |
| title_short | Energy-Efficient Distributed Edge Computing to Assist Dense Internet of Things |
| title_sort | energy efficient distributed edge computing to assist dense internet of things |
| topic | distributed computing IoT fog computing resource utilization multiple access edge computing reliability |
| url | https://www.mdpi.com/1999-5903/17/1/37 |
| work_keys_str_mv | AT sumaiahalgarni energyefficientdistributededgecomputingtoassistdenseinternetofthings AT fathieabdelsamie energyefficientdistributededgecomputingtoassistdenseinternetofthings |