Improving energy efficiency via the use of IoT technologies with intelligent network clustering.
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| Title: | Improving energy efficiency via the use of IoT technologies with intelligent network clustering. |
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| Authors: | Abujassar, Radwan S.1 (AUTHOR) r.abujassar@aou.edu.kw, Al-Jarrah, Omar1 (AUTHOR) o.aljarrah@arabou.edu.kw |
| Source: | Wireless Networks (10220038). Jun2025, Vol. 31 Issue 5, p3533-3553. 21p. |
| Subjects: | Intelligent networks, Telecommunication, Artificial intelligence, Traffic congestion, Energy consumption, Multicasting (Computer networks), Wireless sensor networks, Data transmission systems |
| Abstract: | The transmission of large volumes of sensor node data is the biggest challenge for IoT networks. Communication power overuse threatens nodes' survivability. Thus, network challenges including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Data transmission between companies requires routing mechanisms. Data aggregation is critical to reducing traffic congestion, operational costs, energy use, and network lifetime. When IoT data is consolidated, route planning for reliability, energy efficiency, and effectiveness is tough. This work presents Cluster-based energy-aware & nearest adjacent neighbour (CEAAN), a novel routing approach using NS2 simulation. This approach predicts delivery success using decision trees and neural networks. We consider CEAAN routing scheme predictability, node popularity, power consumption, speed, and location during model training. According to simulations, CEAAN outperforms NS2's trustworthy routing scheme in terms of successful delivery, lost messages, overhead, and hop count. However, these changes only slightly enhance buffer length and occupancy. The hybrid routing technique entails cluster construction, as well as intra- and inter-cluster routing. CEAAN outperformed earlier studies in network resilience, packet transmission efficiency, end-to-end latency, and energy utilization. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The transmission of large volumes of sensor node data is the biggest challenge for IoT networks. Communication power overuse threatens nodes' survivability. Thus, network challenges including QoS, security, network heterogeneity, congestion avoidance, reliable routing, and energy savings must be addressed. Data transmission between companies requires routing mechanisms. Data aggregation is critical to reducing traffic congestion, operational costs, energy use, and network lifetime. When IoT data is consolidated, route planning for reliability, energy efficiency, and effectiveness is tough. This work presents Cluster-based energy-aware & nearest adjacent neighbour (CEAAN), a novel routing approach using NS2 simulation. This approach predicts delivery success using decision trees and neural networks. We consider CEAAN routing scheme predictability, node popularity, power consumption, speed, and location during model training. According to simulations, CEAAN outperforms NS2's trustworthy routing scheme in terms of successful delivery, lost messages, overhead, and hop count. However, these changes only slightly enhance buffer length and occupancy. The hybrid routing technique entails cluster construction, as well as intra- and inter-cluster routing. CEAAN outperformed earlier studies in network resilience, packet transmission efficiency, end-to-end latency, and energy utilization. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10220038 |
| DOI: | 10.1007/s11276-025-03948-w |
