LTEOC: Long Term Energy Optimization Clustering For Dynamic IoT Networks
| Autor: | Hakim Mabed, Mohamed Sofiane Batta, Saad Harous, Zibouda Aliouat |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Battery (electricity)
State of health Computer science Distributed computing 010401 analytical chemistry 020206 networking & telecommunications 02 engineering and technology Energy minimization 01 natural sciences Networking hardware 0104 chemical sciences Green computing 0202 electrical engineering electronic engineering information engineering Cluster analysis Wireless sensor network Energy (signal processing) |
| Zdroj: | UEMCON |
| DOI: | 10.1109/uemcon51285.2020.9298030 |
| Popis: | Mobile traffic is expected to grow by 30% by 2024, saving energy is a crucial necessity even for battery-powered devices such as smartphones for the sake of green computing. Clustering techniques were introduced to conserve the energy of network devices. However, proposed energy optimization techniques do not yield optimal battery life, they mostly consider devices with non-rechargeable batteries and deal with limited energy without considering battery aging (short-term vision). To this end, we focus on the long-term energy optimization and we introduce a dynamic clustering technique that take into consideration the state of health of devices batteries and their degradation level. The proposed scheme efficiently manages the energy resource to enhance the battery behavior which extends the network lifespan in the long term.Simulations results show that the proposed approach out-performs similar works available in the current literature. The batteries life cycle and the network lifetime are improved by 38% and 47% respectively. The average number of generated clusters is reduced by 39%. |
| Databáze: | OpenAIRE |
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