Meta-Heuristic MOALO Algorithm for Energy-Aware Clustering in the Internet of Things
| Autor: | R. Lokeshkumar, Ravi Kumar Poluru |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Routing protocol
Boosting (machine learning) Fitness function Computer science 020209 energy 02 engineering and technology Energy consumption Computer Science Applications Computational Theory and Mathematics Artificial Intelligence Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Cluster analysis Throughput (business) Algorithm Data transmission |
| Zdroj: | International Journal of Swarm Intelligence Research. 12:74-93 |
| ISSN: | 1947-9271 1947-9263 |
| DOI: | 10.4018/ijsir.2021040105 |
| Popis: | Boosting data transmission rate in IoT with minimized energy is the research issue under consideration in recent days. The main motive of this paper is to transmit the data in the shortest paths to decrease energy consumption and increase throughput in the IoT network. Thus, in this paper, the authors consider delay, traffic rate, and density in designing a multi-objective energy-efficient routing protocol to reduce energy consumption via the shortest paths. First, the authors propose a cluster head picking approach that elects optimal CH. It increases the effective usage of nodes energy and eventually results in prolonged network lifetime with enhanced throughput. The data transmission rate is posed as a fitness function in the multi-objective ant lion optimizer algorithm (MOALOA). The performance of the proposed algorithm is investigated using MATLAB and achieved high convergence, extended lifetime, as well as throughput when compared to representative approaches like E-LEACH, mACO, MFO-ALO, and ALOC. |
| Databáze: | OpenAIRE |
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