| Title |
A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city |
| ID_Doc |
36859 |
| Authors |
Singh, S; Garg, D; Manju; Malik, A |
| Title |
A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.micpro.2023.104892 |
| Abstract |
The Internet of Things (IoT) is a revolutionary development that facilitates the instant communication of data among devices, making it an essential paradigm in today's world. It enables the development of smart infrastructure and provides assistance in emergency services during disasters. However, there is a growing need to develop energy-conserving paradigms that cover all aspects of a smart city. In this work, the authors present a hybrid genetic algorithm (GA) inspired greedy strategy-based mutation operation for IoT-enabled heterogeneous wireless sensor networks (WSNs) distributed architecture in a technology driven city. The proposed protocol aims to design a weighted fitness function that considers four parameters: node density, residual and average energy, and distance parameters. Additionally, a 3-tier heterogeneity is considered to prolong the network functional duration, and a deployment strategy is addressed to position these nodes in an energy-efficient manner. The proposed algorithm achieves 31.41%, 20.42%, 13.37%, 1.59% increase in network lifespan compared to GA-based optimized clustering (GAOC), optimized GA with single sink (OptiGAS-StSS), multiple sinks (MS) based GAOC and optimized GA with multiple sink (OptiGAS-StMS), respectively. The significant enhancement in network lifespan is an outcome of choosing the most efficient CH nodes based on the proposed fitness function. Overall, the proposed protocol contributes to developing an energy-conserving paradigm for a smart city. |
| Author Keywords |
Genetic algorithm; Greedy mutation; Heterogeneous WSNs; IoT; Load balancing; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001044057000001 |
| WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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