| Title |
CFMCRS: Calibration fuzzy- metaheuristic clustering routing scheme simultaneous in on-demand WRSNs for sustainable smart city |
| ID_Doc |
42145 |
| Authors |
Fanian, F; Rafsanjani, MK |
| Title |
CFMCRS: Calibration fuzzy- metaheuristic clustering routing scheme simultaneous in on-demand WRSNs for sustainable smart city |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.eswa.2022.118619 |
| Abstract |
Achieving a sustainable state in smart cities would require ongoing monitoring and evaluation of all activities in different dimensions. Hence, it is essential to use smart systems and adaptable, reliable, and controllable schemes in all dimensions of smart cities. This paper focuses on the on-demand wireless rechargeable sensor networks (WRSNs) to allow for ongoing and sustainable monitoring and provide application-based services matching goals, conditions, and the environment within in smart cities. Overcoming the challenge of energy limitation, WRSNs are more reliable than wireless sensor networks (WSNs). Hence, the paper proposes a calibration fuzzy-metaheuristic clustering routing scheme (CFMCRS) for on-demand WRSNs. The proposed CFMCRS benefits from resource-saving and energy supplementary techniques in addition to using metaheuristic and fuzzy logic methods to manage roles and energy distribution in nodes and across the network. It also uses a multiobjective function to match and calibrate the network with the nearest-job-next with preemption (NJNP) charging scheduler to meet WRSN requirements in smart cities. The extensive simulation results of the proposed CFMCRS were compared with the state-of-the-art methods in terms of energy efficiency, latency, minimum sustainability rate, dead rate, survival rate, mean energy of network, number of packets received by base station (BS), request rates, and sensitive analysis. Accordingly, the proposed scheme outperformed the other methods by far for application requirements and improved evaluation factors for application. Ultimately, it can prolong the WRSN lifetime. The simulation results were also validated through ANOVA and its subsequent post-hoc analysis. |
| Author Keywords |
Sustainable smart cities; Wireless rechargeable sensor networks (WRSN); Whale optimization algorithm (WOA); Fuzzy inference system |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001025774200001 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
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