A memetic algorithm for optimal dynamic design of wireless sensor networks

2010 ◽  
Vol 33 (2) ◽  
pp. 250-258 ◽  
Author(s):  
Konstantinos P. Ferentinos ◽  
Theodore A. Tsiligiridis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Memetic Algorithm ◽  
Wireless Sensor ◽  
Dynamic Design ◽  
Optimal Dynamic

Distributed optimal dynamic base station positioning in wireless sensor networks

2012 ◽  
Vol 56 (1) ◽  
pp. 34-49 ◽  
Author(s):  
P.D. Hossein Zadeh ◽  
C. Schlegel ◽  
M.H. MacGregor
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Base Station ◽  
Wireless Sensor ◽  
Optimal Dynamic

scholarly journals Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: memeWSN

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Masood Ahmad ◽  
Babar Shah ◽  
Abrar Ullah ◽  
Fernando Moreira ◽  
Omar Alfandi ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Memetic Algorithm ◽  
State Of The Art ◽  
Wireless Sensor ◽  
The Internet ◽  
Node Mobility ◽  
Clustering Methods ◽  
The Internet Of Things

In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.

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