Abstract
In mobile ad hoc networks (MANETs), the topology differs very often due to mobile nodes (MNs). The flat network organization has high topology maintenance messages overload. To reduce this message overload in MANET, clustering organizations are recommended. Grouping MANET into MNs has the advantage of controlling congestion and easily repairing the topology. When the MANET size is large, clustered MN partitioning is a multiobjective optimization problem. Several evolutionary algorithms such as genetic algorithms (GAs) are used to divide MANET into clusters. GAs suffer from premature convergence. In this article, a clustering algorithm based on a memetic algorithm (MA) is proposed. MA uses local exploration techniques to reduce the likelihood of early convergence. The local search function in MA is to find the optimal local solution before other evolutionary algorithms. The optimal clusters in MANET can be achieved using MA for dynamic load balancing. In this work, the network is considered a graph G (V, E), where V represents MN and E represent the communication links of the neighboring MNs. The aim of this study is to find the cluster headset (CH) as early as possible when needed. High-quality individuals are selected for the new population in the next generation. New individuals are generated using the crossover mechanism on the chromosome once the two parents have been selected. Data are communicated via CHs between other clusters. The proposed technique is compared with existing techniques such as DGAC, MobHiD, and EMPSO. The proposed technique overcomes the state-of-the-art clustering schemes in terms of cluster counting, reaffiliation rate, cluster life, and overload of control messages.
Highlights
A wireless sensor network for the Internet of things mobile ad hoc networks (MANETs) is the set of mobile nodes (MNs) capable to share data with their neighbors. e MNs may generate their own data, or they may be received from other neighbors
Each MN is equipped with deaf omnidirectional antennas. e transmission range of each antenna differs in radius from 100 m to 300 m
E continuous bit rate was used to generate the origins of the traffic. e threshold used for generating traffic was 20 packets per second. e simulations were run for 50 minutes. e average of 100 simulation runs is represented as graphs
Summary
A wireless sensor network for the Internet of things MANET is the set of MNs capable to share data with their neighbors. e MNs may generate their own data, or they may be received from other neighbors. A wireless sensor network for the Internet of things MANET is the set of MNs capable to share data with their neighbors. MANET permits to construct a shortterm MANET for instant communication deprived of some fixed structures. MANET may be used for managing different applications, for example, rescue, flood monitoring, border monitoring, disaster management, and battle field communication. Complexity perform well when the MANET size becomes larger compared to flat MANET regardless of routing method adopted [4]. E scalability issue in flat MANET is very critical with a large number of node, and the nodes are moving in some directions. Once the number of MNs in MANET with flat routing structure is x, the complexity of proactive routing scheme will be O (x2) [5]. E classic implementation of hierarchal design is the clustering structure. To accomplish elementary performance assurance in sized MANET, a hierarchal organization is mandatory [6]. e classic implementation of hierarchal design is the clustering structure. e selection of optimal CHs is an NP-hard problem [7]
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