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Mobile ad hoc networks (MANETs) are self-organized networks without any fixed infrastructure. The topology changes are very frequent in MANETs due to nodes' mobility. The topology maintenance creates an extra overhead, as the mobility information of a single node is shared with all nodes in the network. To address the topology maintenance overhead problem in MANETs, the researchers proposed different cluster-based algorithms to reduce the size of a routing table. The clusters are formed to locally adjust the topology changes within the cluster. If a node wants to communicate with a node outside the cluster, it only communicates with its cluster head (CH). The CH communicates with other CHs to transmit data toward the destination. To efficiently utilize the clustering mechanism in MANETs, stable and balanced clusters are required. To form good quality and optimized clusters, some metrics, such as relative mobility (node speed and direction), node degree, residual energy, communication workload, and neighbor's behavior, are required. In this paper, we present a comprehensive survey of recent CAs in MANETs. We also present the objectives, goals, and contributions of recent research. Similarly, the findings, challenges, and future directions are stated. The validation of each proposed work is analyzed critically in terms of the mobility model, the simulation tool used during simulation, simulation metrics, and the performance metrics used in the validation process. INDEX TERMS MANETs, clustering algorithms, routing, cluster based MANET.

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In mobile ad hoc networks, topology changes very frequently due to node's mobility. Frequent change in topology increases traffic signaling that may arise energy and scalability issue. Cluster-based routing is the energy-efficient technique in mobile ad hoc networks to address the scalability issue and to minimize control messages. In this article, honey bee algorithm is used for dividing the mobile ad hoc network nodes into different clusters. The bees work to gather in groups to perform their activities. The proposed honey bee algorithm-based clustering forms clusters in an efficient manner with fewer resources such as energy and bandwidth utilization. A node is selected as cluster head based on node degree, neighbor's behavior, mobility direction, mobility speed, and remaining energy. Due to the efficient nature of bees and maximum parameter's consideration, the proposed technique inspired from the foraging behavior of honey bees gives efficient and stable cluster formation. The control message overhead is also avoided. The work is validated mathematically, and simulation has been performed for different scenarios. Simulation results are compared with existing clustering schemes. The simulation results show that the honey bee algorithm-based clustering technique used for clustering outperforms the existing schemes under consideration.

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