Efficient artificial fish swarm based clustering approach on mobility aware energy‐efficient for MANET

Abstract

Mobile ad hoc network (MANET) is a gathering of self‐ruling mobile nodes in which ad hoc network framed without settled foundation. Dynamic topology property of MANET may debase the execution of the network, mobility attentiveness, and energy effectiveness because of an ideal cluster head (CH). The low energy adaptive clustering hierarchy protocol is used in the MANETs for enhancement of network life time by proficiently utilizing of the accessible constrained energy. Artificial fish swarm optimization is used to select the CH in MANET communication process. The election of CH takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster for longer duration of time. From the implementation results, it is observed that the proposed method provides the maximum network life time as 80 to 140 hours and minimum energy consumption as 9 to 130 J, compared with existing protocol and optimization technique.

Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks

“…Update wolves position. To attain the preys, every wolf has to know its place based on the location of delta, beta and alpha wolves are template in equations (7) and (8). In GWO-dependent method, alpha wolves are the wide resolution in the solution put; the beta wolves are the optimal solutions from the before iteration; and the delta wolves are the optimal solution from the present iteration.…”

“…Energy conserving could be enhanced and maintained by adapting the network topology and modifying the sensors transmitting energy level in router. 8,9 Clustering model is applied for decreasing the utilization of power in routing protocols. 10 This architecture contains the sensors which is grouped as clusters, the sensor nodes having minimum power are obtained to execute sensing operation, and transmit the data which have undergone sensing to their cluster head (CH) in small distance.…”

Reliable Multi-Object Tracking Model Using Deep Learning and Energy Efficient Wireless Multimedia Sensor Networks

“…Numerous target tracking models have been developed to handle the obstacles that exist in the real-time deployment [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. In [32], a particle swarm optimization (PSO) based object tracking technique is developed for image sequences.…”

“…Once the clusters are formed, every node will execute the RNN-T algorithm to track the animals efficiently. After some predefined rounds of the tracking process, the captured video will be sent by the cluster members to CHs [18]. Then, the CHs will transfer data to BS via intermediate CHs.…”

Machine Learning/AI for IoT, M2M, and Computer Communication

“…Khuntia and Hazra took leverage of deep q‐learning to propose a resource sharing scheme to solve channel and power allocation problems in article 4, “An efficient Deep reinforcement learning with extended Kalman filter for device‐to‐device communication under‐laying cellular network.” Another use of q‐learning with reinforcement learning to enhance connectivity in D2D is stated in the fifth paper, “Reinforcement learning algorithm for 5G indoor device‐to‐device communications” by Sreedevi and Rama Rao . The sixth paper, “Efficient artificial fish swarm based clustering approach on mobility aware energy‐efficient for MANET” by Gupta et al, uses artificial fish swarm optimization to increase the network lifetime of the network.…”