A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing

Prakash, B.; Jayashri, S.; Karthik, T. S.

doi:10.1007/s00500-019-03789-8

Cited by 20 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other mechanisms include software-defined energy-aware routing protocol [13], [14], admission control for preventing fast battery depletion [15], operating certain nodes in sleep mode [16], limiting certain router functions for a period of a time [17], [18], energy aware multi-layer design [19] and topology control mechanism [20], [21]. For channel management, a dynamic channel assignment for wireless mesh network [22] or implementing a genetic algorithm and neural network to optimize energy [23] are also presented.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

Energy-aware wireless mesh network deployment using optimization mechanism

2023

View full text Add to dashboard Cite

Wireless mesh networks are widely used to create network infrastructure in rural areas due to their flexible properties, such as self-healing mechanisms and associated redundant paths. For example, a wireless mesh network can suitably operate with limited battery power in wildlife monitoring applications. The locations of mobile routers to support mobile sensor nodes are essential for extending the system lifetime. This study proposes an energy-aware wireless mesh network deployment optimization mechanism. The goal is to determine a suitable location for the mesh routers with the aim of maximizing network lifetime. After the location solution is obtained from the proposed mechanism, it is then evaluated for system lifetime and network performance by the network simulator (ns-3). The proposed method outperforms the brute-force method in terms of computation time for all amounts of mesh clients. For example, for 30 mesh clients, the proposed method uses only a few minutes, while the brute-force mechanism requires more than 200 minutes to complete the process. Furthermore, compared to the brute-force method, it achieves nearly the same system lifetime and other performance parameters, such as throughput, packet delivery ratio, and packet inter-arrival time. In the real implementation, in which the sensor node placements can be changed during the installation period owing to the environmental status or the recommendation of the installers, the results can be recalculated in a short period.

show abstract

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

Energy-aware wireless mesh network deployment using optimization mechanism

2023

View full text Add to dashboard Cite

show abstract

“…e improved fuzzy logic algorithm is applied to vehicle and UAV path planning, which improves the stability and real-time performance of the path [22]. Now, researchers have proposed many hybrid algorithms based on the coevolution strategy [23], such as hybrid genetic artificial neural network algorithm [24] and hybrid Genetic and Fuzzy logic algorithms [25]. Ravankar et al [26] proposed a hybrid potential based probabilistic roadmap algorithm, which improved the efficiency of the algorithm and can generate paths in narrow channels.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Song

Yang

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

show abstract

“…In [25] a study on the efforts that have been made to compensate the losses of packets is presented. Methodology for doing the use of a 2.4 GHz network of 18 nodes with a full range of physical link quality indicators is studied in [26]. Minimization of energy with a five-stage neural network and a genetic algorithm is proposed in [27].…”

Section: Related Workmentioning

confidence: 99%

Communication system improvement with control performance based on link quality in wireless sensor actuator networks

Tawfeeq

Mahmood

2021

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">New communication and networking paradigms started with wireless sensor actuator networks (WSANs) to introduce new applications. One of these is the automatic gain control system (AGC). It will enable a high degree of the decentralized and mobile control. In this study, neural networks (NN) with fuzzy logic (one of the techniques of artificial intelligence (AI)) is used to enhance the control performance depending on the link quality. The NN and fuzzy inference system (FIS) with Mamdani’s method used to build a model reference, adaptive controller, for recompensing for delay time packets losses, and improving the reliability of WSAN. Between 88.62% and 99.99%, validation data is obtained for the medium and high conditions of operation with the proposed algorithm. Experimental and simulation results show a promising approach.</span>

show abstract

A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing

Cited by 20 publications

References 15 publications

Energy-aware wireless mesh network deployment using optimization mechanism

Energy-aware wireless mesh network deployment using optimization mechanism

Intelligent Optimization Algorithm‐Based Path Planning for a Mobile Robot

Communication system improvement with control performance based on link quality in wireless sensor actuator networks

Contact Info

Product

Resources

About