Enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms

Kaliappan, M.; Augustine, Susan; Paramasivan, B.

doi:10.1016/j.jnca.2016.07.003

Cited by 33 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonetheless, the traditional algorithms have the disadvantages of inequality and premature convergence. Cheng et al [35] and Kaliappan et al [36] proposed to use dynamic genetic algorithms to solve the problem of load balancing clusters in mobile ad hoc networks. The former achieves dynamic optimization by integrating several immigrants, memory, multi-population schemes and their combinations into the standard GA.…”

Section: Related Workmentioning

confidence: 99%

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Zou

Min

2019

Applied Sciences

View full text Add to dashboard Cite

Featured Application: A distributed face recognition system can quickly handle a large number of face recognition tasks, but the problem of load imbalance may lead to time delay and sharp increase of CPU utilization. To deal with the problem, this paper proposes a dynamic load balancing method based on prediction, which can effectively alleviate the time delay and sharp increase of CPU utilization. Distributed face recognition can be widely used in many important research fields and industries such as public security, customs, finance, security. Also, the method proposed in this paper can help provide new ideas for solving task assignment and load balancing problems in other distributed systems such as speech recognition, fingerprint recognition, and vehicle identification.Abstract: With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.

show abstract

Section: Related Workmentioning

confidence: 99%

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Zou

Min

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…7(b) shows the simulation result in time 100. different UE at random time 100. This proposed scheme is compared with DLBCP [39], GABOC [33], EIGA [40], MEGA [40]. The comparison is made in terms of energy consumption, packet delivery ratio, throughput, and routing overhead over the network size of 50 to 200 UEs and their comparison is shown in table 2, 3, 4, and 5 respectively.…”

Section: Experiments Evaluation and Analysismentioning

confidence: 99%

Grid based Bio-Inspired Energy Efficient Trusted Hybrid Routing Protocol for MANET

M.*¹,

Phanikumar²

2019

IJRTE

View full text Add to dashboard Cite

Device to device (D2D) data communication in cellular wireless network can be done either directly from source equipment (SE) to destination equipment (DE) or through relay equipment (RE). This type of network mainly known as Wireless ad hoc networks. Mobile ad-hoc networks (MANETs) have constraints such as such as limited power, route failure, bandwidth allocation, and computational complexity. The major impact on the performance of network depends on routing strategy. Routing strategy for matching the multi constraints of network is so far challenge in conventional network. MANETs suffers from dynamic movement of node, power, dynamic routing and storage complexity. Dynamic movement force the computation of re-routing, which act as extra computational burden on the system. In this paper, we propose the Grid Cluster based Multi-Objective Genetic Algorithm for Energy Efficient Trusted Network (gCMOGAEETN) algorithm for finding optimal routes from a given SE to a given DE. Our simulation results show that gCMOGAEETN algorithms are efficient in solving these routing problems and are capable of finding the optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of user equipment (UEs) is higher than or equal to 50. The analytical and simulation result shows that proposed method exhibit significantly higher performance than optimal adaptive forwarding strategy (OAFS), sub-optimal adaptive forwarding strategy (SAFS), memory enhanced genetic algorithm (MEGA), Elitism-based Immigrants Genetic algorithm (EIGA), dynamic load-balanced clustering problem (DLBCP) and Genetic Algorithm Based Optimization of Clustering (GABOC)..

show abstract

“…23 It was critical to choose the energy-productive cluster set out toward keeping up the cluster structure and adjust the load viably. 23 It was critical to choose the energy-productive cluster set out toward keeping up the cluster structure and adjust the load viably.…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, MANET can likewise be divided into clusters for keeping up the network structure. 23 It was critical to choose the energy-productive cluster set out toward keeping up the cluster structure and adjust the load viably. In this work, it utilized dynamic genetic algorithms, eg, elitism-based immigrants genetic algorithm and memory-enhanced genetic algorithm to take care of dynamic load-adjusted clustering issue.…”

Section: Introductionmentioning

confidence: 99%

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

Gupta

Khanna

Lakshmanaprabu

et al. 2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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.

show abstract

Enhancing energy efficiency and load balancing in mobile ad hoc network using dynamic genetic algorithms

Cited by 33 publications

References 20 publications

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

Grid based Bio-Inspired Energy Efficient Trusted Hybrid Routing Protocol for MANET

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

Contact Info

Product

Resources

About