An optimal and secure resource searching algorithm for unstructured mobile peer-to-peer network using particle swarm optimization

Kumar, Dharmendra; Pandey, Mayank

doi:10.1007/s10489-022-03291-z

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…In recent years, swarm intelligence algorithms such as ABC have been applied to load-balancing problems in cloud computing. For instance, [25,26] [26].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Kumar¹

2023

Preprint

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This paper uses an artificial bee colony algorithm to present a QoS-oriented technique for load balancing in green cloud computing. The proposed technique aims to optimize workload allocation decisions while minimizing energy consumption and carbon emissions and ensuring QoS requirements are met. The technique involves a fitness function that evaluates the fitness of each solution based on a set of QoS and energy consumption metrics. It also involves the ABC algorithm, which generates novel solutions and explores the search space. The algorithm also applies a dynamic threshold-based approach to adjust the threshold values based on the current system load and QoS requirements. Experimental results demonstrate that the proposed technique outperforms other load-balancing techniques regarding QoS and energy consumption metrics. Compared to other load-balancing techniques, the new technique significantly improved response time, throughput, availability, resource utilization, energy consumption, and carbon emissions. The proposed technique is likely to improve energy efficiency, reduce cloud computing's carbon footprint, and meet quality of service requirements.

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“…In recent years, swarm intelligence algorithms such as ABC have been applied to load-balancing problems in cloud computing. For instance, [25,26] [26].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Kumar¹

2023

Preprint

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“…The individual optimum of the ith particle is marked as P=[Pn, P2.... Pa]; the other extreme value is the optimal solution currently found by the whole population, which is called "global extreme value" or "global optimum (gbest)". In addition, it is also possible to use a part of the whole population as the neighbor of the particle instead of the whole population, so the best of all neighbors is the local extreme value [17][18]. When these two optimal values are found, particles update their speed and new position according to the following formula:…”

Section: Psomentioning

confidence: 99%

Electromechanical Integration Construction of Automobile Inspection Equipment Based on Particle Swarm Optimization

2020

KME

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Based on particle swarm optimization (PSO), this paper studies the mechatronics design method of automobile inspection equipment. Firstly, the basic theoretical knowledge of CANSYB and its operation principle are introduced. Secondly, the PSO algorithm is analyzed, improved and verified for applicability, and the effective parameters are selected as test variables and combined with the objective function to build a complete system structure model. Finally, the real-time dynamic performance of the vehicle monitoring equipment under the simulated experimental environment is realized on the Matlab platform using MATLAB software, so as to obtain the required data. The test results show that the improved PSO algorithm can effectively save the material cost, and the stator copper consumption of small three-phase asynchronous motor can be saved by 8% on average; the silicon steel consumption is saved by 3% on average. At the same time, the improved PSO algorithm not only reduces the number of iterations in the optimization process, but also has a strong distributed ability. At the same time, the random search nature of the PSO algorithm makes the function not easy to fall into local optimization, so as to ensure that the best optimization effect can be achieved.

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“…The benefits of this model is programming simplicity, and lesser deviation between the observed and evaluated values. PSO algorithm is a simple and fast optimizer that can solve real-world optimization issues [5]. This algorithm improves the efficiency of searching and it minimizes the network overhead.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

Mahalakshmi¹,

Srinivasan²,

Aghalya³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This work aims to predict link failure and improve routing efficiency in MANET. The Fuzzy Linear Regression Method (FLRM) measures the long lifespan link based on the link failure. The mobile node group is built by the Received Signal Strength (RSS). The Hill Climbing (HC) method selects the Group Leader (GL) based on node mobility, node degree and node energy. Additionally, it uses a Data Gathering node forward the information from GL to the sink node through multiple GL. The GL is identified by linking lifespan and energy using the Particle Swarm Optimization (PSO) algorithm. The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

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An optimal and secure resource searching algorithm for unstructured mobile peer-to-peer network using particle swarm optimization

Cited by 12 publications

References 51 publications

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Electromechanical Integration Construction of Automobile Inspection Equipment Based on Particle Swarm Optimization

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

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