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

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Psomentioning

confidence: 99%

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

2020

KME

View full text Add to dashboard Cite

show abstract

“…The simplicity and effectiveness of this approach led to its selection. In order to maximize both exploitation and exploration, the suggested method incorporates the first enhancement type of pivoting (local searching) and the crow heuristic algorithm [9,10].…”

Section: Introductionmentioning

confidence: 99%

Security-Aware Resource Allocation in Fog Computing Environments

Kumar

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Fog computing, an innovative approach building upon cloud technology, taps into local user resources to enhance services. It boasts cost-effectiveness, improved security, and reduced network delays, making it a popular choice for real-time applications. However, due to the heterogeneity of fog devices, resource allocation and scheduling pose challenges. This paper introduces a novel solution using a crow-inspired search mechanism and a multi-objective evolutionary approach for fog computing environments. The primary objectives are to optimize security and success rates simultaneously. A local search method enhances the performance of the crow search algorithm (CSA). The proposed method utilizes evolutionary techniques to allocate and schedule resources effectively. We compared our hybrid CSA with the original CSA and Genetic Algorithm (GA) across seven scenarios with varying parameters. Our hybrid CSA consistently outperformed existing methods, demonstrating its effectiveness in achieving the desired objectives.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

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

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

Security-Aware Resource Allocation in Fog Computing Environments

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

Contact Info

Product

Resources

About