Search Results - (( based optimization based algorithm ) OR ( based selection sensor algorithm ))
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Cluster head selection optimization in wireless sensor network via genetic-based evolutionary algorithm
Published 2020“…Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. …”
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An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks
Published 2015“…In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. …”
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An improved energy-efficient clustering protocol to prolong the wireless sensor network lifetime
Published 2021“…Lastly, cluster heads (CHs) are selected in optimal locations with the rotation of the CH function among members of the cluster based on a new CH selection-rotation algorithm by combining a back-off timer mechanism for CH selection and rotation mechanism for CH rotation. …”
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A new routing mechanism for energy-efficient in bluetooth mesh-low power nodes based on wireless sensor network
Published 2023“…Compared to the Ant Colony Optimization-Genetic Algorithm (ACO-GA) and Ant Colony Optimization- Hierarchical Clustering Mechanism (ACOHCM), the ACO algorithm shows superior power savings and efficiency. …”
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Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network
Published 2020“…Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. …”
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Mobile data gathering algorithms for wireless sensor networks
Published 2014“…These mobile elements will go to each transmission range of each respective sensor to upload its data. In this research, a Mobile Data Gathering based Network Layout (MDG-NL) algorithm is proposed. …”
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A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network
Published 2016“…Simulation results show convergence, learning and adaptability of the RL based algorithms to dynamic environment toward achieving the optimal solutions. …”
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Dynamic positioning base station for wireless sensor network using particle swarm optimization (PSO)
Published 2012“…Since the optimal selection of base station location in a network belongs to nondeterministic polynomial (NP) hard problem, the use of approximation algorithms such as Particle Swarm Optimization (PSO) are generally more suitable due to its simplicity and outstanding search strength.…”
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10
A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments
Published 2013“…The motion planning problem poses the question of how a robot can move from an initial to a final position. Sampling-based motion planning is a class of randomized path planning algorithms with proven completeness. …”
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A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains
Published 2023“…Sensor-based motion planning is one the most challenging tasks in robotics where various approaches and algorithms have been proposed to achieve different planning goals. …”
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Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours
Published 2024“…Then, the enhancement clustering algorithm will be constructed based on correlated degree which is selected as a cluster head to serves as a link connectivity between individual node and its neighbor to form network clustering. …”
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Using artificial intelligence search in solving the camera placement problem
Published 2022“…The placement results obtained based on those algorithms are critically compared in terms of the algorithmsâ�� efficiency and performance. …”
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Survey on Clustered Routing Protocols Adaptivity for Fire Incidents: Architecture Challenges, Data Losing, and Recommended Solutions
Published 2025“…Thus, developing an effective routing algorithm to optimize network functionality is a big concern. …”
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A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective
Published 2013“…Machine learning algorithms are iteration based algorithms, as the new knowledge is based on the previous predicted /calculated knowledge which helps to decrease errors in order to increase efficiency. …”
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Predetermined path of mobile data gathering in wireless sensor networks based on network layout
Published 2014“…There are two prevailing strategies used to collect data in sensor networks. The first approach requires data packets to be serviced via multi-hop relay to reach the respective base station (BS). …”
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Autonomous flight algorithm of a quadcopter sensing system for methane gas concentration measurements at landfill site
Published 2018“…During the experimentation, accurate methane gas concentration measurements at landfill sites were obtained using the algorithm for autonomous flight, with the implementation of optimal quadcopter flight parameters and gas sensor mounting arrangements. …”
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Data transmission in wireless sensor network with greedy function and particle swarm optimization
Published 2019“…As distances affect greatly on the energy consumption, Particle Swarm Optimization (PSO) is developed to replace greedy algorithm in PEGASIS to reduce the distances of data transmission. …”
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Proceedings -
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A novel bio-inspired routing algorithm based on ACO for WSNs
Published 2019“…A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. …”
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An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
Published 2017“…Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. …”
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