Search Results - (( using optimization path algorithm ) OR ( using based sensor algorithm ))*

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  1. 1

    Modelling of multi-robot system for search and rescue by Poy, Yi Ler

    Published 2023
    “…This report focusses on developing a novel multi-robot path planning algorithm based on the Modified Particles Swarm Optimization (MPSO) algorithm for dynamic environments. …”
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    Final Year Project / Dissertation / Thesis
  2. 2

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…These algorithms generate paths using random numbers and perform efficiently in guiding the robot in known environments. …”
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    Thesis
  3. 3

    Transmission path optimization Based on Efficiency Communication System by Jin Fan, Kit, Guan Lim, Sin, Helen Ee Chuo, Min, Keng Tan, Ali Farzamnia, Tze, Kenneth Kin Teo

    Published 2022
    “…Routing is the core of wireless sensor network deployment. This paper points out the research focus of the wireless sensor network transmission path and discusses the optimization problem of the transmission based on grid model. …”
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    Conference or Workshop Item
  4. 4

    Adaptive Algorithm for Optimal Route Configuration in Multi-Hop Wireless Sensor Network by Shabani, Hikma, Ahmed Mohamed, Ahmed Haidar, Norhuzaimin, Julai, Musse, Mohamud Ahmed, Hoole, P.R.P., Marai, Majdi

    Published 2017
    “…This paper proposes an optimal route configuration technique based on an adaptive genetic algorithm in which the architecture of multi-hop wireless sensor network is considered as a distributed computing infrastructure. …”
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    Article
  5. 5

    A new routing mechanism for energy-efficient in bluetooth mesh-low power nodes based on wireless sensor network by Almuslehi, Hussein Saad Mohammed

    Published 2023
    “…The multi-criterion energyefficient routing mechanism uses the ACO algorithm, inspired by ants' foraging behaviour. …”
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    Thesis
  6. 6

    Wireless sensor nodes deployment using multi-robot based on improved spanning tree algorithm by Arezoumand, Reza

    Published 2015
    “…Employing a multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor node deployment. …”
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    Thesis
  7. 7

    Smart geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia sensor networks by Hussein, Wael Ali, Mohd Ali, Borhanuddin, A. Rasid, Mohd Fadlee, Hashim, Fazirulhisyam

    Published 2021
    “…This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. …”
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    Article
  8. 8

    Metaheuristic optimization techniques for localization in outdoor wireless sensor networks: a comprehensive review by Gumaida, Bassam, Ibrahim, Adamu Abubakar

    Published 2025
    “…This paper serves as a comprehensive background on localization algorithms and methods used in wireless sensor networks, offering insights for researchers to develop efficient localization algorithms tailored to specific application requirements in diverse work environments.…”
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    Article
  9. 9

    Energy balancing mechanisms for decentralized routing protocols in wireless sensor networks by Saleh, Ahmed Mohammed Shamsan

    Published 2012
    “…Finally, we propose Self-Decision Route Selection scheme which is an improvement of the Hop-based Spanning Tree (HST) algorithm that is used in some routing protocols such as AODV and DSR. …”
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    Thesis
  10. 10
  11. 11

    Trust-based enhanced ACO algorithm for secure routing in IoT by Sharmin, Afsah, Motakabber, S M A, Hassan Abdalla Hashim, Aisha

    Published 2024
    “…In order to provide an energy-efficient and secure routing method for sensors deployed within a dynamic IoT network, this paper presents a trust-aware enhanced ant colony optimization (ACO)-based routing algorithm, incorporating a lightweight trust evaluation model. …”
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    Article
  12. 12

    Predetermined path of mobile data gathering in wireless sensor networks based on network layout by Ghaleb, Mukhtar, Subramaniam, Shamala, Othman, Mohamed, Ahmad Zulkarnain, Zuriati

    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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    Article
  13. 13

    Hierarchical gaussian reinforcement learning for path planning in uncertain environments by AlDahoul, Nouar, Htike@Muhammad Yusof, Zaw Zaw, Akmeliawati, Rini, Shafie, Amir Akramin

    Published 2015
    “…The simulation experimental results seem suggest the efficiency of the proposed algorithm in finding optimal paths of autonomous agents.…”
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    Article
  14. 14

    Dynamic area coverage algorithms for static and mobile wireless sensor network environments using voronoi techniques by Ceesay, Omar M.

    Published 2011
    “…The proposed Voronoi Tessellation-based Coverage Optimization Algorithms for Static and Mobile Wireless Sensor Networks provides up to 99% coverage at various number of mobile-static sensor node combination and up to 12% reduction in average moving distance. …”
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    Thesis
  15. 15

    A noble approach of ACO algorithm for WSN by Sharmin, Afsah, Anwar, Farhat, Motakabber, S. M. A.

    Published 2018
    “…A noble approach of ant colony optimization (ACO) algorithm for discovering the optimum route in the WSNs for data transmission is proposed here for enhancement and optimization considering the issue of path selection to reach the nodes. …”
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    Proceeding Paper
  16. 16

    Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network by Halim, Nurul Hamimi

    Published 2018
    “…However,the sink node position and size of data transmitted will not affect the performance of coverage area.This is because the coverage area value is fluctuated as the parameters value increases.Throughout the experiment conducted,sensor nodes deployed using Modified Harmony Search algorithm (MHS) gives better coverage area compared to other existing methods.The average coverage area percentage obtained by Modified Harmony Search is 63 %.The average coverage area percentage obtained by Modified Random is 48 % and the average coverage area percentage obtained by Harmony Search is 46 %.The highest coverage area recorded for Modified Harmony Search is 70 %.To enhance the energy efficiency,shortest path distance finder is added to each method.Throughout the research,Modified Harmony Search with shortest path distance finder gives optimum results.…”
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    Thesis
  17. 17

    Accurate range free localization in multi-hop wireless sensor networks by Abdulwahhab, Abdullah Raed

    Published 2019
    “…Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks. The performance is evaluated in terms of RMSE in terms of three algorithms WLS, CRLR, and GMSDP based on using the Monte Carlo simulation with account the number of anchors that varying from anchor=4 to anchor =20. …”
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    Thesis
  18. 18

    Using artificial intelligence search in solving the camera placement problem by Altahir, A.A., Asirvadam, V.S., Hamid, N.H.B., Sebastian, P.

    Published 2022
    “…Two case studies are used to evaluate those algorithms, and the camera placement problem is formulated as a coverage maximization problem. …”
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    Book
  19. 19

    A Feasible Architecture Of Real-Time Collision Avoidance And Path Planning For Semi-Autonomous Unmanned Ground Vehicle (Ugv) by Subramaniam, Purawin

    Published 2018
    “…Meanwhile, an Arduino Mega 2560 and ArduPilot Mega 2.6 (APM) are used as microcontrollers for obstacle avoidance and path optimization purpose respectively. …”
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    Monograph
  20. 20

    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

    Published 2013
    “…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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    Conference or Workshop Item