Search Results - (( mobile location sensor algorithm ) OR ( evolution optimization path algorithm ))

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  1. 1
    The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment

    The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment by Kok, Kai Yit

    Published 2016
    “…Significant research has been conducted on Unmanned Aerial Vehicle (UAV) path planning using evolutionary algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Biogeographic-Based Optimization (BBO). …”
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  2. 2
    Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking

    Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking by Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

    Published 2024
    “…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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  3. 3
    Path optimization using genetic algorithm evolution

    Path optimization using genetic algorithm evolution by Low C.Y., Chong K.H., Salleh K., Johnny K.S.P.

    Published 2023
    Conference paper
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  4. 4
    A coalition model for efficient indexing in wireless sensor network with random mobility / Hazem Jihad Ali Badarneh

    A coalition model for efficient indexing in wireless sensor network with random mobility / Hazem Jihad Ali Badarneh by Hazem Jihad , Ali Badarneh

    Published 2021
    “…First, the high dependability on multi-attributes (location and time) of packets in random mobile sensors. …”
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  5. 5
    Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim

    Finding minimum path by using Genetic Algorithm (GA)/ Siti Zuraifah Hashim by Hashim, Siti Zuraifah

    Published 2007
    “…Roadways and telephone systems are the examples of them. Genetic Algorithms (GA), pioneered by John Holland, applies the principle of evolution found in nature to the problem of finding an optimal solution. …”
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  6. 6
    Dynamic area coverage algorithms for static and mobile wireless sensor network environments using voronoi techniques

    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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  7. 7
    Differential evolution optimization for constrained routing in Wireless Mesh Networks

    Differential evolution optimization for constrained routing in Wireless Mesh Networks by Sanni, Mistura Laide, Hassan Abdalla Hashim, Aisha, Hassan, Wan Haslina, Ahmed, Gharib Subhi Mahmoud, Anwar, Farhat, Zakaria, Omar

    Published 2014
    “…This solution addresses efficient and optimal routing path construction for cost and quality metrics of the application. …”
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  8. 8
    Collaborative Location-Based Mobile Game with Error Detection Algorithm

    Collaborative Location-Based Mobile Game with Error Detection Algorithm by Adrus, Mohamad Tazuddin, Wong, Ming Ming, Abang Mohamad Aizuddin, Abang Mohd Mohtar

    Published 2018
    “…From just using buttons, players can now interact with games through a wider spectrum of inputs which includes touch screen, camera, light sensor, accelerometer, compass and GPS. This is driven by the availability of these modules and sensors within mobile devices that are omnipresent nowadays. …”
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  9. 9
    Mobile data gathering algorithms for wireless sensor networks

    Mobile data gathering algorithms for wireless sensor networks by Ghaleb, Mukhtar Mahmoud Yahya

    Published 2014
    “…Minimal Constrained Rendezvous Node (MCRN) algorithm is designed to ensure that the number of pause locations for the mobile element is minimized. …”
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  10. 10
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  11. 11
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  12. 12
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  13. 13
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  14. 14
    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

    Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields by Xu, Y., Choi, J., Dass, S., Maiti, T.

    Published 2013
    “…Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. …”
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  15. 15
    An energy efficient simultaneous-node repositioning algorithm for mobile sensor networks

    An energy efficient simultaneous-node repositioning algorithm for mobile sensor networks by Khan, M.A., Hasbullah, H., Nazir, B., Khan, I.A.

    Published 2014
    “…A novel algorithm for simultaneous-node repositioning is introduced. …”
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  16. 16
    Autonomous Fire Fighting Mobile Platform

    Autonomous Fire Fighting Mobile Platform by Teh, Nam Khoon, Saman, Abu Bakar Sayuti, Sebastian , Patrick

    Published 2012
    “…The tasks for the AFFMP once it navigates out of the patrolling route include the obstacle avoidance, locating for more precise location of fire source using front flame sensor and extinguish the fire flame. …”
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  17. 17
    Developing stereo vision system for object recognition and positioning of AMAD-R mobile robot

    Developing stereo vision system for object recognition and positioning of AMAD-R mobile robot by Mohd Saifizi, Saidon, Hazry, Desa, Paulraj, Murugesa, Mohd Sofian, M. Rosbi

    Published 2009
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  18. 18
    Simulation and Design of an Intelligent Mobile Robot for Fire Fighting

    Simulation and Design of an Intelligent Mobile Robot for Fire Fighting by Chor, Keong Seng

    Published 2003
    “…Keywords: (Fuzzy logic control, mobile robot, part recognition algorithms)…”
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  19. 19
    Making location-aware computing working accurately in smart spaces

    Making location-aware computing working accurately in smart spaces by Mantoro, Teddy, Ayu, Media Anugerah, Weyn, Maarten

    Published 2011
    “…WiFi, GPS, GSM and Accelerometer. The algorithm is based on opportunistic localization algorithm and fuse different sensor data in order to be able to use the data which is available at the user position and processable in a mobile device.…”
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  20. 20
    Literature Review of Optimization Techniques for Chatter Suppression In Machining

    Literature Review of Optimization Techniques for Chatter Suppression In Machining by A. R., Yusoff, Mohamed Reza Zalani, Mohamed Suffian, Mohd Yusof, Taib

    Published 2011
    “…Various algorithms can be applied in the optimization of machining problems; however, Differential Evolution is the most appropriate for use in chatter suppression, being less time consuming, locally optimal, and more robust than both Genetic Algorithms, despite their wide applications, and Sequential Quadratic Programming, which is a famous conventional algorithm.…”
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