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Ms. Amjaad Alhaqbani
Researcher at Space and Aeronautics Research Institute, King Abdulaziz City for Science and Technology (KACST)

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Research Keywords & Expertise

0 Localization
0 Remote Sensing
0 task allocation
0 satellite and UAV remote sensing
0 Bio-inspired algorithms

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Journal article
Published: 23 December 2020 in Remote Sensing
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The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

ACS Style

Amjaad Alhaqbani; Heba Kurdi; Kamal Youcef-Toumi. Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions. Remote Sensing 2020, 13, 27 .

AMA Style

Amjaad Alhaqbani, Heba Kurdi, Kamal Youcef-Toumi. Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions. Remote Sensing. 2020; 13 (1):27.

Chicago/Turabian Style

Amjaad Alhaqbani; Heba Kurdi; Kamal Youcef-Toumi. 2020. "Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions." Remote Sensing 13, no. 1: 27.

Conference paper
Published: 01 March 2012 in Third International Conference on Communications and Networking
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RSS-based localization is one of the most predominant practical techniques for localization in Wireless Sensor Networks (WSNs). However, it is known to be inaccurate due to high RSS variability. In this paper, we experimentally analyze and illustrate the problem of RSS-based localization in WSNs, and we propose a simple Kalman-Filter smoothing technique to reduce RSS variability for the sake of improving the localization accuracy. To evaluate its performance, we investigate our proposed Kalman Filter and a Moving Average Filter to devise a mapping between Smoothed RSS and distance. We show that the localization error is almost less with Kalman Filter than with Moving Average Filter.

ACS Style

Anis Koubâa; Maissa Ben Jamâa; Amjaad Alhaqbani. An empirical analysis of the impact of RSS to distance mapping on localization in WSNs. Third International Conference on Communications and Networking 2012, 1 -7.

AMA Style

Anis Koubâa, Maissa Ben Jamâa, Amjaad Alhaqbani. An empirical analysis of the impact of RSS to distance mapping on localization in WSNs. Third International Conference on Communications and Networking. 2012; ():1-7.

Chicago/Turabian Style

Anis Koubâa; Maissa Ben Jamâa; Amjaad Alhaqbani. 2012. "An empirical analysis of the impact of RSS to distance mapping on localization in WSNs." Third International Conference on Communications and Networking , no. : 1-7.