An Adaptive Activation Scheduling Strategy for a Border Surveillance Network

Laouira, Mohamed Lamine; Abdelli, Abdelkrim; Othman, Jalel Ben; Kim, Hyunbum

doi:10.1109/globecom38437.2019.9013541

Cited by 2 publications

(2 citation statements)

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“…This monitoring has become more important due to the increase in activities such as terrorist infiltration and illegal movement. In [1] current border surveillance approaches have been classified into five main categories, namely: i. Wired sensors-based technologies (such as fiber optic sensors); ii.…”

Section: ) Border Surveillance Systemsmentioning

confidence: 99%

“…The system has been designed to be able to manage a swarm of UAV. 8) "Solutions for the implementation of a sensors network for border surveillance", [11] In this thesis, the researcher proposed a border control system with a multi-layer framework that detects and tracks any crossborder entry with minimal human intervention based on a combination of several technologies such as multimedia sensors, radars, and drones, and presented a detailed deployment scheme for all layers of the proposed structure. To save energy, load balancing and eliminate redundancy, with an activation scheduling strategy, the contributions of this thesis were evaluated through a simulation process.…”

Section: ) "Cost-effective Real-time Aerial Surveillance System Using...mentioning

confidence: 99%

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Design a border Surveillance System based on Autonomous Unmanned Aerial Vehicles (UAV)

2023

IJSER

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After the spread of autonomous driving systems in cars, the next step is the development of autonomous drone systems. This will have an application in many military and civil fields, including surveillance and photography instead of satellites, as well as in search and rescue, dissemination, aid and distribution of goods, as it can cover large areas and save effort and human cost. The goal of this paper is to discuss using autonomous drones to design a border surveillance system. Where the current surveillance systems were studied to discuss the possibility of using drones as an alternative to long-range cameras, and a design proposal was presented that works on three different levels of surveillance systems. The possibility of controlling the drone through a single-board computer that is installed on the control tower with the base of the charging station for the drone was also discussed. In order to achieve the required balance between the speed of controlling the drone and the accuracy of object detection and classification, the concept of parallel processing was applied with the fast algorithms for tracking and detection (contour detection) and the high accuracy deep learning model (YOLOv8n) for detection and classification , which was trained on a customized dataset to improve the accuracy of object detection and classification in images taken from different view angles of the drone. After testing the experimental model of the system, the results showed a good enhancement in the quality of the captured images, as well as a solution to the problem of occlusion or blurring of objects in the image. Also, the results confirmed the possibility of relying on a single-board computer in processing images and controlling the drone to track moving targets in a way that was not possible by relying on transferring images to remote central processing.

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Section: ) Border Surveillance Systemsmentioning

confidence: 99%

Section: ) "Cost-effective Real-time Aerial Surveillance System Using...mentioning

confidence: 99%