Yearly Archive:2017

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Starting the prototyping phase

It is one of the most exciting phases in any R&D project, the start of the development of the prototype that will be used to proof and demonstrate the findings of the project. In DroneITS project, we have planned for this activity to start as soon as possible (just a year after the kickoff of  the project), to make sure we have a reliable and a stable test-bed before the closure of the project, by end of 2019.

Below is a photo of two colleagues (Miss. Nour Alsahan and Mr. Ahmed Abuzrara) working on the development and integration of the first DroneITS’s UAV, and the work is progressing very well. We hope to have a first version of the prototype ready for a demonstration in a month.

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Project’s Outcomes Presented at VTC Spring 2017

Our paper entitled “On the Placement of UAV Docking Stations for Future Intelligent Transportation Systems” has been presented by Dr. Hamid Menouar at the workshop on Positioning Solutions for Cooperative Intelligent Transportation Systems which was hold in conjunction with the IEEE Vehicular Technology Conference (VTC-Spring 2017), Sydney, Australia, June, 2017 (http://www.ieeevtc.org/vtc2017spring/).

The abstract of the presented paper is below:

Unmanned Aerial Vehicles (UAV) have attracted a lot of attention in a variety of fields especially in intelligent transportation systems (ITS). They constitute an innovative mean to support existing technologies to control road traffic and monitor incidents. Due to their energy-limited capacity, UAVs are employed for temporary missions and, during idle periods, they are placed in stations where they can replenish their batteries. In this paper, the problem of UAV docking station placement for ITS is investigated. This constitutes the first step in managing UAV-assisted ITS. The objective is to determine the best locations for a given number of docking stations that the operator aims to install in a large geographical area. Based on average road network statistics, two essential conditions are imposed in making the placement decision: i) the UAV has to reach the incident location in a reasonable time, ii) there is no risk of UAV’s battery failure during the mission. Two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are proposed. Results show that both algorithms achieve close coverage efficiency in spite of their different conceptual constructions.