Project Title:

Efficient Drone Delivery in a Multi-Drone Skyway Network 

Project Description:

Drones are a new type of IoT devices that offer cost-effective and fast delivery services. The potential utilization of drones is limited by payload capacity and battery consumption constraints. Drones may need multiple times of recharge for persistent delivery operation. The drone delivery environment is highly constrained because the availability of recharging stations is not guaranteed. We leverage the service paradigm to address the key challenges in delivery by drones. The functional and non-functional properties of drones are abstracted as Drone-as-a-Service (DaaS). The drone services operate in a skyway network which is constructed by linking the skyway segments. Each skyway segment connects two nodes which are the rooftops of the high-rise buildings. Each node is assumed to be a recharging station or a delivery target. Given a source and a destination, the objective of this project is to collect a trajectory dataset considering the battery limitations and availability of pads on recharging stations. The dataset will be used for predicting the arrival of drones at certain stations in a multi-drone network and help in computing the best skyway segments leading to the destination.

Internship Publications:

• Service-based Trajectory Planning in Multi-Drone Skyway Networks. Sarah Bradley, Albertus Alvin Janitra, Babar Shahzaad, Balsam Alkouz, Athman Bouguettaya, and Abdallah Lakhdari. PerCom 2023, Atlanta, USA, Demo paper.