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1. Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

   https://doi.org/10.3390/drones8020057  

   Myers, Rodman J. ; Perera, Sirani M. ; McLewee, Grace ; Huang, David ; Song, Houbing ( February 2024 , Drones)

   The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

    

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2. A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars

   https://doi.org/10.1109/TITS.2023.3266460  

   Imran, Navid Mohammad ; Mishra, Sabyasachee ; Won, Myounggyu ( September 2023 , IEEE Transactions on Intelligent Transportation Systems)

   Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms. 

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3. Performance Evaluation and Analysis of Proactive and Reactive MANET Protocols at Varied Speeds

   https://doi.org/10.1109/CCWC47524.2020.9031233  

   Skaggs-Schellenberg, Russell ; Wang, Nan ; Wright, Daniel ( January 2020 , 2020 10th Annual Computing and Communication Workshop and Conference (CCWC))

   A Mobile Ad Hoc Network (MANET) is a decentralized wireless network that does not rely on pre-existing infrastructure. Instead, it is each node's responsibility to forward data according to its specified routing protocol. Although these protocols perform the same task, their performance in a variety of scenarios differ. This paper simulates four different routing protocols in NS-3 at a variety of movement speeds and area sizes, comparing their Packet Delivery Ratio (PDR) and Average End-To-End Delay (AETED). The performance results reflect what would be expected if a system would be implemented in a similar environment. Therefore, it is crucial selecting a protocol to best suit a system. 

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4. Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions

   A. Khochare, Y. Simmhan ( May 2021 , IEEE International Conference on Computer Communications (INFOCOM))

   null (Ed.)

   Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility–runtime trade-offs under diverse workloads. 

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5. A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

   https://doi.org/10.1109/CISS.2019.8693023  

   Islam, Shafkat ; Razi, Abolfazl ( March 2019 , 53rd Annual Conference on Information Sciences and Systems (CISS))

   This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for firefighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider a more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that the success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB). 

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