The Covering-Assignment Problem for Swarm-Powered Ad Hoc Clouds: A Distributed 3-D Mapping Usecase

• Published in: IEEE internet of things journal Vol. 8; no. 9; pp. 7316 - 7332
• Main Authors: Costa, Leandro R., Aloise, Daniel, Gianoli, Luca G., Lodi, Andrea
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D reconstruction; Ad hoc networks; Cloud computing; Clustering; Combinatorial analysis; Computation; Data transmission; Drone aircraft; Drones; Image reconstruction; Infrastructure; Internet of Things; Microcomputers; Operations research; Optimization; Process control; swarm; Three-dimensional displays; Workload; workload optimization; Workloads
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3039261

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of operations that are typically carried out by humans. Besides automating field operations, drones acting de facto as a swarm can serve as an ad hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members and the field agents. By considering the practical example of a swarm-powered 3-D reconstruction application on top of such cloud infrastructure, we present a new optimization problem for the efficient generation and execution of multinode computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the interdrone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones.