Our paper “MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms,” co-authored with Ph.D. students Bharath Keshavamurthy (ASU) and Matt Bliss (Purdue Univerisity) has been accepted on the IEEE Transactions on Cognitive Communications and Networking! Congratulations Bharath and Matt!<\/p>\n\n\n\n
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This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the long-term delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication: enabled by a decentralized command-and-control network, the optimal single-agent policy is augmented with spread maximization, consensus-driven conflict resolution, adaptive frequency reuse, and piggybacking. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req\/min\/UAV, single-agent MAESTRO offers 3.8x faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7x faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.<\/p>\n\n\n\n
A preprint can be found here: https:\/\/arxiv.org\/abs\/2007.01228<\/p>\n","protected":false},"excerpt":{"rendered":"
Our paper “MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms,” co-authored with Ph.D. students Bharath Keshavamurthy (ASU) and Matt Bliss (Purdue Univerisity) has been accepted on the IEEE Transactions on Cognitive Communications and Networking! Congratulations Bharath […]<\/p>\n","protected":false},"author":112,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-582","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/582","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/users\/112"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/comments?post=582"}],"version-history":[{"count":0,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/posts\/582\/revisions"}],"wp:attachment":[{"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/media?parent=582"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/categories?post=582"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.engineering.asu.edu\/michelusi\/wp-json\/wp\/v2\/tags?post=582"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}