2020/02/04 by Justin Hu, Hu, Justin, Ariana Bruno +10 · 1 citation
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed Control Multi-Agent Systems #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2002.01419
openalex publication_date 2020/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Swarms of autonomous devices are increasing in ubiquity and size. There are two main trains of thought for controlling devices in such swarms; centralized and distributed control. Centralized platforms achieve higher output quality but result in high network traffic and limited scalability, while decentralized systems are more scalable, but less sophisticated. In this work we present HiveMind, a centralized coordination control platform for IoT swarms that is both scalable and performant. HiveMind leverages a centralized cluster for all resource-intensive computation, deferring lightweight and time-critical operations, such as obstacle avoidance to the edge devices to reduce network traffic. HiveMind employs an event-driven serverless framework to run tasks on the cluster, guarantees fault tolerance both in the edge devices and serverless functions, and handles straggler tasks and underperforming devices. We evaluate HiveMind on a swarm of 16 programmable drones on two scenarios; searching for given items, and counting unique people in an area. We show that HiveMind achieves better performance and battery efficiency compared to fully centralized and fully decentralized platforms, while also handling load imbalances and failures gracefully, and allowing edge devices to leverage the cluster to collectively improve their output quality.