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2 papersLast indexed Aug 31, 2026
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Jan 6, 2024·2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)
4 cites
On the Decentralization of Mobile Crowdsensing in Distributed Ledgers: An Architectural Vision

Lorenzo Gigli, Federico Montori, Mirko Zichichi, Luca Bedogni · 6 authors

Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.

Open access
Mobile Crowdsensing and Crowdsourcing
Evacuation and Crowd Dynamics
Human Mobility and Location-Based Analysis
Original source
Dec 18, 2023·Swarm Intelligence
15 cites
Decentralized traffic management of autonomous drones

B. Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz · 5 authors

Abstract Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task—filled with conflicts—is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area.

Open access
2 source records
Evacuation and Crowd Dynamics
Traffic control and management
Transportation Planning and Optimization
Original source