Surveillance, management and estimation of spontaneous crowd formations in urban environments, e.g., during open-air festivals or rush hours, are necessary measures for city administration. Most solutions that implement these measures however require additional costly hardware installations (e.g., installation of observation cameras) and infrastructure support, and often pose privacy concerns. In this work, we present UrbanCount, a fully distributed crowd counting protocol for cities with high crowd densities. UrbanCount relies on mobile device-to-device communication to perform crowd estimation. Each node collects crowd size estimates from other participants in the system whenever in communication range and immediately integrates these estimates into a local estimate. The objective of UrbanCount is to produce a precise mapping of the local estimate to the anticipated global result while preserving node privacy. We evaluate the proposed protocol via extensive tracedriven simulations of synthetic and realistic mobility models. Furthermore, we investigate the dependency between accuracy and density, and demonstrate that in dense environments the local estimate does not deviate by more than 2% for synthetic and 7% for realistic scenarios.
Distributed aggregation algorithms have traditionally been applied to environments with no or rather low rates of node churn. The proliferation of mobile devices in recent years introduces high mobility and node churn to these environments, thus imposing a new dimension on the problem of distributed aggregation in terms of scalability and convergence speed. To address this, we present DiVote, a distributed voting protocol for mobile device-to-device communication. We investigate a particular use case, in which pedestrians equipped with mobile phones roam around in an urban area and participate in a distributed yes/no poll, which has both spatial and temporal relevance to the community. Each node casts a vote and collects votes from other participants in the system whenever in communication range; votes are immediately integrated into a local estimate. The objective of DiVote is to produce a precise mapping of the local estimate to the anticipated global voting result while preserving node privacy. Since mobile devices may have limited resources allocated for mobile sensing activities, DiVote utilizes D-GAP compression. We evaluate the proposed protocol via extensive trace-driven simulations of realistic pedestrian behavior, and demonstrate that it scales well with the number of nodes in the system. Furthermore, in densely populated areas the local estimate of participants does not deviate by more than 3% from the global result. Finally, in certain scenarios the achievable compression rate of DiVote is at least 19% for realistic vote distributions.
sbuc
commented
6 years ago
[x] Danielis, Peter and Kouyoumdjieva, Sylvia T. (KTH, School of Electrical Engineering (EES)) and Karlsson, Gunnar (KTH, School of Electrical Engineering (EES)): UrbanCount: Mobile Crowd Counting in Urban Environments
[x] DiVote: A Distributed Voting Protocol for Mobile Device-to-Device Communication