Vehicular sensor networks (VSNs) enable brand new and promising sensing applications, such as traffic reporting, relief to environmental monitoring, and distributed surveillance. In our past work, we have designed and implemented {em MobEyes}, a middleware solution to support VSN-based urban monitoring, where agent vehicles (e.g., police cars) move around and harvest meta-data about sensed information from regular VSN-enabled vehicles. In typical urban sensing operations, multiple agents should collaborate in harvesting and searching for key meta-data in parallel. Thus, it is critical to effectively coordinate the harvesting operations of multiple agents in a decentralized and lightweight way. The paper presents a novel meta-data harvesting algorithm, called {em datataxis}, whose primary idea is to effectively cover a large search area by properly alternating foraging behaviors inspired by E. coli chemotaxis and L'{e}vy flights to favor agent movements towards ``information patches'' where the concentration of not-harvested meta-data is high. The proposal avoids harvesting work duplication via stigmergy-based prevention of useless concentration of agents in the same region at the same time. We have validated datataxis via extensive simulations that demonstrate how the proposed bio-inspired behavior of harvesting agents effectively balances their movements, by outperforming other decentralized strategies. Moreover, our solution has shown to be robust and to work well under a wide range of operation parameters, thus making it easily and rapidly deployable for different urban sensing operations.

Bio-inspired Multi-Agent Data Harvesting in a Proactive Urban Monitoring Environment

BELLAVISTA, PAOLO;
2009

Abstract

Vehicular sensor networks (VSNs) enable brand new and promising sensing applications, such as traffic reporting, relief to environmental monitoring, and distributed surveillance. In our past work, we have designed and implemented {em MobEyes}, a middleware solution to support VSN-based urban monitoring, where agent vehicles (e.g., police cars) move around and harvest meta-data about sensed information from regular VSN-enabled vehicles. In typical urban sensing operations, multiple agents should collaborate in harvesting and searching for key meta-data in parallel. Thus, it is critical to effectively coordinate the harvesting operations of multiple agents in a decentralized and lightweight way. The paper presents a novel meta-data harvesting algorithm, called {em datataxis}, whose primary idea is to effectively cover a large search area by properly alternating foraging behaviors inspired by E. coli chemotaxis and L'{e}vy flights to favor agent movements towards ``information patches'' where the concentration of not-harvested meta-data is high. The proposal avoids harvesting work duplication via stigmergy-based prevention of useless concentration of agents in the same region at the same time. We have validated datataxis via extensive simulations that demonstrate how the proposed bio-inspired behavior of harvesting agents effectively balances their movements, by outperforming other decentralized strategies. Moreover, our solution has shown to be robust and to work well under a wide range of operation parameters, thus making it easily and rapidly deployable for different urban sensing operations.
U. Lee; E. Magistretti; M. Gerla; P. Bellavista; P. Liò; K.-W. Lee
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/60557
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