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Abstract
The recent move to empower sensor-rich vehicles with wireless communication technology has created the potential for a city-wide sensing platform that is an essential input to realise a wide range of traffic safety and efficiency applications, that are the building blocks of a future intelligent transportation system (ITS). In particular, many applications, such as adaptive traffic light adjustment, hazard road detection and congestion predictions, are envisioned based on the assumption of the availability of high quality floating car data (FCD). However, efficiently collect- ing and disseminating the huge amount of data generated by a variety of sensors mounted on moving vehicles remains a challenging task due to the limited channel resources, and the highly dynamic and disconnected network topology presented in vehicular networks. In this thesis, we address the above challenges and developed FCD systems in vehicular networks by exploiting the concept of mobile agents. A mobile agent is a self-contained software component that migrates from one host to another, executing application-specific functions. Being highly autonomous, a mobile agent is fundamentally resilient to network faults and offers significant advantages for data collection in highly dynamic and distributed environments. We first address the problem of harvesting FCD from operating vehicles to a remote FCD server, proposing a mobile agent based centralised FCD systems (CFCD) in heterogeneous vehicular networks. The proposed system, inspired by the theory of random walk on graphs, dispatches multiple mobile agents to collect road segment traffic aggregates from vehicles moving within a large urban region. We mathematically prove that given a sufficient number of mobile agents, the system is able to collect all road segments within the region, provides 100% collection coverage in the best case. The proposed CFCD system is further extended to develop a distributed FCD (DFCD) system, such that vehicles can exchange and share their sensed observations by simply overhearing these agents on the air. In addition, we also derive analytical models, which take into consideration agent population, storage capacity and road network topology, to accurately predict system performance in term of dissemination speed. Finally, due to the lossy link present in the vehicular environment, a collection cycle may not reach all vehicles within the segment, resulting in low collection coverage. We propose a learning algorithm based on recursive Bayesian estimation to prevent premature termination of the collection process. Extensive simulations are conducted using widely recognised simulation tools. The results show that the proposed FCD collection and dissemination approach outperforms the traditional approaches in terms of collection coverage, latency and communication overhead for varying vehicle densities and road network patterns.