Author:

Abstract:

Rapid advancements in embedded technology enable vehicles to become equipped with embedded on-board computation and wireless communication capabilities. Intelligent transportation systems (ITS) is an emerging research field that demands efficient delivery of information between vehicles for the creation of useful and usable telematic applications operating in nomadic large-scale environments. Some interesting inter-vehicular applications made possible by this technology include accident avoidance, incident notification, congestion monitoring and parking space allocation. For these types of applications it is crucial that the peers receive the \textit{right information at the right time and the right place} to make informed decisions. However, due to the ephemeral, highly dynamic and mobile nature of the vehicular networks we face four major challenges: (1) how to identify what information is relevant to take actions or make decisions, (2) how to disseminate the relevant information to the interested peers efficiently, (3) how to coordinate with other peers and (4) last but not the least, how to compare the effectiveness of different algorithms and protocols being proposed for the previous concerns. This thesis aims to optimize communication in large scale vehicular networks using context-aware strategies in order to address these challenges. We optimize communication by maximizing throughput, reducing network traffic utilization with context-based routing and filtering-out irrelevant information. To address challenge one, we define a mechanism to compute the quality of the information and the peers in vehicular ad-hoc networks (VANETs) to ensure that vehicles can take well-informed decisions. The application developers and the system architects are offered the flexibility to define application specific quality requirements. Thus, we provide a two-fold solution in which on the one hand we focus on the quality of the information and on the other hand we aim at determining the reputation of the nodes involved in the communication. This helps to eliminate the use of erroneous, ambiguous and imprecise information provided by unknown entities.To address challenge two, we design and develop context-aware algorithms that can propagate this information among the peers intelligently and evaluate these algorithms on the basis of certain network metrics. Together, these objectives lead to an efficient way to optimize communication by eliminating unnecessary information in large scale vehicular networks. To address challenge three, we present two extensions of the work on intelligent algorithms for adaptive context-aware communication. First, we describe a context-based grouping mechanism which allows the definition of groups of peers (vehicles) based on common spatio-temporal characteristics and shared interests. Each group defined by these characteristics specifies which context information can be distributed inside the group between peers. This approach of context-based grouping efficiently manages context distribution and significantly reduces irrelevant/redundant information over a large scale network. Second, we present another extension of the work on intelligent algorithms for adaptive context-aware communication. We investigate how exploiting a social network helps peers in large-scale highly dynamic vehicular networks to obtain timely and relevant information. We propose a three-leaved mirror approach and demonstrate this by providing a ubiquitous-help-system (UHS) which resides on top of a vehicular network. Our approach involves a comparison of social and spatial relevance between two peers in terms of the spatial closeness. Finally, we illustrate with the help of simulated evaluation that the use of social networking capabilities combined with knowledge about the spatio-temporal context significantly improves purposeful interaction between peers. These interactions improve in terms of both the efficiency of the data dissemination, the relevancy and the quality of the delivered information. To address challenge four, we propose a framework for context-aware adaptive information sharing that allows the evaluation and comparison of alternative information routing schemes (i.e. algorithms and protocols) using network-based metrics to measure a variety of quality attributes. This thesis presents a number of real-world VANET applications and scenarios that help us to implement and compare our solutions with the state-of-the-art. This thesis also provides the foundations for the further design and development of various context-aware applications in VANETs.