This paper looks at the problem of data prioritization, commonly found in mobile ad-hoc networks. The proposed general solution uses a machine learning approach in order to learn the relevance value of reports, which represent sensed data. The general solution is then applied to a travel time dissemination application. Through the use of offline learning, the paper analyzes the feasibility of the proposed approach and compares the accuracy performance of several common machine learning algorithms. The results show that not all machine learning algorithms may be used for prioritization and that the use of the logistic regression algorithm is particularly suited for the problem. The learned logistic regression model is then used in a simulated VANET environment. The results of the simulations show that it is better at prioritizing reports in terms of their usefulness in aiding vehicles to choose the shortest travel time paths.

Vehicular ad-hoc networks (VANETs) is a promising approach to the dissemination of spatiotemporal information such as the current traffic condition of a road segment or the availability of a parking space. Due to the constraint of the communication bandwidth, only a limited number of information items may be transmitted upon a vehicle-to-vehicle communication opportunity. Ranking becomes critical in this situation, by enabling the most important information to be transmitted under the bandwidth constraint. In this paper we propose a method for online learning of spatio-temporal information ranking in VANETs. In this method, mobile nodes such as vehicles judge the relevance of incoming information items and use them as training examples for Naive Bayesian learning. Additionally, a separate machine learning algorithm is used to estimate the probability of a duplicate item being transmitted. The method is used in place of commonly used heuristics, and is evaluated for travel time and parking availability dissemination applications.

In this paper we study querying multimedia data such as video and voice clips in hybrid vehicular networks that consist of vehicles that are capable of both infrastructure-less short-range communication and infrastructure communication. We introduce a set of query processing strategies which differ from each other in terms of push versus pull, whether or not infrastructure communication is utilized, and whether metadata dissemination is separated from multimedia dissemination. We analyze these strategies theoretically and by simulations, and identify the one that is superior to the others. Categories and Subject Descriptors H.2.4 [Database Management]: Systems – distributed databases, multimedia databases.

Abstract — In this paper we provide an architecture for Tactical Information Middleware for bandwidth constrained information management. We propose the ideas of rank-based data dissemination, and the use of a tactical information management query language. These ideas will deal with dynamic changes in bandwidth and explore opportunistic data dissemination. Thus, will lead to a cross layer design of a system capable of handling the dynamic data management issues relevant in many mission critical applications. 1 I.

The use of Vehicular Ad-Hoc Network (VANET) has been applied to many applications involving information dissemination. Many of such applications are limited by the communication limitations of a VANET, such as limited transmission range and bandwidth. This imposes a necessity for evaluating the relevance of information. This paper proposes the use of machine learning for finding relevance of information for a parking information dissemination system. The proposed method uses the learned relevance for aiding vehicles in decision making by finding the probability that a given parking location will be available at the time of arrival. The method was evaluated through simulations and the results show that the proposed method is successful at learning the relevance of parking reports, which resulted in lower parking discovery times for vehicles.

Abstract — Vehicular ad-hoc networks (VANETs) is a promising approach to the dissemination of spatio-temporal information such as the current traffic condition of a road segment or the availability of a parking space. Due to the constraint of the communication bandwidth, only a limited number of information items may be transmitted upon a vehicle-to-vehicle communication opportunity. Ranking becomes critical in this situation, by enabling the most important information to be transmitted under the bandwidth constraint. In this paper we propose a method for online learning of spatio-temporal information ranking for a travel time dissemination application within a VANET. In this method, vehicles judge the relevance of incoming information items and use them as training examples for Naive Bayesian learning. Additionally, a separate machine learning algorithm is used to estimate the probability of a duplicate item being transmitted. The method is used in place of commonly used heuristics, and is shown to be superior in the application of travel time dissemination. Keywords-machine learning, VANET, data prioritization, dissemination I.