Lobna Nassar

Vehicular ad-hoc networks(VANETs): capabilities, challenges in information gathering and data fusion

Vehicular Ad-hoc Network (VANET) has become an active area of research due to its major role to improve vehicle and road safety, traffic efficiency, and convenience as well as comfort to both drivers and passengers. This paper thus addresses some of the attributes and challenging issues related to Vehicular Ad-hoc Networks (VANETs). A lot of VANET research work have focused on specific areas including routing, broadcasting, Quality of Service (QoS), and security. In this paper, a detailed overview of the current information gathering and data fusion capabilities and challenges in the context of VANET is presented. In addition, an overall VANET framework, an illustrative VANET scenario are provided in order to enhance safety, flow, and efficiency of the transportation system.

Vehicular ad-hoc Networks(VANETs): capabilities, challenges in context-aware processing and communication gateway

Vehicular Ad-hoc Networks (VANETs) have attracted attention in the support of safe driving, intelligent navigation, and emergency and entertainment applications. VANET can be viewed as an intelligent component of the Transportation Systems as vehicles communicate with each other as well as with roadside base stations located at critical points of the road, such as intersections or construction sites. In this paper, we provide an overview of the context-aware processing and communication gateway associated with Vehicular Ad-hoc Network (VANET). The concept of context-awareness, the recent advances and various challenges involved in context-aware processing are discussed. Some arising ideas such as based on context ontology, relevancy, hybrid dissemination, service oriented routing are also presented. This paper further briefly describes the communication gateway in VANET which includes its functional view together with the standards and their detailed preliminary specifications applicable to VANET.

Recent Advances on Context-Awareness and Data/Information Fusion in ITS

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the target sources. On the other hand, context-aware information processing provides awareness of the driving environments by deploying intelligent query processing and smart information dissemination. The fusion and context-awareness should help in improving ITS operations with better road-awareness service, traffic monitoring, vehicle detection as well as development of new methods. This paper is centered on data fusion and context aware methodologies developed recently in the areas of ITS rather than on their ITS applications. We found that the recent progresses in ITS fusion are devoted to the potential cooperative approaches providing real-time/dynamic vehicle sensing technologies, whereas the recent context awareness techniques are deploying service concepts (e.g. location aware service) and frameworks. It is believed that the newly developed advanced fusion/context-aware techniques are becoming more effective to tackle complex traffic scenarios (e.g. traffic intersection) as well as complex urban environments.

VANET IR-CAS: utilizing IR techniques in developing context aware system for VANET

The proposed VANET IR-CAS is a context aware system that utilizes information retrieval (IR) techniques, such as indexing, document scoring and document similarity, to enhance context aware information dissemination in VANET. It uses a hybrid context model; spatial model for service filtering, ontology model for context reasoning and knowledge sharing, markup model for file exchange, and situational model for safety and convenience services. Its VANET OWL ontology managed by Jena semantic web framework succeeded in formalizing the semantics of VANET context domain and heightened the system abstraction level. Relevance of dispatched information to prospective recipients is enhanced by employing IR techniques and partial relevance. For commercial services, we used the hybrid vehicular communication (HVC) to increase the decentralized processing, exploit the vehicle processing power and increase user satisfaction and privacy. V2V is used for safety and convenience services where the level of abstraction has increased by using high level situation context attributes. In addition, more precise application notifications are now feasible after improving reasoning about situation certainty and severity. Hence, the main novelty of VANET IR-CAS is that it provides a highly abstract hybrid context model with IR based processing that raises the notification relevance, certainty and precision beside increasing decentralization and user satisfaction.

VANET IR-CAS for Commercial SA: Information Retrieval Context Aware System for VANET Commercial Service Announcement

We propose a context aware system for VANET based on information retrieval and show how it supports Service Announcement (SA). VANET IR-CAS enhances scalability through its highly abstract hybrid context model. It uses an ontology that formalizes the semantics of VANET context domain to allow for knowledge sharing. A hybrid vehicular communication (HVC) is utilized to increase the decentralization, exploit vehicle processing power and protect privacy. The employed IR techniques and partial relevance improve the dispatched information relevance to prospective recipients and add to their satisfaction. Using smart service grouping raises filtering efficiency due to reduction in required connection time.

VANET IR-CAS for Safety ACN: Information Retrieval Context Aware System for VANET Automatic Crash Notification Safety Application

We propose IR-CAS ACN, a fully Automated Crash Notification safety application that enhances accuracy and efficiency with its precise notifications and increased decentralization. It can be considered as an improvement to the BMW Advanced ACN (AACN): It decentralizes the severity calculation by introducing in-vehicle severity estimation. It fully automates the solution and disseminates more informative messages with partial rather than graded relevance that is insensitive to differences in severity within grades. Different IR models are compared using binary and partial effectiveness measures; estimating severity by calculating the Manhattan distance between the crash and severest crash context vectors outperforms tried models.

Evaluating VANET information retrieval context aware systems using the average distance measure ADM

Information Retrieval (IR) techniques are utilized in developing context aware systems for VANET safety and convenience services. For the safety services a context aware system for the Automatic Crash Notification called IR-CAS ACN is developed while the context aware Congested Road Notification system IR-CAS CRN is developed for the convenience services. Different IR models like the vector space, fuzzy logic and binary models are proposed for each of these systems. The performance of the proposed models for IR-CAS ACN is compared using test collections that are based on nineteen years of real life crash records associated with their severity levels while the performance of the IR-CAS CRN is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The highway capacity manual (HCM) speed-flow curves along with the Greenshield model are utilized in generating these freeway flow cases and their levels of service. The average distance measure (ADM) is used to evaluate the tested IR models. Results show that using the vector space model for severity estimation by calculating the Manhattan distance between the crash/congestion current context vectors and the severest crash/congestion context vectors outperforms the fuzzy and binary severity estimation models.

Enhancing Topic Detection in Twitter Using the Crowdsourcing Process

A decade ago, the crowdsourcing term was first coined and used to represent a method for expressing the wisdom of the crowd in accomplishing tasks that need human intelligence rather than machines and can be more efficiently accomplished time and financial wise using the crowd rather than indoor experts. This crowdsourcing process mainly contains four modules: designing incentives, then collecting, aggregating and verifying received information. The expert discovery module can be added to reduce the cost and enhance reliability and accuracy. The crowdsourcing process is used in this work to harness the mental ability of reliable Internet users around the globe and to improve the knowledge discovery techniques over social media; especially Twitter. The main objective is to improve the quality of the Twitter Exemplar-based topic detection system. The feedback from the crowd is utilized to adjust weights of the cosine similarity function deployed in the Exemplar-based topic detection algorithm. Testing the system using the Football Association Cup (FA Cup) dataset, it is found that the crowdsourcing has achieved a constant increase in the topic recall (by up to 15%), term precision (by up to 4%) and term recall (by up to 3%). Therefore, the new weights succeeded in increasing the three measures of topic quality significantly.

Fuzzy Logic in VANET context aware Congested Road and Automatic Crash Notification

For VANET safety services a context aware system for the Automatic Crash Notification (ACN) is developed while the context aware Congested Road Notification system (CRN) is developed for the convenience services. A simple fuzzy logic model is proposed and compared to different severity estimation models deployed for both systems. The performance of the ACN models is compared using a test collection that is based on nineteen years of real life crash records associated with their severity levels while the performance of the CRN models is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The non-binary Spearman correlation coefficient and the Average Distance Measure (ADM) are used to evaluate the performance of the tested models. Results show that the simple fuzzy severity estimation model has a comparable performance to more complicated systems such as the CoTEC (CoOperative Traffic congestion detECtion) fuzzy system and the URGENCY algorithm, and outperforms the binary severity estimation models for the ACN and CRN systems.

Overview of the crowdsourcing process

A decade ago, the crowdsourcing term was first coined and used to represent a method for expressing the wisdom of the crowd in accomplishing two types of tasks. One type includes tasks that need human intelligence rather than machines, and the other type covers those tasks that can be accomplished with a higher time and cost efficiency using the crowd rather than employing experts. The crowdsourcing process contains five modules: The first is designing incentives to mobilize the crowd to do the required task. This step is followed by four modules for collecting and assuring quality and then verifying and aggregating the received information. The verification and quality control can be done for the tasks, collected data and the participants by having more participants answer the same question or accepting answers only from experts to avoid errors from unreliable participants. Methods of discovering topic experts are utilized to discover reliable candidates in the crowd who have relevant experience in the discussed topic. Expert discovery reduces the number of needed participants per question which reduces the overall cost. This work summarizes and reviews the methods used to accomplish each processing step. Yet, choosing a specific method remains application dependent.