MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

Architecture and Implementation of a Trust Model for Pervasive Applications

Collaborative effort to share resources is a significant feature of pervasive computing environments. To achieve secure service discovery and sharing, and to distinguish between malevolent and benevolent entities, trust models must be defined. It is critical to estimate a device\u27s initial trust value because of the transient nature of pervasive smart space; however, most of the prior research work on trust models for pervasive applications used the notion of constant initial trust assignment. In this paper, we design and implement a trust model called DIRT. We categorize services in different security levels and depending on the service requester\u27s context information, we calculate the initial trust value. Our trust value is assigned for each device and for each service. Our overall trust estimation for a service depends on the recommendations of the neighbouring devices, inference from other service-trust values for that device, and direct trust experience. We provide an extensive survey of related work, and we demonstrate the distinguishing features of our proposed model with respect to the existing models. We implement a healthcare-monitoring application and a location-based service prototype over DIRT. We also provide a performance analysis of the model with respect to some of its important characteristics tested in various scenarios

TRULLO - local trust bootstrapping for ubiquitous devices

Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (TRUst bootstrapping by Latently Lifting cOntext) that assigns initial trust values based only on local information (on the ratings of its user’s past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone

Analysing and using subjective criteria to improve dental care recommendation systems

Online reviews and rating sites are shaping industries as the users rely on recommendations given by former consumers and sharing opinions on the web. Dentistry has also been impacted by dental patients' reviews. This paper classifies trust-related information for dental care recommendations onto 4 categories: context, relationship, reputation and subjective criteria. It discusses each category and describes how they help focussing on trust when matching patients and dentists in brief. The paper then focuses on subjective criteria and presents the results of a survey aimed at showing trustrelated information emerged from subjective characteristics. Traits of personalities are used as subjective characteristics of patients and that of dentists are derived from the online patients' reviews. 580 Australian patients were surveyed to determine what factors affect their decision to find the trusted dentist. Subjective characteristics of dentists such as dentists' qualities and experienced dentists are considered the most important factors after location and cost. The most preferred dentists' qualities by almost all types of personalities are experienced, professional and quality of service. When the patients are further classified based on levels of fear, their preferences for dentists' qualities changed. Subjective qualities of both patients and dentists are important factors to improve the matching capability for the dental care recommendation systems

An integrated framework to classify healthcare virtual communities

Healthcare (HC) strives to improve service quality through its cost-effective social computing strategy. However, sudden rise in the count of virtual community of practices (VCoPs) introduced many choices for physicians; As a result, it is not surprising to observe current literature reporting lack of study to investigate ideas integration within and between VCoPs. VCoPs need to be categorized for HC physicians so they will be able to pin-point effective a VC to attain assistance from. This paper is one of the first investigative studies, in HC sector, that proposed a framework to classify and pin-point appropriate VCoPs, for physicians, after it reviewed and analyzed traditional and up-to-date theoretical, empirical and case study literature in the area of social computing, knowledge management (KM) and VCoPs. The implementation of this framework pinpointed professional VCoPs as most appropriate for physicians based on strict requirements, i.e. closed physician communities holding many participants, which are older than 5 years with high boundary crossing. This framework is also a “one-size-fit-all” formula to build an organizational VCoP, utilizable by other business sectors