Vehicular cloud (VC) is a promising environment, where intelligent transport applications can be developed relying on mobile vehicles, which can be both cloud users and cloud service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a variety of services to users' vehicles. In this context, to consume a cloud service on the move, a user vehicle must first identify the most stable vehicles, relative to his/her motion, which are able to provide the service, and then select the most suitable service according to his/her preferences, while both provider vehicles and their services are described by attributes or quality constraints. Therefore, we introduce a generic relative motion model, as a generic link stability metric, upon which vehicles can form a stable cloud, and we address the VC service selection by using linguistic quantifiers and fuzzy quantified propositions, to define our flexible quantified service selection (FQSS) scheme, which aggregates efficiently both user preferences and service constraints and ranks service providers from the most to the least satisfactory. To break ties among the top-ranked service providers, we make use of our parameters for ranking refinement, called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp). The simulation results show that our link stability achieves generic motion, by modeling a wider range of vehicle motion types, and our FQSS scheme allows a good successful service consumption rate while reducing latency.

Vehicular cloud is a promising paradigm in which mobile vehicles can be both cloud users and service providers. It enables vehicles that have sufficient resources to act as mobile cloud servers by offering a wide range of services to user's vehicles. However, user vehicles need to discover provider vehicles along with their services, and request targeted services from them, especially with the high mobility of vehicles. Moreover, provider vehicles and their services are characterized by specific quality criteria and consumer vehicles need to select the most suitable services. In this paper, we design a new protocol that enables vehicles to discover and consume services of mobile cloud servers that are moving nearby. The protocol harnesses public buses, which provide a stability in terms of time (regular timetables) and space (fixed lines in an urban area), making them act as cloud directories, where provider vehicles can register their services. Public buses spread service registration information to the buses within the same bus line, and to buses of connected lines, as well. A consumer vehicle can discover the available services, along with their constraints, by querying the buses in their vicinity. To ensure service consumption, the buses provide also a routing protocol allowing communication between the provider and the consumer. Furthermore, we extend our protocol to enable, on the one hand, vehicle providers to select the most appropriate public bus for an efficient service registration, and users to select, on the other hand, the most satisfactory service, which satisfies both provider constraints and user preferences. Simulation results showed the efficiency of our protocol in terms of service discovery and service consuming delays, as well as its ability to select both the most suitable cloud directory and service provider for both providers and consumers, respectively.

Flexible querying of information systems dedicated to multimodal transport networks aims to help user organizing his/her trips by promoting public transport networks (bus, subway, train, boat, plane, etc.). In this context, it is necessary to provide an integrated environment in which it is possible for the user to express queries with complex preferences so as to meet his/her expectations. Complex preferences are modeled by fuzzy bipolar conditions which associate negative and positive conditions. Queries involving such conditions are called bipolar queries. In our case, such queries are addressed to multimodal transport information systems, which are often made of several distributed and heterogeneous databases. Therefore, semantic aspects have to be taken into consideration in the querying process so that only the most relevant data is targeted to evaluate queries. We introduce then in this paper a new approach for flexible querying of information systems that combines a reasoning mechanism (fuzzy bipolar DLR-Lite) with a relational language of a high expressiveness (bipolar SQLf language).

Nowadays, smartphones and wearable devices are endowed with several sensors, which can harvest large quantities of data about urban areas (location information, pollution levels, etc.), going through a list of personal and surrounding contexts such as noise level, traffic awareness, to name a few. Exploiting this wealth of information provided by the crowd allows developers to design and build several applications over the so-called Mobile CrowdSensing, such as traffic regulation, environmental monitoring, tourism recommendation, etc. However, for this to be possible, many barriers still have to be overcome such as collecting, handling, structuring and representing the crowd data in a suitable way. In this paper, we propose a three-fold solution to the problem of data management in CrowdSensing systems. Firstly, we structure the collected data based on semantic ontologies. Secondly, we enrich the data based on a novel contextual awareness data interlinking. Finally, we refine recommendations with contexts, through taking into consideration meta-information interlinked to the main information of interest. We have implemented our model in a tourism recommendation application as a proof of concept. The experimental evaluation - which we carried out - has shown very promising results.