Ismail Berrada

Controlling messages for probabilistic routing protocols in Delay-Tolerant Networks

A Delay Tolerant Networks is a network where contemporaneous connectivity among all nodes does not exist. Conventional routing protocols are not suitable for DTN because they assume end-to-end connectivity. DTNs are characterized by the absence of it. This leads to the critical problem of how to route a packet from one node to another, in such a network. This problem becomes more complex, when the node mobility also is considered. In this paper, a novel strategy is presented and evaluated for controlling messages in DTNs based on the delivery predictability metric of probabilistic routing protocols. Simulation results show that this new strategy gives better results in terms of delivered and relayed messages.

A formal model of human driving behavior in vehicular networks

Vehicular Ad-hoc Networks (VANET) are considered as a promising approach for building a variety of applications for Intelligent Transportation Systems (ITS). They are a kind of mobile networks that enables moving vehicles to exchange information about the driving environment. Although the amount of information disseminated through a VANET provides a great opportunity to enhance traffic safety, a study of the behavior of a human driver towards this information remains as an important axis to ensure safer roads. Driving behavior models are proposed by researchers as an important approach that allows a better understanding of human driving behavior. The main goal of this paper is to model and learn driver behavior in the presence of different type of traffic information. For this, we propose a new formal approach to construct a driving behavior model that will be adapted to an individual driver. To describe the model we define rectangular hybrid input output automata formalism which consists of an adaptation of a set of notions related to hybrid automata concept. Then for model construction, we propose an online passive learning based approach to construct the model according to the observed driving behavior. The constructed model may be useful to predict the driver behavior in the future, prevent unsafe situations and provide more comfort to the driver.

A Formal Driving Behavior Model for Intelligent Transportation Systems

Vehicular Ad hoc Networks are considered recently as a fertile field of research. Their applications are showing a growing importance as they are expected to improve road safety and traffic efficiency, through the development of vehicle safety applications whose main goal is to provide the driver with assistance in dangerous situations. Thanks to vehicular communications, drivers can permanently receive information about road conditions which help them to make more reliable decisions. The idea behind this paper is to enable an adaptive assistance to drivers in different situations, based on their past driving experience. As a first step, we focus on the modeling and learning of individual driving behavior at a picoscopic level. This paper proposes a formal description of a driver-centric model, using the formalisms of hybrid IO automata and rectangular automata. Then, an online passive learning based approach for the construction of the described model is proposed. Having a model that describe the behavior of drivers can enable us to predict and recognize a driver preferences in different driving context, enabling thus an adaptive assistance.

A holistic approach for modeling and verification of human driver behavior

Driver behavior has long been considered as particularly relevant for the development of automotive applications, especially that recently these applications are increasingly trying to adapt to the driver. However, drivers behave differently in the different traffic situations, hence the need of techniques to enable cars to learn from their drivers and create a model of his behavior. Actually, future generation of cars will be equipped with all sorts of sensing, computing and communication devices that will allow them to acquire all information about the state of the vehicle, the driver and the environment. And, hence make easier the driving behavior learning process. The present paper addresses the problem of modeling and learning the behavior of a driver in an intelligent car by presenting an approach for the construction and verification of a learned driving model. First, we propose a new way for modeling the driver-vehicle and environment, which consists of considering driver-vehicle as a rectangular hybrid input output automaton while representing contextual information about driving environment as conditions on the automaton variables. The construction of the model is ensured through a continuous monitoring of the driver-vehicle and environment system. The use of rectangular predicate states and environmental conditions will facilitate the verification of driving behavior. We then present a formal verification of properties of the constructed model expressed in Probabilistic Computational Tree Logic (PCTL) to assess its convenience to different traffic situations.

Short: Graphical Specification and Automatic Verification of Business Process

This paper deals with the integration of the formal verification techniques of business process (BP) in the design phase. In order to achieve this purpose, we use the graphical notation of Business Process Modeling Notation (BPMN) for modeling BP and specifying constraint properties to be verified. A formal semantics for some response properties are given.

Minimizing Cost Travel in Multimodal Transport Using Advanced Relation Transitive Closure

The optimization computation is an essential transversal branch of operations research which is primordial in many technical fields: transport, finance, networks, energy, learning, etc. In fact, it aims to minimize the resource consumption and maximize the generated profits. This work provides a new method for cost optimization which can be applied either on path optimization for graphs or on binary constraint reduction for Constraint Satisfaction Problem (CSP). It is about the computing of the “transitive closure of a given binary relation with respect to a property.” Thus, this paper introduces the mathematical background for the transitive closure of binary relations. Then, it gives the algorithms for computing the closure of a binary relation according to another one. The elaborated algorithms are shown to be polynomial. Since this technique is of great interest, we show its applications in some important industrial fields.

Context-Aware Driving Assistance: An Approach for Monitoring-Based Modeling and Self-learning Cars

Recent cars are equipped with a large number of sensors, electronic and communication devices that collect heterogeneous information about the vehicle, the environment and the driver. The use of the information coming from all these devices can highly contribute to the improvement of the vehicle safety as well as the driving experience. The last few years were marked by the development of a large number of in-vehicle intelligent systems that use driving behavior models to assist the driver ubiquitously. However, an important aspect to enhance driving experience is to make the provided assistance as close as possible to the behavior of the car owner, hence a need of personal models of drivers learned from their observed behavior. In this paper, the concept of intelligent and self-learning car is presented and examples of some car’s embedded systems are given. Also, the role of modeling driver behavior in the design of driving assistance systems is emphasized. Further-more, the importance of monitoring-based driving behavior model construction to enable a personalized assistance is brought out together with some potential applications of formal driving behavior models.

Including Personality Traits, Inferred from Social Networks, in Building Next Generation of AEHS

User profile inference on online social networks is a promising way for building recommender and adaptive systems. In the context of adaptive learning systems, user models are still constructed by means of classical techniques such as questionnaires. Those are too time-consuming and present a risk of dissuading learners to use the system. This paper explores the feasibility of learner modeling based on a proposed set of features extracted and inferred from social networks, according to the IMS-LIP specification. A suitable general architecture of an AEHS is presented, whose adaptation combines three distinct aspects: Felder and Silverman learning style, knowledge level and personality traits. This latter is a novel adaptation criterion, it is an interesting user feature to be incorporated in user models, a feature that is not yet considered by existing AEHS. However, adapting such systems to personality traits contributes to achieving a better adaptation by varying learning approaches, integrating collaboration and adapting feedback. The aim of this paper is to show how this contribution is doable through the proposed framework.

A near-optimal green control for probabilistic routing protocols in delay-tolerant networks

Delay tolerant networks present a promising solution for environments where there is no continuous network connectivity. By using the store-carry and forward protocol, mobile nodes can communicate with each other even though there is no permanent link from the source to the destination. Since routing protocols in DTNs are based on mobile nodes for sending messages, the choice of the message forwarding strategy can have a major impact on the system performance. PRoPHET protocol is among the most widely used routing protocols for such networks. In this paper, we propose new energy efficient forwarding schemes for probabilistic routing protocols that can optimise different performance metrics. Based on some assumptions, we set new rules for message transmission to neighbouring nodes. Using the ONE simulator, numerical results show that our new forwarding schemes reduce the energy consumption considerably and deliver more messages compared to the original version of PRoPHET.

The 4-Octahedron Abstract Domain

In static analysis, the choice of an adequate abstract domain is an interesting issue. In this paper, we provide a new numerical abstract domain: 4-Octahedron. It is an Octahedra subclass that infers relations of the form: { \( x \sim \alpha , x-y \sim \beta , (x-y) - (z-t) \sim \lambda \)}, such that: x, y, z and t are real variables, \(\alpha , \beta \) and \( \lambda \) are real constants and \({\sim }\in \{\le ,\ge \}\). Its precision lies between the octagons and octahedra. We construct a suitable structure for its representation, we provide normalization algorithms for computing its canonical form and we give methods to compute its transfer functions (Union, Intersection, Assignment, Projection, ...). Complexity of the implementation algorithms is proved to be polynomial.