Abstract

Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs, but most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.

Abstract

Vehicular Ad hoc NETworks (VANETs) are a special kind of Mobile Ad hoc NETworks (MANETs), which can provide scalable solutions for applications such as traffic safety, internet access, etc. To properly achieve this goal, these applications need an efficient routing protocol. Yet, contrary to the routing protocols designed for the MANETs, the routing protocols for the VANETs must take into account the highly dynamic topology caused by the fast mobility of the vehicles. Hence, improving the MANET routing protocol or designing a new one specific for the VANETs are the usual approaches to efficiently perform the routing protocol in a vehicular environment. In this context, we previously enhanced the Destination-Sequenced Distance-Vector Routing protocol (DSDV) based on the Particle Swarm Optimization (PSO) and the Multi-Agent System (MAS). This motivation for the PSO and MAS comes from the behaviors seen in very complicated problems, in particular routing. The main goal of this paper is to carry out a performance evaluation of the enhanced version in comparison to a well-known routing protocol which is the Intelligent Based Clustering Algorithm in VANET (IBCAV). The simulation results show that integrating both the MAS and the PSO is able to guarantee a certain level of quality of service in terms of loss packet, throughput and overhead.

Abstract

Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased.

Abstract

During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.

Abstract

During the last decades, Learning Classifier Systems have known many advancements that were highlighting their potential to resolve complex problems. Despite the advantages offered by these algorithms, it is important to tackle other aspects such as the uncertainty to improve their performance. In this paper, we present a new Learning Classifier System (LCS) that deals with uncertainty in the class selection in particular imprecision. Our idea is to integrate the Belief function theory in the sUpervised Classifier System (UCS) for classification purpose. The new approach proved to be efficient to resolve several classification problems.

Abstract

During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.

Abstract

In order to cope with certain challenges posed by device capacity and the nature of IoT networks, a lightweight access control model is needed to resolve security and privacy issues. The use of complex encryption algorithms is infeasible due to the volatile nature of IoT environment and pervasive devices with limited resources. In this paper, we present the Trust-ABAC, an access control model for the Internet of Things, in which a coupling between the access control based on attributes and the trust concept is done. We evaluated the performance of Trust-ABAC through an experiment based on a simulation. We used the OMNeT++ simulator to show the efficiency of our model in terms of power consumption, response time and the average number of messages generated by an access request. The obtained results of simulation prove the good scalability of our Trust-ABAC model.

Abstract

In technology-enhanced learning, semantic annotations have been employed to attach semantic metadata to learning materials in order to significantly enhance their accessibility by human users and machines as well. In this paper, we present an ontology-based multi-level semantic representation model that aims to enrich the description of learning objects with semantics regarding their subjects, competencies and instructional roles. More specifically, the proposed model uses three ontologies: a subject domain ontology describing the domain concepts and the relations that are covered by the subject matter being taught, a competency ontology describing the competency-related characteristics of learners and learning resources, and an instructional role ontology specifying the instructional role(s) a learning object can play in an instructional setting. To demonstrate the feasibility of our model, an illustrative example is given that explains how learning object semantics can be represented with different granularities.

Abstract

Abstract

Approximate reasoning aims to manage knowledge imprecision in the inference process. It is a generalization of the Modus Ponens of classical logic. Originally, it is defined in fuzzy logic context, where knowledge are modeled by a quantitative way. We are interested in this paper to approximate reasoning in the symbolic multi-valued logic context. This logic allows presenting imprecise knowledge in a qualitative way, where every predicate is modeled by a multi-set. In order to express imprecision, each multi-set is associated to a scale base of ordered symbolic degrees. In a previous work where a symbolic approximate reasoning has been defined, it has been assumed that all multi-sets of the inference schema have the same scale base. This has the disadvantage to prevent free definition of knowledge. For that, we propose in this paper a new approximate reasoning which can infer with multi-sets having different scale bases. Our solution consists of interfacing all the multi-sets in order to avoid information loss.