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Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time. Index Terms-Clustering architecture, Cognitive network, Fuzzy logic, Multi-criteria decision making, Vehicular Ad-hoc Networks.I. INTRODUCTION owadays, the rapid growth of the world population is indelibly tightly coupled with the massive growth in vehicular communications deployment and application. Recent advances in wireless communication technologies has led to the development of new communication paradigms where vehicles can communicate with each other, i.e. VANET, with the required infrastructure [1]. VANET supports a plethora of safety and non-safety related applications [1,2]. However, these applications require low latency, and high-reliability communication at appropriate overall data rates. Furthermore, a timely update of the network topology is a significant requirement for the operation of safety applications in this network. VANETs pose challenges related to their specific characteristics that distinguish them from other wireless networks. Beside the high mobility, the irregular distribution of nodes leads to frequent VANET partitions, particularly in highway scenarios [1][2][3][4]. Furthermore, the transmission range of vehicles is short, which reduces connectivity [1][2][3][4]. Hence, supporting dynamic data traffic in VANET cannot be guaranteed. Frequent network partitioning results in frequent intermittent connections that may lead to severe packet loss and significant latency [4].In addition, VANETs are non-structured, autonomous, totally decentralized self-configured networks with distributed characteristics nodes [1][2][3][4]. These properties make resource management a challenging problem. Maintaining a global network topology in a centralized entity is essential in order to manage a large-scale VANET in a metropolitan a...

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The recommender system came to help the user in finding the required item in a short time by filtering the available choices. This paper addresses the problem of recommending items to users by presenting new three genetic-based recommender system (GARS+, GARS++ and HGARS). HGARS is a combination of GARS+ with GARS++. It is an enhanced version of the genetic-based recommender system that works without the being a hybrid model. In the proposed algorithms, the genetic algorithm is used to find the optimal similarity function. This function depends on a liner combination of values and weights. We experimentally prove that HGARS improves the accuracy by 16.1%, the recommendation quality by 17.2% and the performance by 40%.