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The basis of vehicular ad hoc networks (VANETs) is the exchange of data between entities, and making a decision on received data/event is usually based on information provided by other entities. Many researchers utilize the concept of trust to assess the trustworthiness of the received data. Nevertheless, the lack of a review to sum up the best available research on specific questions on trust management in vehicular ad hoc networks is sensible. This paper presents a systematic literature review to provide comprehensive and unbiased information about various current trust conceptions, proposals, problems, and solutions in VANETs to increase quality of data in transportation. For the purpose of the writing of this paper, a total of 111 articles related to the trust model in VANETs published between 2005 and 2014 were extracted from the most relevant scientific sources (IEEE Computer Society, ACM Digital Library, Springer Link, Science Direct, and Wiley Online Library). Finally, ten articles were eventually analyzed due to several reasons such as relevancy and comprehensiveness of discussion presented in the articles. Using the systematic method of review, this paper succeeds to reveal the main challenges and requirements for trust in VANETs and future research within this scope.Keywords: Systematic literature review; Trust management; VANET; Trust metric 1 Review IntroductionVehicular ad hoc networks (VANETs) are a class of ad hoc networks that consist of vehicles and roadside units (RSUs). VANETs were originally created to enhance safety on the road using cooperative collision warning via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In V2V communication, vehicles send and receive messages to and from one another. These messages can alert signals about road congestion, accidents ahead, or information about traffic on a given route. V2I communication takes place between nodes and roadside infrastructure and involves finding the nearest cheapest gas station, internet services, online toll payment, etc.According to [1], the applications in VANETs are categorized into safety and non-safety applications. The basis In order to overcome these threats and increase security, several concepts have been proposed by researchers. Wei and Chen [3] stated that authentication is one method for ensuring the integrity of transmitted messages. In [4], the reputation of a vehicle is introduced to evaluate the reliability of received data. Dotzer et al. also stated that a common method to deal with the safety threats in VANETs is to establish trust relationships and detect selfish and malicious entities [5].Security is one of the main issues in VANETs, and trust is a key element of security [6]. Hence, since VANETs are based upon data exchange among vehicles, trustworthiness of data is of great importance. In addition, data communication between trusted vehicles directly affects

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

Cloud computing is currently emerging quickly as a clientserver technology structure and, currently, providing distributed service applications. However, given the availability of a diverse range of wireless access technologies, people expect continuous connection to the most suitable technology that matches price affordability and performance goals. Among the main challenges of modern communication is the accessibility to wireless networks using mobile devices, with a high service quality (QoS) based on preferences of the users. Past literatures contain several heuristic approaches that use simplified rules to look for the best network that is available. Nevertheless, attributes of mobile devices need algorithms that are quick and effective in order to select best available network near realtime. This study proposes a hybrid intelligent handover decision algorithm primarily founded on two main heuristic algorithms: Artificial Bee Colony or ABC as well as Particle Swarm Optimization or PSO named ABCPSO to select best wireless network during vertical handover process. The ABCPSO algorithm has been optimized to achieve small cost function that are powered using the IEEE 802.21 standard taking into account different available wireless networks, the application requirements and the user preferences to improve QoS. Numerical results demonstrate that the ABCPSO algorithm compared to the related work has lower cost and delay, higher available bandwidth and less number of handover.