Abstract-In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels. The primary network consists of a source and a destination. Depending on whether the secondary network which consists of two source nodes have a single relay for cooperation or multiple relays thereby employs opportunistic relay selection for cooperation and whether the two source nodes suffer from the primary users' (PU) interference, two cases are considered in this paper, which are referred to as Scenario (a) and Scenario (b), respectively. For the considered underlay spectrum sharing, the transmit power constraint of the proposed system is adjusted by interference limit on the primary network and the interference imposed by primary user (PU). The developed new analysis obtains new analytical results for the outage capacity (OC) and average symbol error probability (ASEP). In particular, for Scenario (a), tight lower bounds on the OC and ASEP of the secondary network are derived in closed-form. In addition, a closed from expression for the end-to-end OC of Scenario (a) is achieved. With regards to Scenario (b), a tight lower bound on the OC of the secondary network is derived in closed-form. All analytical results are corroborated using Monte Carlo simulation method.

A vehicle ad-hoc network (VANET) is an essential component of the intelligent transportation system. In VANET, vehicle-to-vehicle communication plays a significant role in providing a secure network and safe exchange of information between nodes. However, VANET is prone to critical risks, threats, and attacks due to its unique characteristics and dynamic topology. Trust is one of the most critical aspects of security. So, this concept has already proved its accuracy and efficiency in various wireless applications. To ensure trust in vehicular communications, we propose a decentralized prediction and reputation approach that includes two models. First, we introduce a reputation model based on the Bayesian theorem and watchdog concept. We investigate the vehicle's behavior in the network to measure the vehicle's trust rate. Then, we present a prediction model to anticipate the vehicle's state and detect malicious vehicles. Based on this approach, vehicles can avoid interacting with unreliable vehicles and ignore incoming messages from them to reduce the effect of malicious nodes in the network. Our extensive simulations show the performance and accuracy of our approach. Also, the certainty rate remains between 91% and 99% regardless of the simulation scenario's duration. This simulation also demonstrates our classification's certainty, even under different network situations.

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In this paper, we propose a multidimensional trust model for vehicular networks. Our model evaluates the trustworthiness of each vehicle using two main modes: 1) Direct Trust Computation DTC related to a direct connection between source and target nodes, 2) Indirect Trust Computation ITC related to indirectly communication between source and target nodes. The principal characteristics of this model are flexibility and high fault tolerance, thanks to an automatic trust scores assessment. In our extensive simulations, we use Total Cost Rate to affirm the performance of the proposed trust model.

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