Effective information dissemination constitutes the cornerstone of communication in Vehicular Ad hoc Networks (VANETs). Avoiding broadcast storm is one of the considerable challenges in the development of an effective dissemination scheme for VANETs, which leads to extensive rebroadcasting. Reliable emergency messages delivery is another substantial measure of performance, especially in dense traffic and high mobility scenarios. A generic solution to deal with these problems is to cluster vehicles, so that emergency messages can be re-broadcasted to adjacent clusters only by a smaller number of vehicles. Thus, reliable selections of cluster head and relay nodes are important to reduce the number of retransmissions. In this regard, we propose a clustering technique for Reduced Broadcast Overhead Scheme for Emergency Message Dissemination (RBO-EM). RBO-EM is based on the mobility metrics to strengthen the cluster formation, avoid communication overhead and maintain the message reliability in a high mobility scenario. Moreover, we introduce relay nodes based on estimated link stability to limit the number of retransmissions. RBO-EM is evaluated against eminent schemes by varying traffic densities and speed. Simulation results indicate that RBO-EM enables a reasonable performance gain in terms of coverage, end-to-end delay and message reliability.

Enhancing spectrum utilization efficiency (SUE) to accommodate the multitude of 5G-based IoT devices within the available scarce spectrum has been a pivotal point of research in the current decade. Equipped with interweave and underlay modes, cognitive radio networks (CRNs) are envisioned to be the most promising technology for SUE enhancement. Since the 5G-based IoT is swiftly transforming into a heterogeneous network, hybrid underlay-interweave mode CRNs appear to be the optimal key for SUE enhancement. Besides enhancing SUE, ensuring fairness among secondary users (SUs) to equitably utilize the network's resources in the wake of primary users' (PUs) service emergence has been a critical problem in CRNs. Considering the importance of SUE enhancement, in this paper, we investigate two problems. Firstly, we precisely analyze, in a novel way, the performance of hybrid underlay-interweave mode of CRNs for SUE enhancement from the standpoint of SUs. For this purpose, we propose a hybrid underlay-interweave mode enabled CRN scheme and apply it on a legacy CRN. We model the proposed scheme with continuous time Markov chain and derive closed form expressions for various SUE-related quality of service parameters. The analysis is conducted under dynamic reservation of channels for interrupted users, considering the effects of multi-levels network traffic loads and varying channel failure rates. Secondly, we propose a multi-attribute based fairness driven algorithm for determination and interruption of SUs' services to ensure fairness among services in the network's resources utilization. We evaluate our proposed scheme under both perfect and imperfect spectrum sensing scenarios. The obtained results demonstrate that, as compared to the state-ofthe-art, the proposed scheme significantly enhances SUE while the proposed algorithm achieves a noticeable fairness among SUs' services. INDEX TERMS Cognitive radio networks, dynamic spectrum access, dynamic channel reservation, hybrid underlay-interweave mode, spectrum utilization efficiency.

In Vehicular Adhoc Networks (VANETs), disseminating Emergency Messages (EMs) to a maximum number of vehicles with low latency and low packet loss is critical for road safety. However, avoiding the broadcast storm and dealing with large-scale dissemination of EMs in urban VANETs, particularly at intersections, are the challenging tasks. The problems become even more challenging in a dense network. We propose an Effective Emergency Message Dissemination Scheme (EEMDS) for urban VANETs. The scheme is based on our mobility metrics to avoid communication overhead and to maintain a stable cluster structure. Every vehicle takes into account its direction angle and path loss factor for selecting a suitable cluster head. Moreover, we introduce estimated link stability to choose a suitable relay vehicle that reduces the number of rebroadcasts and communication congestion in the network. Simulation results show that EEMDS provides an acceptable end-to-end delay, information coverage, and packet delivery ratio compared to the eminent EM dissemination schemes.

Smart health systems typically integrate sensor technology with the Internet of Things, enabling healthcare systems to monitor patients. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. However, the communication between the edge node and the mobile user is susceptible to impersonation attacks in mobile edge computing (MEC) for the biomedical application. For this purpose, we propose a detection mechanism for medical and healthcare services, i.e., reinforcement learning for impersonation attacks. We construct a system model of MEC, a key generation model (KGM), and an impersonation attack model (IAM). In addition, we also design an impersonation attack detection algorithm based on the SARSA technique under the IAM. In our proposed work, the SARSA-based method outplays the detection of impersonation attacks in the dynamic environment compared to the traditional Q-learning technique. Finally, we evaluate the false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER) in the hypothesis tests to compare the performance of our proposed method with the traditional Q-learning. In comparison to the classic Q-learning based technique, simulation experiments show that the suggested approach can avoid impersonation attacks in a dynamic environment for medical and healthcare services. The results also indicate that the SARSA technique has a high detection accuracy and low average error rate compared to the conventional Q-learning based approach.

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