No abstract

No abstract

Vehicular Ad Hoc NETworks (VANETs) has been developed to be, as a part of the Internet of Vehicles (IoVs), one of the most promoting technologies in the near future. Extensive researches are focusing on developing appropriate solutions in order to provide better Quality of Services (QoS) to these latter. This paper deals with network performance improvement based on clustering mechanism to reduce the number of communications caused by the high frequency of arrival/departure of vehicles while maintaining relevant clock synchronization solutions adequately operating. Thus, we attempt to adopt certain known clustering algorithms to our previous solution of time synchronization in VANETs namely OTRB (Osets Table Robust Broadcasting) in order to evaluate the impact of a hierarchical communication on time synchronization process. An analytical study with a comparison on the inuence of the clustering mechanism is given. The comparison takes place using simulation software NS2 (Network Simulator 2) and VanetMobiSim (VANET Mobility Simulator). The performance parameters include the average of arrival/departure of nodes and the number of isolated nodes. Simulation results reveal that an appropriate method should reduce the overhead of re-clustering and lead to an ecient network coverage with the best time synchronization rate.

The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

Internet of Drones (IoD) plays a crucial role in the future Internet of Things due to its important features such as low cost, high flexibility, and mobility. The number of IoD applications is drastically increasing from military to civilian fields. Nevertheless, drones are resource-constrained and highly vulnerable to several security threats and attacks. The use of blockchain technology for securing IoD networks has gained growing attention. To this end, this paper presents a systematic literature review to analyze the current research area regarding the security of IoD environments using the emerging blockchain technology. Forty relevant studies were selected from 129 published articles to answer the identified research questions. The selected studies were classified into three main classes based on blockchain type. Furthermore, a comparison of the reviewed articles in terms of different factors is provided. The research findings show that the blockchain can guarantee fundamental security requirements such as authentication, privacy-preserving, confidentiality, integrity, and access control. Finally, open issues and challenges related to the combination of blockchain and IoD technologies are discussed.

No abstract