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Vehicular ad hoc networks (VANETs) have emerged as an appropriate class of information propagation technology promising to link us even while moving at high speeds. In VANETs, a piece of information propagates through consecutive connections. In the most previous vehicular connectivity analysis, the provided probability density function of intervehicle distance throughout the wide variety of steady-state traffic flow conditions is surprisingly invariant. But, using a constant assumption, generates approximate communication results, prevents us from improving the performance of the current solutions and impedes designing the new applications on VANETs. Hence, in this paper, a mesoscopic vehicular mobility model in a multilane highway with a steady-state traffic flow condition is adopted. To model a traffic-centric distribution for the spatial per-hop progress and the expected spatial per-hop progress, different intervehicle distance distributions are utilized. Moreover, the expected number of hops, distribution of the number of successful multihop forwarding, the expected time delay and the expected connectivity distance are mathematically investigated. Finally, to model the distribution of the connectivity distances, a set of simplistic closed-form traffic-centric equations is proposed. The accuracy of the proposed model is confirmed using an event-based network simulator as well as a road traffic simulator.

Internet of Things (IoT) technology is one of the successful and interesting products of ubiquitous wireless networks that beneficiate human life with the make use of smart devices. Many companies have a significant plan to develop and utilize the IoT benefits and futures. Context‐aware computing applications are extensively used anywhere and anytime but they may violate or abuse human privacy. For example, if an IoT healthcare resource like a pacemaker acts as a backdoor on a human body, human life will be in danger. Moreover, targeting the privacy of IoT users is much more catastrophic compared with targeting network security as well as network denial of service. In this article, we review the security vulnerabilities and prerequisite of the IoT resource management by pointing out to the IoT architecture in terms of the principal abstraction layer and summarize the corresponding IoT security terms. Next, we define IoT penetration testing, IoT backdoor types, backdoor severity, backdoor mechanism, and propose the corresponding backdoors detection techniques. Finally, we review the reported IoT backdoors and investigate the limitation of backdoors detection and highlight future directions.

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