The increase in popularity of vehicles encourages the development of smart cities. With this advancement, vehicular ad-hoc networks, or VANETs, are now frequently utilized for inter-vehicular communication to gather data regarding traffic congestion, vehicle location, speed, and road conditions. Such a public network is open to various security risks. Overall, protecting personal information on VANET is a vital responsibility. The integration of fog computing and VANETs has gained significant importance in recent years, driven by advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs remains a major challenge. To overcome this challenge, we introduce a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for secret communication, and provides privacy protection without the use of bilinear pairing. We rigorously prove the security of our proposed protocol, which is designed specifically for fog-based VANETs, and has been shown to meet their stringent security requirements. Moreover, we performed formal and informal analysis that shows our proposed protocol is highly efficient,our protocol’s computational and communication overhead are lower than those of other relevant protocols by 45.570% and 29.432%, respectively. Finally we use NS-3 simulation to prove that our proposed algorithm is a practical and scalable solution for secure communication in fog-based VANETs.

A massive multiple‐input multiple‐output (MIMO) system uses a large number of antennas in the base station (BS) to serve multiple users, which significantly improves the capacity of the system. However, in time division duplex (TDD) mode, the pilot contamination (PC) is inevitable due to the multiplexing of pilots. This paper proposed a pilot assignment based on graph coloring and location information (GC‐LI) to improve the performance of users. Specifically, based on graph coloring, the proposed GC‐LI algorithm combines location information like the angle of arrival (AoA), distance, and correlation to construct an interference graph. Then, we calculate the interference between any two users and use the postprocessing discrete Fourier transform (DFT) filtering process to effectively distinguish the users with nonoverlapping AoAs. Finally, according to the interference graph, the GC‐LI algorithm is proposed to mitigate the intercell interference (ICI) between users with the same pilot by assigning different pilots to connected users with high ICI metrics based on some regulation. Simulation results show that the GC‐LI algorithm is suitable for various types of cells. In addition, compared with the existing pilot assignment algorithms based on graph coloring, users’ average signal‐to‐interference‐plus‐noise ratio (SINR) and uplink achievable sum rate (ASR) are significantly improved.

Cognitive radio is introduced into the demand response management (DRM) of smart grid with the hope of alleviating the shortage of spectrum resources and improving communication quality. In this paper, we adopt an energy detection algorithm based on generalized stochastic resonance (GSRED) to improve the spectrum sensing accuracy under the circumstances of low signal-to-noise ratio without increasing system overhead. Specifically, a DRM scheme based on real-time pricing is investigated, and the social welfare is taken as the main index to measure system control performance. Furthermore, considering the adverse effects incurred by incorrect spectrum sensing, we incorporate the probability of the DRM system causing interference to primary user and spectrum loss rate into the evaluation index of the system control performance and give the final expression of the global optimization problem. The influence of sensing time on system communication outage probability and spectrum loss rate is elaborated in detail through theoretical derivation and simulation analysis. Simulation results show that the GSRED algorithm has higher detection probability under the same conditions compared with the traditional energy detection algorithm, thus guaranteeing lower communication outage probability and spectrum loss rate.

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.