Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, anovel5G automobile network emergency situationsbased onChebyshev polynomial using fog computing and authentication is proposed.This is the first study to useChebyshev chaotic mapping algorithmthatuses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme hasfivestages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. Theformalsecurity analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response.The evaluation showed thatthe proposed method cannot execute forgery, impersonation, or replay attacks.Theproposed method has lower computational, communication, and storage overhead than earlier efforts.

In this research, a set of data mining techniques are applied to target to balance between the industry requirement and user satisfaction. The proposed approach aims at exploring the most significant attributes for the industry services’ evaluation. The exploration goal ensures a double-sided benefit for both the industry and the user. From one perspective, it raises the evaluation accuracy level for the main service’s attributes which is most important to the user and consequently leads to higher user satisfaction. On the other side, it minimizes the user’s collaboration effort in the evaluation process which raises the user’s collaboration willingness. The proposed approach has been applied to the IoT services industry in Saudi Arabia. The results proved that eliminating insignificant attributes has provided minimal user effort with retaining the required evaluation accuracy and the success percentage reached 90%.

Surveillance systems are essential for social and personal security. However, monitoring multiple video feeds with multiple targets is challenging for human operators. Therefore, automatic and smart surveillance systems have been introduced to support or replace traditional surveillance systems and build safer communities. Advancements in artificial intelligence techniques, particularly in the field of computer vision, have boosted this area of research. Most existing works have focused on image‐based (RGB‐based) machine learning and deep learning algorithms for detecting anomalous and violent events. In this study, we propose a unique Ensemble Spatial–Temporal Skeleton‐Based Graph Convolutional Networks (ESTS‐GCNs) model for violence detection that automatically uses spatial and temporal data to detect violence in surveillance videos. Skeleton‐based algorithms are less sensitive to pixel‐based noise and background interference, making them excellent candidates for activity and anomaly detection. Our proposed ensemble‐based architecture utilizes Graph Convolutional Networks (GCNs) and comprises multiple spatial and temporal modules. Three different spatial pipelines are exploited: channel‐wise topologies, self‐attention mechanism, and graph attention networks. The models were trained and evaluated using two skeleton‐based datasets introduced by us: Skeleton‐based Real‐Life Violence Situations (RLVS) and NTU‐Violence (NTU‐V). Our model achieved a maximum accuracy of around 93% and outperformed existing models by more than 10%.

Recent advancements in computation and communication technologies and the increasing adoption of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies have paved the way to tremendous developments in modern transportation systems. Driven by the massive number of connected vehicles and the stringent requirements of the public traffic management system, the transportation of data to and from the centralized cloud servers poses a great challenge. As a result, to meet the computational requirements and handle the massive amount of sensory data efficiently, the potential solution is to process/analyze the data at the edge of the network. Motivated by the challenges mentioned above, in this paper, we design a new empirically intelligent XGboost (EIXGB)-enabled logistic transportation system at the edge network for analyzing the data efficiently. Besides that, the proposed EIXGB technique intends to obtain real-time results based on the monitoring parameters of the public traffic management system with higher accuracy and minimum error. Extensive simulation results demonstrate the efficiency of the proposed EIXGB technique over the standard machine learning techniques using a set of parameters. The proposed technique achieves 87-97% accuracy over the different sets of features of a real-time dataset as per the simulation results.