Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicle’s reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the data set to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models.

The seamless integration of sensors and smart communication technologies has led to the development of various supporting systems for financial technology (FinTech). The emergence of the next-generation Internet of Things (Nx-IoT) for FinTech applications enhances the customer satisfaction ratio. The main research challenge for FinTech applications is to analyze the incoming tasks at the edge of the networks with minimum delay and power consumption while increasing the prediction accuracy. Motivated by the above-mentioned challenge, in this article, we develop a ranked-based service deployment strategy and an artificial intelligence technique for financial data analysis at edge networks. Initially, a risk-based task classification strategy has been developed for classifying the incoming financial tasks and providing the importance to the risk-based task for meeting users’ satisfaction ratio. Besides that, an efficient service deployment strategy is developed using$Hall's$theorem to assign the ranked-based financial data to the suitable edge or cloud servers with minimum delay and power consumption. Finally, the standard support vector machines (SVMs) algorithm is used at edge networks for analyzing the financial data with higher accuracy. The experimental results demonstrate the effectiveness of the proposed strategy and SVM model at edge networks over the baseline algorithms and classification models, respectively.

An emergency medical vehicle can save the patient’s life if it arrives at his location as quickly as possible. Unmanned aerial vehicles (UAVs) offer wide visibility and mobility, making them a viable choice for smart cities and intelligent transportation systems (ITSs) as edge devices for the Internet of Things (IoT). Based on population behavior and overcrowding, video surveillance through the Internet of multimedia things (IoMT) and public safety in smart cities can help determine the most efficient routes for emergency medical vehicles. This study investigates UAV overcrowding and abnormal population activity patterns, which affect the flow of emergency medical vehicles and traffic flow. Moreover, the purpose of this article is to analyze received video frames from UAVs in order to identify the most efficient route for emergency medical vehicles in smart cities to transfer patients in the event of abnormalities or overcrowding. In order to detect overcrowding on the streets, a hybrid Cascade-ResNet is utilized, which detects congestion based on many data points. Based on our proposed approach, we achieve a 2.5% improvement over similar methods because it is effective, flexible, and accurate. UAV video frames can be used to communicate with emergency response vehicles, to monitor traffic congestion, and to monitor other aspects of smart city life.

With the exponential growth of the Internet of Things (IoT) devices in Maritime Transportation Systems (MTS), the centralized cloud-centric framework can hardly meet the requirements of the applications in terms of low latency and power consumption. By inventing the distributed edge-centric framework, real-time IoT applications can meet the requirements of the MTS by analyzing the tasks at the edge of the networks. However, one of the critical challenges of the edge-centric MTS is to provide security and privacy between local IoT devices and distributed edge nodes. Motivated by that, in this paper, we design a blockchain-enabled edge-centric framework for analyzing the real-time data at the edge of the networks with minimum latency and power consumption while meeting the security and privacy issue of MTS. The introduction of blockchain and smart contract in the edge-centric MTS frameworks help to validate the transactions of each block at edge nodes by estimating the lifetime, belief, and trustfulness, and mitigate various types of security threats. Further, we introduce different classification models to predict the malicious vessels over the real-time maritime dataset at a secured edge-centric MTS framework. Extensive simulation results demonstrate that the superiority of the proposed strategy with baseline approaches under various performance metrics.

Abstract The Internet of things (IoT) and communication technologies are enabling the consumer to use the smart devices. The explosion of smart devices is shifting the IoT into a framework, which we call the cognitive IoT. The cognitive IoT can enhance many sectors such as smart cities, healthcare, industry 4.0, transportation, just to name a few. Most of the data produced in smart cities are wasted because the important information is not extracted due to lack of standard mechanism for knowledge extraction and archiving methods. This has attracted the attention of researcher to design new approaches of machine and cognitive learning that can handle vast amount of dynamic data. The cognitive smart city is the integration of IoT, smart city technology, real‐time big data analytics and artificial intelligence (AI) strategies for proactive actions. The services in smart cities relies on the collection and analysis of the data which are provided by the use themselves or accessed by the services providers. The citizen engagement is the key for success of smart city; however, the engagement may get reduced due to privacy concerns arising from data collection. Therefore, privacy‐preservation shall be achieved in a manner where valuable data is exchanged with service provider, and other third party while protecting the citizens' privacy, upholding data laws and enforcement. Therefore, there is a need to control the anonymization and mix some more techniques to preserve the quality of the data. The proposed formal method for privacy‐preservation in smart cities is based on pseudonymization, clustering, anonymization and differential privacy methods. The modified clustering algorithm selects the initial cluster based on the concept of dissimilarity between the data sequences. We have assessed the functional correctness and preformation of the proposed model for privacy‐preservation in smart cities. The proposed method has lower discriminating rate as compared to other existing methods.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

peer reviewed

With the help of the new generation information technology, the Internet of Vehicle (IoV) networks have become widespread. IoV can improve the automatic driving ability, and provide users with intelligence, comfort, safety, energy saving and efficiency traffic services. However, the IoV networks face serious challenges due to the complex wireless environment. The vehicles cannot obtain the real-time traffic condition and early warning information, which leads to the decrease of link quality and the failure of information transmission. To evaluate the communication quality of IoV networks, the outage probability (OP) is commonly employed as a metric. This paper considers mobile IoV networks, and investigates communication quality prediction. Novel OP expressions are derived, which can analyze the OP performance. Then, to predict OP in real time, an intelligent OP prediction approach with an Elman model is proposed. This is evaluated with data generated using the OP expressions. In terms of computational complexity and prediction accuracy, the results obtained show that the Elman-based approach provides better forecasting effect than other methods. For prediction accuracy, the proposed Elman approach is increased by 84.6%. For computational complexity, the execution time is reduced by 79.9%.

The birth of beyond 5G (B5G) and emerge of 6G have made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy-efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This article addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multiagent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are, respectively, introduced for optimization. Furthermore, the work analyzes the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

The sixth generation (6G) is envisioned to be a spawned key technology that will support the ubiquitous and seamless connection of a massive number of Internet-of-Things (IoT) devices. The extremely high data rate, low end-to-end delay, high mobility of IoT devices propel the desideratum of extenuating the concern of reducing the energy consumption, i.e., green communication. Hence, in this article, we address the concern of green communication in 6G-enabled massive IoT devices by following the cluster-based data dissemination in the network. We propose a novel hybrid whale spotted hyena optimization (HWSHO) algorithm by synthesizing the whale optimizer algorithm (WOA) with exploitation capabilities of spotted hyena optimizer (SHO). We perform a simulation experimental study that shows the supreme performance of our proposed technique over the most recent proposed energy-efficient data dissemination methods. The proposed technique is an exemplary solution that could be pertinent to various hostile applications seeking green communication of 6G-enabled IoT devices.