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In recent years researchers have shown significant interest in vehicular networks to augment road safety by providing real-time messaging services among vehicles. This work aims to provide a detailed analysis of emergency message dissemination techniques for the Internet of Vehicles (IoV). We explored position-based data dissemination techniques for emergency message dissemination, which is considered the best routing method because it does not rely on predestination entries of the route. Position-based schemes encounter some challenges, such as delay and accurate positioning. Existing survey papers of IoV focused on architecture, technologies, and layers. However, this article examines a brief comparison of subtypes of position-based emergency message routing, beacon-oriented and beacon-less techniques. In the end, we presented the basic challenges of emergency message dissemination; moreover, future directions are highlighted to promote the development of new protocols for emergency message dissemination to enhance the efficiency of IoV in a better way.

Video services are becoming more pervasive due to the swift development of next-generation intelligent communications and network systems. With the growth of these types of services, the numbers of users are increasing at an exponential rate, and numbers of devices are being used for it, e.g., wearable equipment, smartphones, tablets, personal computers or laptops, and smart televisions. Real-time object tracking and identification of the state of drowsiness of driver is a challenging area of research. An increasing number of good results are reported concerning its robustness and accuracy. Frequently, no clear statements about the tracking situations are made, when it comes to the application of these methods to real-world problems. In order to address the problem of identification of the state of the drowsiness of driver, a research project has been initiated in the Kingdom of Saudi Arabia. This research aims to avoid road accidents due to the drowsiness of drivers by keeping them active through suitable means. Different methods are analyzed to detect the driver’s drowsiness, and a state of the art solution is proposed in this research. At the current state, we are considering the openness and closeness of eyes for detecting the driver’s drowsiness. Various techniques are analyzed to keep the fatigued driver active. In the case of the inevitable effect of drowsiness and the abnormal response of the driver towards alerts, the driver’s location and sufficient information of the driver’s state are communicated with the Police Base station. As a result of this research, we are optimistic about coming up with highly positive results in its social, economic and industrial benefits. Extending this research towards the product and its development on a large scale can make a significant contribution to the Kingdom’s economy as well.

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.

Multimedia content in 5G/6G networks makes safe, confidential, and efficient content delivery difficult. Intelligent systems that adapt to the ever‐changing network environment are needed to distribute multimedia content in these networks. Reinforcement learning (RL) can optimize multimedia content distribution based on network congestion, capacity, and user preferences. This study proposes RL‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments incorporating several aspects of the suggested framework. The framework delivers multimedia material securely and privately with great quality. This study provides an intelligent multimedia content delivery architecture that uses RL approaches to solve 5G/6G content delivery problems. This research presents an RL system optimized with the double DQN algorithm having a reward of 51604.93 in 7000 episodes for efficient video file sharing on intracity buses. The RL agent balances network congestion and bandwidth by leveraging multiple sources such as bus and intersection caches and base stations, improving secure multimedia content delivery in 5G/6G networks and enhancing the passenger experience. The study confirms the system's effectiveness using reward and loss metrics and identifies potential future research directions. Future work could explore additional RL algorithms, scalability for larger networks, complex delivery scenarios, and integration with blockchain and edge computing for improved security and efficiency in multimedia content delivery. This study proposes reinforcement learning (RL)‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments that incorporate several aspects of the suggested framework.

In today's smart world, the privacy protection of data is an important issue. Data is distributed, reproduced, and disclosed with extensive use of communication technologies. Many non-traditional challenges arise with the rapid increase of IoT devices for system design and implementation. However, security and privacy are the main issues in IoT. With advanced technologies, an illegal copy of the content can easily be generated and shared. Therefore, it is crucial for users to protect and secure their data. In the said perspective, an efficient third-generation watermarking technique is proposed, which works on the computational intelligence model to insert a large amount of robust watermark and make an extra effort to hide more information than first and second-generation techniques. The Advanced Encryption Standard (AES) encryption algorithm is employed to guarantee secure communication, which has a significantly less computational cost. The proposed technique evaluated parameters including security, robustness, imperceptibility, and capacity. The results of the proposed technique are compared to existing text watermarking methods, which illustrates it is secure, robust, imperceptible, and inserts a large amount of watermark information through computational intelligence.

Anomalies in cardiac functionality can be fatal. Early detection of these anomalies, and in many cases their precursors, can save lives. The probability of the occurrence of these anomalies is extremely among people with a pre-diagnosed heart condition. In this research, we discovered that much remote Electrocardiography (ECG) monitoring systems do not convey “enough” information to the diagnosing doctor or the nominated caregiver. A few examples of this information can be the type of cardiac abnormality, the exact waveform of the ECG signal, time and frequency of the occurrence of the anomaly, machine-understandable part so that medical SCADA be alerted about the case, and immediate preventative urgent steps correlated to that emergency. It is also important to delivery surrounding context to the Health Information System so that the medical expert make his/her diagnosis with ample support data. The most important component in this communication is the security of contents from cybercrimes. We propose a cost-efficient and non-invasive health monitoring system that is secure and quickly deployable. The presented system embeds an intelligent wearable data acquisition system with unique identification algorithms requiring very little computational time and simple threshold-based classification.

The articles in this special issue have solicited high-quality manuscripts that describe in-depth the breadth of real-world blockchain-based multi-disciplinary IIoT deployments and present actual experiences in resolving contextual blockchain-related challenges. In addition, these articles develop and share best practices, vision realizations and lessons learned in this integrated environment and establish guiding principles for technical, operational and business successes.

The flow of data traffic has been vastly increased because of the implementation of the Internet of Things (IoT)-and security is a top need for Internet communication. The encryption-decryption process ensures that data access is restricted to legitimate users. But there is a tradeoff between the level of security and the communication overhead.

As networks have continued to gain popularity throughout the 1980s and well into today, communication technologies have taken a critical number of major t wists and turns, with a wide variety of wireless technologies, protocols, services, and configurations developed and deployed. There have been murmurs of network functions virtualization and software-defined networking in the past 10 years, but these technologies have been caught in the spotlight of attention in recent years. Today, network functions (e.g., firewalls) reside in the cloud to increase network agility and scalability while also enabling effective use of net work resources. Similarly, mobility has advanced from an employee’s former luxury to today ’s business necessity. Mobile devices and smartphones have not only transformed personal communicat ions but also increased business productivity and revolutionized society. Consonantly, the Internet of Things (IoT) has been dominating the technological landscape, from autonomous connected vehicles to wearable devices.

As we delve into the Internet of Things, we are witnessing intensive interaction and heterogeneous communication among different devices over the Internet. Consequently, these devices generate a massive volume of big data. The potential of these data has been analyzed by complex network theory, describing a specialized branch, known as "human dynamics." In this extension, the goal is to describe human behavior in the social area in real time. These objectives are starting to be practicable through the quantity of data provided by smartphones, social networks, and smart cities. These make the environment more intelligent and offer an intelligent space to sense our activities or actions and the evolution of the ecosystem. To address the above-mentioned needs, this work presents a new concept of SmartBuddy that focuses on the analysis, the ecosystem provided by smart cities, wearable devices (e.g., body area network), and big data to determine human behaviors as well as human dynamics.

Pirated application developers find an alternate way to publish pirated versions of the same Android mobile applications (apps) on different Android markets. Therefore, a centralized, automated scrutiny system among multiple app stores is inevitable to prevent publishing pirated or cloned version of these Android applications. In this paper, we proposed an Android clone detection system for Internet of things (IoT) (Droid‐IoT) devices. First, the proposed system receives an original Android application package (APK) file along with possible candidate cloned APKs over the cloud network. The system uses an apkExtractor tool to extract Dalvik Executable (DEX) files for each subject program. The Jdex decompiler is used to extract Java source files from DEXs. Then, the bag‐of‐word model is used to extract tokenized features from source files. Further, the weighting filters are used to zoom the importance of each token. Moreover, Synthetic Minority Oversampling is applied to retrieve balanced features for better training of data. Finally, TensorFlow with Keras deep learning model is designed to predict clones in Android applications. The experimental results have shown that Droid‐IoT can successfully detect cloned apps with an accuracy of up to 96%. The primary purpose of this system is to prevent the publishing of pirated apps among different app stores under different pirated names. App cloning is a process of creating similar apps availablein any app store by different developer name and therefore, a centralized, automated scrutiny system is required to prevent publishing pirated or cloned version of android applications. This paper presented an android clone scammers detection Android clone detection system for Internet of things (IoT)(Droid‐IoT) devices. The primary purpose of this system is to prevent the publishing of pirated apps among different app stores under different pirated names will send through email.

The growing volume of traffic generates the ultimate challenge on the research for traffic control. Vehicle automation is getting closer to reality due to the massive mobile communication capacity provided by fifth‐generation (5G) wireless sensor networks (WSNs). Nowadays, manually controlling and monitoring vehicles at traffic rush hours is becoming difficult for safety control. Thus, the present condition requires providing control services and devices, which are functioning in real‐time worldwide mode through soft Internet of Vehicles (IoV) services over a software‐defined network (SDN). The SDN makes software integration and progress monitoring much easier and convenient. This article focuses on WSN‐based vehicle automation following controlling and observation through speech recognition and image processing. The article also demonstrates the working prototype of the proposed mechanism to automatically drive a car and to detect obstacles through voice and image‐based deep learning, respectively. Moreover, obstacle detection on the road is a major concern in vehicle controlling systems. The system detects every obstacle on roads using cameras mounted to cover the view for driver assistance. Once the obstacle found, it generates alert for the driver to stop a collision. All these experiments require a high‐speed communication platform for evaluations. A case study is used on vehicle observation and controlling using speech commands and image processing with real‐world on‐road vehicle data sets. Vehicle automation is getting closer to reality due to massive mobile communication capacity provided by the fifthgeneration (5G) Wireless Sensor Network (WSN). Nowadays, manually controlling and monitoring vehicles at traffic rush hours is becoming difficult enough for safety control. Thus, the present condition requires providing control service sand devices, which are functioning in real‐time worldwide mode through the soft Internet of Vehicles (IoV) services over Software Defined Network (SDN).