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Deep learning (DL) has emerged as a key application exploiting the increasing computational power in systems such as GPUs, multicore processors, Systems-on-Chip (SoC), and distributed clusters. It has also attracted much attention in discovering correlation patterns in data in an unsupervised manner and has been applied in various domains including speech recognition, image classification, natural language processing, and computer vision. Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. In addition, now, a number of commercial vendors also offer accelerators for deep learning systems (such as Nvidia, Intel, and Huawei).

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Mobile users suffer from deteriorating signal quality due to vehicle penetration losses. To solve this, small cells are deployed within the vehicles to improve the Quality of Service (QoS). These small cells called moving small cell access points (MSAPs), however, suffer from backhaul issues since they would have to send a huge amount of data to the core network. To solve the backhaul problem, cloud radio access network (CRAN) along with the millimeter wave (mmwave) can be a viable solution for moving vehicles. However, in order to realize its potential benefits, an effective remote radio head (RRH) deployment strategy and the resource-efficient allocation are needed. In this paper, we investigate the placement of RRH alongside a railway track; then, for the placed RRH, a joint time slot and power allocation problem are formulated with an objective of maximizing the resource efficiency (RE) of the MSAP backhaul network. An optimal Branch and Bound Algorithm (BnBA) is proposed for the constituted non-linear integer problem, and the effects of changing various model parameters are investigated. The simulation results show that our proposed algorithm deviates 52% of the sub-optimal result.

Emerging technologies rapidly change the essential qualities of modern societies in terms of smart environments. To utilize the surrounding environment data, tiny sensing devices and smart gateways are highly involved. It has been used to collect and analyze the real-time data remotely in all Industrial Internet of Things (IIoT). Since the IIoT environment gathers and transmits the data over insecure public networks, a promising solution known as authentication and key agreement (AKA) is preferred to prevent illegal access. In the medical industry, the Internet of Medical Things (IoM) has become an expert application system. It is used to gather and analyze the physiological parameters of patients. To practically examine the medical sensor-nodes, which are imbedded in the patient's body. It would in turn sense the patient medical information using smart portable devices. Since the patient information is so sensitive to reveal other than a medical professional, the security protection and privacy of medical data are becoming a challenging issue of the IoM. Thus, an anonymity-based user authentication protocol is preferred to resolve the privacy preservation issues in the IoM. In this paper, a Secure and Anonymous Biometric Based User Authentication Scheme (SAB-UAS) is proposed to ensure secure communication in healthcare applications. This paper also proves that an adversary cannot impersonate as a legitimate user to illegally access or revoke the smart handheld card. A formal analysis based on the random-oracle model and resource analysis is provided to show security and resource efficiencies in medical application systems. In addition, the proposed scheme takes a part of the performance analysis to show that it has high-security features to build smart healthcare application systems in the IoM. To this end, experimental analysis has been conducted for the analysis of network parameters using NS3 simulator. The collected results have shown superiority in terms of the packet delivery ratio, end-to-end delay, throughput rates, and routing overhead for the proposed SAB-UAS in comparison to other existing protocols.

The Internet of Things (IoT) paradigm has integrated the sensor network silos to the Internet and enabled the provision of value-added services across these networks. These smart devices are now becoming socially conscious by following the social Internet of Things (SIoT) model that empowers them to create and maintain social relationships among them. The Social Internet of Vehicle (SIoV) is one application of SIoT in the vehicular domain that has evolved the existing intelligent transport system (ITS) and vehicular ad-hoc networks (VANETs) to the next phase of Intelligent by adding socializing aspect and constant connectivity. SIoV generates a massive amount of real-time data enriched with context and social relationship information about vehicles, drivers, passengers, and the surrounding environment. Therefore, the role of privacy management becomes essential in SIoV, as data is collected and stored at different layers of its architecture. The challenge of privacy is aggravated because the dynamic nature of SIoV poses a major threat in its adoption. Motivated by the need to address these aspects, this paper identifies the challenges involved in managing privacy in SIoV. Furthermore, the paper analyzes the privacy issues and factors that are essential to be considered for preserving privacy in SIoV environments from different perspectives including the privacy of a person, behavior and action, communication, data and image, thoughts and feelings, location and space, and association. In addition, the paper discusses the blockchain-based solutions to preserve privacy for SIoV.

As the integration of smart mobile devices to the Internet of Things (IoT) applications is becoming widespread, mobile device usage, interactions with other devices, and mobility patterns of users carry significant amount of information about the daily routines of the users who are in possession of these devices. This rich set of data, if observed over a time period, can be used to effectively verify a user. In previous works, verification of users on personalized electronic devices via biometric properties, such as fingerprint and iris, has been successfully employed to increase the security of access. However, with the integration of social networks with the IoT infrastructure and their popularity on smart handheld devices, identification based on behavior over social networks is emerging as a novel concept. In this paper, we propose an intelligent add-on for the smart devices to enable continuous verification of users. In the experiments, we use data from built-in sensors and usage statistics of five different social networking applications on mobile devices. The collected feature set is aggregated over time and analyzed using machine learning techniques. We show that when smart devices are equipped with continuous verification intelligence, it is possible to verify users with less than 10% false rejection probabilities, and the users can keep using the devices with no interruption for biometric authentication 90% of the time. In the case of anomalous behavioral patterns, the proposed system can verify genuine users with up to 97% success ratio using an aggregated behavior pattern on five different social network applications.

The increasing number of applications based on the Internet of Things, as well as advances in wireless communication, information and communication technology, and mobile cloud computing, has allowed mobile users to access a wider range of resources when mobile. As the use of vehicular cloud computing has become more popular due to its ability to improve driver and vehicle safety, researchers and industry have a growing interest in the design and development of vehicular networks for emerging applications. Vehicle drivers can now access a variety of on demand resources en route via vehicular network service providers. The adaptation of vehicular cloud services faces many challenges, including cost, privacy, and latency. The contributions of this paper are as follows. First, we propose a game theory-based framework to manage on-demand service provision in a vehicular cloud. We present three different game approaches, each of which helps drivers minimize their service costs and latency, and maximize their privacy. Second, we propose a quality-of-experience framework for service provision in a vehicular cloud for various types of users, a simple but effective model to determine driver preferences. Third, we propose using the trusted third party concept to represent drivers and service providers, and ensure fair game treatment. We develop and evaluate simulations of the proposed approaches under different network scenarios with respect to privacy, service cost, and latency, by varying the vehicle density and driver preferences. The results show that the proposed approach outperforms conventional models, since the game theory system introduces a bounded latency of ≤3%, achieves service cost savings up to 65%, and preserves driver privacy by reducing revealed information by up to 47%.

Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.