Recent technical innovation has driven the evolution of autonomous vehicles. To improve safety as well as on-road vehicular experience, vehicles should be connected with each other or to vehicular networks. Some specification groups, e.g., IEEE and 3GPP, have studied and released vehicular communication requirements and architecture. IEEE’s Wireless Access in Vehicular Environment focuses on dedicated and short-range communication, while 3GPP’s New Radio V2X supports not only sidelink but also uplink communication. The 3GPP Release 16, which supports 5G New Radio, offers evolved functionalities such as network slice, Network Function Virtualization, and Software-Defined Networking. In this paper, we define and design a vehicular network architecture compliant with 5G core networks to enable and support autonomous driving. As a validation example, a high-definition map needs to contain the context of trajectory for localization and planning of autonomous driving vehicles. We also propose new methods by which autonomous vehicles can push and pull map content efficiently, without causing bottlenecks on the network core. We evaluate the performance of the proposed method via network simulations and our autonomous driving vehicle on the road. Experimental results indicate that the proposed method improves the performance of vehicular content delivery in real-world road environments.

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

The security and privacy of electronic health records (EHRs) have received considerable attention from healthcare workers and researchers. To ensure security, various encryption and decryption schemes as well as key management protocols have been developed. However, owing to sharing and scalability issues, additional security technologies have been proposed. Nonetheless, these technologies cause other problems, such as efficiency issues. Blockchain-based EHR management systems have been proposed to overcome computational overhead. However, because most blockchain systems are installed by outsourcing companies, EHRs may be leaked to the company. Hence, we herein propose a blockchain-based EHR management scheme with proxy re-encryption. In this scheme, we set a proxy server that re-encrypts the ciphertext between file servers, thereby solving EHR sharing issues. Furthermore, because the server is separated from the blockchain system, the outsourcing company cannot manipulate the server or access the records. In addition, the blockchain assists in access control by using smart contracts, thereby enabling secure and efficient EHR sharing. By performing security analysis, we prove that our proposed scheme solves the aforementioned security problems. In addition, we experimentally demonstrate the efficient operation of the proposed system.

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

Software Defined Networking (SDN) is an emerging paradigm, which brings the network innovation and attracts both the industries and researchers. SDN is a programmable network model that separates the control logic from the forwarding plane. The centralize control plane takes care of all networking resources. The attackers target the SDN controller, to paralyze the logic plane that is considered as the brain of the network which provides a lot of benefits. However, due to the characteristics the control plane becomes the attractive target of security attacks for the adversaries. One of the most known threats is Distributed Denial-of-Service (DDoS) attacks with the goal to exhausting network resources by sending heavy traffic to them, causing network congestion. Since SDN was proposed, DDoS attack has become a popular research field in SDN security and many researchers have been presented DDoS attacks detection, prevention and mitigation solutions in SDN environment using different methods. This paper surveys the previous researches in DDoS attacks detection and mitigation based methods available in SDN environment.

In a personal area network (PAN) with wearable devices, the security of a file stored is an important concern. Secret sharing has received considerable attention for handling security, as it guarantees confidentiality and integrity even when some devices, whose count when taken together less than the minimum threshold, are compromised, lost, or stolen. However, because traditional secret sharing has tremendous computational overhead and requires a large storage space, it is not appropriate for battery-powered wearable devices. To overcome this limitation, combinatorial-based file sharing is proposed. However, this incurs another efficiency problem, as preparation of file storage and retrieval involves computational costs. Additionally, this file sharing has been designed without considering an environment with heterogeneous wearable devices. To overcome the aforementioned problems, we first propose a new fog network model that delegates the calculation for the preparation of file storage and retrieval to a fog node, such as a smartphone or tablet PC. A fog node has relatively faster operational ability and larger battery capacity, and hence the resources of the devices in the PAN can be managed efficiently. Additionally, we propose a new algorithm that generates file shares by considering the heterogeneous characteristics of wearable devices. In particular, we consider the factors of storage capacity and network speed to determine the sizes of the file shares. We demonstrate that our proposed model manages the file stored efficiently, using mathematical analysis and simulations.