The significant advancement of blockchain technology in the field of industrial networks has created a meaningful impact and for this, it comes to the field of the military where blockchain can have a greater impact. In the military environment, a lot of machines and smart devices are connected. Unmanned aerial vehicle (UAV) combined with mobile edge computing (MEC) can offer real-time services. However, through the UAV, smart devices and intelligent machines, attackers can easily access. This leads to disruption on operations and revealing the data confidentiality to an adversary. To prevent this issue an architecture is proposed based on blockchain technology. The architecture is applied to provide a solution for malicious cyber-attacks and prevent data loss to ensure the security on device-to-device (D2D) communication in the military network to provide maximum security. Simulation results illustrate that how efficaciously the applied method can work for restorative the security on D2D communication. Finally, in conclusion, how the proposed framework can be enhanced by applying other feasible techniques along with future research directions is discussed.

A UAV-assisted Blockchain Based Secure Device-to-Device Communication in Internet of Military Things

Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies; however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.

Analysis of Security Issues and Countermeasures for the Industrial Internet of Things

Fifth generation (5G)-industrial Internet of things (IIoT) is the integration of IIoT and a private 5G network. The IIoT is a concept that involves incorporating smart objects, gadgets, and solutions into cutting-edge industrial operations to increase reliability, efficiency, and over-production costs. Furthermore, the integration of IIoT and 5G/beyond 5G (B5G) provides the potential for ubiquitous and instantaneous connectivity. The 5G architecture can handle the IIoT’s stringent ultra-low latency, realtime processing, high data rate, nearby storage, and reliability requirements. A new era of economic growth is predicted for IIoT-assisted 5G/B5G wireless networks. It should be noted that the majority of the work in IIoT is focused on the architecture, with reliability and throughput being largely ignored. This paper provides a comprehensive review of B5G assisted IIoT wireless networks, with a focus on enhanced mobile broadband (eMBB) and ultra-reliable low latency communication (URLLC) services. Furthermore, it provides insights into various applications and key enabling technologies from the perspective of URLLC, eMBB, and their tradeoff.

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URLLC and eMBB in 5G Industrial IoT: A Survey

This paper proposes a face recognition for security system based on artificial intelligence and edge computing with a limited dataset. Generative adversarial networks (GANs) are used to manipulate the dataset in order to overcome the accuracy issue of the limited dataset. The average accuracy of GANs outperforms the limited dataset up to 92.79%. Edge computing is used to overcome a high latency of cloud computing with Jetson Nano board, which produces an average of 8.8 frame-per-second. Furthermore, the detected face will make a payment using a smart contract to the blockchain network with low static difficulty to open a gate or door's lock. In comparison, a low static difficulty outperforms the Proof-of- Work consensus algorithm in terms of transaction time around 33–39 milliseconds.

Generative Adversarial Networks Based on Edge Computing With Blockchain Architecture for Security System

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

This letter proposes dual fieldbus communication for Industrial Internet of Things using an edge server. Dual fieldbus communication implements a redundant fieldbus to prevent bus faults and increase the system’s availability. The primary communication link uses Modbus while the redundant fieldbus is a Controller Area Network (CAN). When an error occurs, the protocol changes automatically to use the CAN bus because of its faster error recovery. It outperforms the conventional Modbus for both detected data error and data success rates. Meanwhile, the edge server implements a long short-term memory (LSTM) and performs 95 percent faster than processing in the cloud.

Kevin Putra Dirgantoro

Papers

Generative Adversarial Networks Based on Edge Computing With Blockchain Architecture for Security System

Dual fieldbus industrial IoT networks using edge server architecture

Blockchain side implementation of Pure Wallet (PW): An offline transaction architecture

Private Ethereum Blockchain for Industrial Internet of Things (IIoT)

Feasibility study on the rectifier diode and bridge circuit theory