Open Access

The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.

Time-Sensitive Networking (TSN) is becoming increasingly important. Especially in the field of industrial applications, the demand for uniform, converged real-time networks is continuously increasing. Furthermore, the request to integrate wireless, mobile, and real-time capable network elements is getting more and more relevant to industrial automation use cases. To address these requests, the 3rd Generation Partnership Project (3GPP) has extended their specifications for mobile telecommunication protocols by descriptions to integrate 5G mobile networks into TSN starting from Release 16 onwards. While the specifications provide a good theoretical overview, there is still a lack of real implementations or even proof of concepts. Therefore, we started an implementation of a 5G network that is ready to be integrated into existing TSN. This work gives an overview of the current work in progress, mainly focusing on the implementation of the TSN Application Function (TSN AF) and the time synchronization features within the TSN Translators (DS-TT and NW-TT). It also shows current limitations and difficulties and how we have overcome them with our setup.

Performance benchmarking is crucial for optimizing networks, including 5G Non-Public Networks (5G-NPN). Since one of the major advantages of 5G-NPN is to guarantee Quality of Service (QoS), ensuring optimal performance for their diverse applications is critical. This requires adjustments and testing of various radio-related parameters. Also, performance analysis and benchmarking need to be done based on the evaluation of relevant Key Parameter Indicators (KPIs) in order to identify the parameter set for the optimum performance for each application’s requirements. Many published results on performance benchmarking often lack transparency in their scoring methods. Additionally, QoS benchmarking evaluation for 5G-NPN use cases needs further steps due to the varying ranges of their KPIs. For example Block Error Rate (BLER) is mostly represented by percentage, while Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are negative values.3GPP in TR 103.559 outlines practices for benchmarking network QoS, with a focus on Speech and multimedia Transmission Quality (STQ). This paper extends these outlines to evaluate 5G-NPN performance by defining a multi-objective function. We select a specific 5G-NPN use case as an example and apply four tests with varying network configurations. After each test, we collect most relevant 5G KPIs. To facilitate comparison, all KPIs are rescaled to a common scale. Additionally, we assign weights to each KPI based on its significance in the chosen use case. By combining rescaling and weight assignments, we propose a single metric that effectively characterizes the overall network performance for 5G-NPNs based on their specific use case requirements.

In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.