https://escholarship.org/content/qt5c28p0zv/qt5c28p0zv.pdf?t=qnh8fa

Comparison of blockchain platforms: a systematic review and healthcare examples.

In this paper, a two-level soft slicing scheme is proposed for 5G-and-beyond radio access networks (RAN) to support ultra-reliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network-level, we first determine the amount of radio resources required for eMBB services, and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource pre-allocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the pre-allocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft slicing scheme meets stringent quality-of-service (QoS) requirements for both URLLC and eMBB services, and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.

Two-level Soft RAN Slicing for Customized Services in 5G-and-beyond Wireless Communications

As an emerging technology, blockchain (BC) has been playing a promising role in today's software-defined networking (SDN)-enabled Internet of Things (IoT) applications. Because of the salient feature of the network function virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud-driven ecosystem. When cloud-enabled systems encounter immense security and operational challenges caused mainly by third-party dependency, large-scale data communication, and maintenance, BC offers effective and robust data transfer solutions without incorporating intermediaries over the distributed network. With the increased SDN-BC convergence in the IoT domain, the underlying challenges and perspectives deserve proper attention methodically and structurally. From the motivation of addressing such issues, this study provides necessary insights to combine those for successful plug-and-play. Therefore, the study includes purposefully investigating current state-of-the-art to extract the research trends, future directions, and perspectives in this domain. This study provides a comprehensive survey of IoT, SDN, NFV, and BC-enabled emerging technologies. More importantly, the authors intelligently integrated the four different technologies—IoT, SDN, BC, and NFV based on characteristics, scopes, challenges, taxonomies, and tables in numerous areas. Initially, the authors introduce the SDN-IoT ecosystem in brief and address the features and applications. We took a close look at the SDN's overall taxonomy based on security, environment, scopes, and challenges. We also briefly describe the integration of SDN-IoT with the NFV ecosystems. Moreover, we review the prospect of BC technology from security perspectives, its extent, challenges of practical implementation, and the possible integration of IoT regarding smart applications. Finally, this study highlights several future directions based on these technologies. 

Impacts of blockchain in software‐defined Internet of Things ecosystem with Network Function Virtualization for smart applications: Present perspectives and future directions

With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified information model for production line devices and improve the connectivity of heterogeneous networks. However, in industrial wireless network application scenarios, there are various types of sensing information and large amounts of data to be transmitted. Each type of data has different requirements for real-time. The traditional OPC UA communication method is difficult to achieve real-time and reliable transmission in intelligent production lines and can’t meet the transmission requirements of time-sensitive data. In this paper, we propose a real-time communication model of the OPC UA wireless network for intelligent production lines (UAMPDS). IEEE 802.15.4e TSCH is used as the wireless communication infrastructure, and OPC UA protocols are fused to this model. Meanwhile, we design a load-aware time-slot scheduling algorithm to dynamically allocate and schedule time slots according to the network topology and traffic load of each node. We also implement a dynamic preemptive resource scheduling strategy based on service differentiation to ensure real-time transmission of time-sensitive data. The experimental results show that this model achieves differentiated services based on data with different latency tolerance and effectively reduces the transmission latency of real-time service data under the constrained network resources. It can avoid the starvation phenomenon of low priority queues, and improves the overall quality of service of intelligent production line communication networks.

/pdf/real-time-communication-model-based-on-opc-ua-wireless-36gaueh3y7.pdf

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes. 

Two-Level Soft RAN Slicing for Customized Services in 5G-and-Beyond Wireless Communications

5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security challenges and fulfil the requirement of ultra-reliable and low-latency communications (URLLC) has not been fully resolved. In this article, we present an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment. First, we elaborate the benefits and shortcomings of NFV for industry, by executing an industry application experiment in virtualized and nonvirtualized data center networks. Then, we illustrate an NFV-enabled 5G paradigm for URLLC in detail, with a special focus on the service chain acceleration and spectrum sharing built on NFV, blockchain, software-defined networking, and mobile edge computing. Finally, we establish a mathematical model to study the worst-cast transmission latency of NFV-enabled 5G with the input of the bursty traffic. The proposed model can be exploited to support the plan, management, and optimization of NFV-enabled 5G URLLC systems for industry.

/pdf/nfv-and-blockchain-enabled-5g-for-ultra-reliable-and-low-3dk97ipl9e.pdf

NFV and Blockchain Enabled 5G for Ultra-Reliable and Low-Latency Communications in Industry: Architecture and Performance Evaluation

A blockchain is a distributed database or ledger that maintains an ever-growing list of data records in opposition to tampering and revision. It provides immutable data storage over a distributed network and supports a large number of encrypted and coded interactions, which improves the reliability of the entire network interaction system and reduces the need for trust. Even if some nodes in the blockchain are hacked and fail, the system can run as usual. (In such a scenario, users are enabled to form a distributed peer -to peer (P2P) network in which they could interact with each other in an efficient manner without a trusted intermediary). In addition to being famous for decentralization, blockchain has shown other significant characteristics during its development, such as reliability, anonymity, transparency, auditability and programming. According to the different degree of openness and coverage, the current blockchain can be classified into three categories: public blockchain, consortium blockchain and private blockchain. In this process, the evolution of blockchain has gone through three processes: blockchain 1.0, 2.0 and 3.0. Blockchain 1.0, known as digital currency stage, is strongly related with the decentralization and payment of cryptocurrencies. Blockchain 2.0, known as digital finance stage, introduces economic, financial and market applications by programming far from simple currency transactions. Among them, the most significant features of blockchain 2.0 are the introduction and application of smart contracts. Blockchain 3.0, known as digital society stage, provides decentralized solutions for a variety of industries beyond just financial scene.

Introduction to blockchains

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the required resources in an intelligent manner, incurring coarse-grain prediction accuracies. To tackle this problem, in this paper, we propose an Accurate Prediction of Required virtual Resources (APRR) approach via Deep Reinforcement Learning (DRL). We first confirm the resource requests have more similar features and identify the high-dimensional required resources in computing, storage and bandwidth can be effectively consolidated into a single standardized value. Built upon these observations, we then model the required resources as a time-variant network matrix, which includes a number of elements, obtained from the network measurements, and some missing elements needed to be inferred. To obtain accurately predicted results, DRL-based matrix factorization with a set of available rules has been introduced into APRR and alternately executed in agent to minimize the prediction errors. Moreover, the error-prioritized designed for model training with quicker convergence. Simulation experiments on real-world datasets illustrate that APRR can accurately predict the required virtual resources compared with the related approaches.

Jialin Tian

Papers

NFV and Blockchain Enabled 5G for Ultra-Reliable and Low-Latency Communications in Industry: Architecture and Performance Evaluation

Introduction to blockchains

Accurate Prediction of Required Virtual Resources via Deep Reinforcement Learning