Showing papers on "Systems architecture published in 2019"

6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

Abstract: A key enabler for the intelligent information society of 2030, 6G networks are expected to provide performance superior to 5G and satisfy emerging services and applications. In this article, we present our vision of what 6G will be and describe usage scenarios and requirements for multi-terabyte per second (Tb/s) and intelligent 6G networks. We present a large-dimensional and autonomous network architecture that integrates space, air, ground, and underwater networks to provide ubiquitous and unlimited wireless connectivity. We also discuss artificial intelligence (AI) and machine learning [1], [2] for autonomous networks and innovative air-interface design. Finally, we identify several promising technologies for the 6G ecosystem, including terahertz (THz) communications, very-large-scale antenna arrays [i.e., supermassive (SM) multiple-input, multiple-output (MIMO)], large intelligent surfaces (LISs) and holographic beamforming (HBF), orbital angular momentum (OAM) multiplexing, laser and visible-light communications (VLC), blockchain-based spectrum sharing, quantum communications and computing, molecular communications, and the Internet of Nano-Things.

The Marabou Framework for Verification and Analysis of Deep Neural Networks

Abstract: Deep neural networks are revolutionizing the way complex systems are designed. Consequently, there is a pressing need for tools and techniques for network analysis and certification. To help in addressing that need, we present Marabou, a framework for verifying deep neural networks. Marabou is an SMT-based tool that can answer queries about a network’s properties by transforming these queries into constraint satisfaction problems. It can accommodate networks with different activation functions and topologies, and it performs high-level reasoning on the network that can curtail the search space and improve performance. It also supports parallel execution to further enhance scalability. Marabou accepts multiple input formats, including protocol buffer files generated by the popular TensorFlow framework for neural networks. We describe the system architecture and main components, evaluate the technique and discuss ongoing work.

A Comprehensive Survey of RAN Architectures Toward 5G Mobile Communication System

Abstract: The fifth generation (5G) of mobile communication system aims to deliver a ubiquitous mobile service with enhanced quality of service (QoS). It is also expected to enable new use-cases for various vertical industrial applications-such as automobiles, public transportation, medical care, energy, public safety, agriculture, entertainment, manufacturing, and so on. Rapid increases are predicted to occur in user density, traffic volume, and data rate. This calls for novel solutions to the requirements of both mobile users and vertical industries in the next decade. Among various available options, one that appears attractive is to redesign the network architecture-more specifically, to reconstruct the radio access network (RAN). In this paper, we present an inclusive and comprehensive survey on various RAN architectures toward 5G, namely cloud-RAN, heterogeneous cloud-RAN, virtualized cloud-RAN, and fog-RAN. We compare them from various perspectives, such as energy consumption, operations expenditure, resource allocation, spectrum efficiency, system architecture, and network performance. Moreover, we review the key enabling technologies for 5G systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; and some crucial radio access technologies (RATs), such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in 5G RAN and 5G RATs and identify several possible directions of future research.

TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning

Abstract: Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters. This paper addresses the memory capacity and bandwidth challenges of embedding layers and the associated tensor operations. We present our vertically integrated hardware/software co-design, which includes a custom DIMM module enhanced with near-memory processing cores tailored for DL tensor operations. These custom DIMMs are populated inside a GPU-centric system interconnect as a remote memory pool, allowing GPUs to utilize for scalable memory bandwidth and capacity expansion. A prototype implementation of our proposal on real DL systems shows an average 6.2-17.6× performance improvement on state-of-the-art DNN-based recommender systems.

A Survey on Human Behavior Recognition Using Channel State Information

Abstract: Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including pattern-based, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions.

Adaptive 5G Low-Latency Communication for Tactile InternEt Services

Abstract: The tactile internet will enable a new range of capabilities to enable immersive remote operations and interactions with a physical world. Tactile internet use cases span over many fields, from remote operation of industrial applications in, e.g., hazardous environments, via remote-controlled driving in a fully automated intelligent transport system, to remote surgery where the unique expert skills can be delivered to different locations in the world. Fifth-generation (5G) communication will play a fundamental part in this tactile internet vision, as it will provide necessary capabilities for the demanding communication needs in terms of reliability and low latency, for operators or teleoperated systems that are connected wirelessly. This paper provides an overview of tactile internet services and haptic interactions and communication. The 5G functionality for ultrareliable and low-latency services is described in depth and it is shown how 5G new radio (NR) and the evolved long-term evolution (LTE) radio interface can achieve guaranteed low-latency wireless transmission. The costs for providing reliable and low-latency wireless transmission in terms of reduced spectral efficiency and coverage are discussed. The 5G system architecture with a software-based network design based on a distributed cloud platform is presented. It is shown how the 5G network is configured for tactile internet services via multidomain orchestration.

Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit

Abstract: This chapter focuses on delivering a tutorial on iFogSim. iFogSim simulation toolkit is developed upon the fundamental framework of CloudSim. CloudSim is one of the wildly adopted simulators to model cloud computing environments. The chapter briefly discusses the iFogSim simulator and its three basic components: physical components, logical components, and management components. It explores high‐level steps to model and simulate fog computing environment. The chapter revisits the way of installing iFogSim and provides a guideline to model fog environment. Some fog scenarios and their corresponding user extensions are included in the chapter. Finally, the chapter concludes with the simulation of a simple application placement policy and a case study in smart healthcare. The case study includes a schematic illustration of the system architecture and application model for the Internet‐of‐Things (IoT)‐enabled healthcare solutions.

Design and Implementation of Open LoRa for IoT

Abstract: Long Range (LoRa) network is emerging as one of the most promising low-power wide-area (LPWA) networks, since it enables the energy-constraint devices distributed over wide areas to establish affordable connectivity. However, how to implement a cost-effective and flexible LoRa network is still an open challenge. This paper aims at exposing a feasible solution of design and implementation, allowing users to conveniently build a private LoRa network for various IoT applications. First, several typical application scenarios of LoRa network are discussed. Then, the LoRa system architecture is presented with the functionality of each component. We address the hardware design and implementation of LoRa Gateway, which is the bridge between LoRa nodes and LoRa network server. Especially, this paper contributes by proposing an improved software architecture of LoRa network server whose source codes are open on GitHub. Under the architecture, LoRa network server is divided into four decoupled modules and uses the messaging system based on streaming data for the interaction between modules to guarantee scalability and flexibility. Finally, the extensive experiments are conducted to evaluate the performance of LoRa networks in typical environments.

Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks

Abstract: Remote healthcare monitoring has exponentially grown over the past decade together with the increasing penetration of Internet of Things (IoT) platforms. IoT-based health systems help to improve the quality of healthcare services through real-time data acquisition and processing. However, traditional IoT architectures have some limitations. For instance, they cannot properly function in areas with poor or unstable Internet. Low power wide area network (LPWAN) technologies, including long-range communication protocols such as LoRa, are a potential candidate to overcome the lacking network infrastructure. Nevertheless, LPWANs have limited transmission bandwidth not suitable for high data rate applications such as fall detection systems or electrocardiography monitoring. Therefore, data processing and compression are required at the edge of the network. We propose a system architecture with integrated artificial intelligence that combines Edge and Fog computing, LPWAN technology, IoT and deep learning algorithms to perform health monitoring tasks. In particular, we demonstrate the feasibility and effectiveness of this architecture via a use case of fall detection using recurrent neural networks. We have implemented a fall detection system from the sensor node and Edge gateway to cloud services and end-user applications. The system uses inertial data as input and achieves an average precision of over 90% and an average recall over 95% in fall detection.

WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation

Abstract: Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation–Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Cyber-physical production systems architecture based on multi-agent’s design pattern—comparison of selected approaches mapping four agent patterns

Abstract: The growing complexity of production systems requires appropriate control architectures that allow flexible adaptation during their runtime. Although cyber-physical production systems (CPPS) provide the means to cope with complexity and flexibility, the migration with existing control systems is still a challenge. The term CPPS denotes a mechatronic system (physical world) coupled with software entities and digital information (cyber part), both enabling the smart factory concept for the Industry 4.0 (I4.0) paradigm. In this regard, design patterns could help developers to build their software with common solutions for manufacturing control derived from experiences. We provide a description and comparison of the already existing multi-agent systems (MAS) design patterns, which were collected and classified by introducing two classification criteria to support MAS developers. The applicability of these criteria is shown in the case of specific example architectures from the lower and higher control levels. The authors, together with experts from the German Agent Systems committee FA 5.15, gathered more than twenty MAS patterns, evaluated, and compared four selected patterns with the presented criteria and terminology. The main contribution is a CPPS architecture that fulfills requirements related to the era of smart factories, as well as the Reference Architectural Model I4.0 (RAMI 4.0). The conclusions indicate that agent-based patterns greatly benefit the CPPS design. In addition, it is shown that manufacturing based on MAS is a good way to address complex requests of the CPPS development.

Hardware-Constrained Millimeter-Wave Systems for 5G: Challenges, Opportunities, and Solutions

Abstract: Although mmWave systems promise to offer larger bandwidth and unprecedented peak data rates, their practical implementation faces several hardware challenges compared to sub-6 GHz communication systems. These hardware constraints can seriously undermine the performance and deployment progress of mmWave systems and thus necessitate disruptive solutions in the cross-design of analog and digital modules. In this article, we discuss the importance of different hardware constraints and propose a novel system architecture that is able to release these hardware constraints while achieving better performance for future mmWave communication systems. The characteristics of the proposed architecture are articulated in detail, and a representative example is provided to demonstrate its validity and efficacy.

Private 5G Networks for Vertical Industries: Deployment and Operation Models

Abstract: Fifth generation of mobile communication network (5G) technology enables, for the first time, realization of stand-alone, standard-based private wireless networks to support a broad range of demanding applications across a variety of vertical industries. This is achieved, among other features, due to the flexibility, modularity and programmability of the 5G system architecture. The flexibility enables a large number of deployment and operation models, which on the other hand could make it a challenge to find the model fitting best for a particular vertical industry. Therefore, this paper, looks into realization of private 5G networks in the vertical domains, with a focus on the smart factory, and provide a systematic classification of the potential deployment architecture and operation models. In addition to that, a comprehensive set of evaluation metrics are identified and a comparative analysis of the identified deployment and operation models are presented.

System architectures for Industrie 4.0 applications

Abstract: Industrie 4.0 principles demand increasing flexibility and modularity for automated production systems. Current system architectures provide an isolated view of specific applications and use cases, but lack a global, more generic approach. Based on the specific architectures of two EU projects and one German Industrie 4.0 project, a generic system architecture is proposed. This system architecture features the strengths of the three isolated proposals, such as cross-enterprise data sharing, service orchestration, and real-time capabilities, and can be applied to a wide field of applications. Future research should be directed towards considering the applicability of the architecture to other equal applications.

UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Abstract: Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

A Low-Cost High-Speed Neuromorphic Hardware Based on Spiking Neural Network

Abstract: Neuromorphic is a relatively new interdisciplinary research topic, which employs various fields of science and technology, such as electronic, computer, and biology. Neuromorphic systems consist of software/hardware systems, which are utilized to implement the neural networks based on human brain functionalities. The goal of neuromorphic systems is to mimic the biologically inspired concepts of the nervous systems, envisioned to provide advantages, such as lower power consumption, fault tolerance, and massive parallelism for the next generation of computers. This brief presents a neural computing hardware unit and a neuromorphic system architecture based on a modified leaky integrate and fire neuron model in a spiking neural network for a pattern recognition task in register-transfer level. The neuron model and the spiking network are explored, considering digital implementation, targeting low-cost high-speed large-scale systems. Results of the hardware synthesis and implementation on field-programmable gate array are presented as a proof of concept. Accordingly, the maximum frequency of the implemented neuron model and spiking network are 412.371 MHz and 189.071 MHz, respectively.

Hybrid Intrusion Detection System for Edge-Based IIoT Relying on Machine-Learning-Aided Detection

Abstract: The design of an intrusion detection system (IDS) plays a critical role in guaranteeing the security of the Industrial Internet of Things (IIoT). Recently, the rapid development of edge-based IIoT has posed new challenges in the design of a beneficial IDS considering its billions of IIoT devices and substantially decentralized data interaction. Both the detection method and the system architecture become the key components in constructing an efficient IDS for edge-based IIoT. In this article, we survey the typical state-of-theart studies about detection methods and system structure of IDS. Moreover, we propose a hybrid IDS architecture and introduce a machine learning aided detection method, which outperforms the literature in terms of a range of benchmarks.

Software Defined 5G and 6G Networks: a Survey

Abstract: The current mobile communications could not satisfy the explosive data requirement of users. This paper reviews the frontier technology of software definition networks (SDN) of 5G and 6G, including system architecture, resource management, mobility management, interference management, challenges, and open issues. First of all, the system architectures of 5G and 6G mobile networks are introduced based on SDN technologies. Then typical SDN-5G/6G application scenarios and key issues are discussed. We also focus on mobility management approaches in mobile networks. Besides, three types of mobility management mechanism in software defined 5G/6G are described and compared. We then summarize the current interference management techniques in wireless cellular networks. Next, we provide a brief survey of interference management method in SDN-5G/6G. Additionally, considering the challenges, we discuss mm-Wave spectrum, un-availability of popular channel model, massive MIMO, low latency and QoE, energy efficiency, scalability, mobility and routing, inter operability, standardization and security for software defined 5G/6G networks.

MnnFast: a fast and scalable system architecture for memory-augmented neural networks

Abstract: Memory-augmented neural networks are getting more attention from many researchers as they can make an inference with the previous history stored in memory. Especially, among these memory-augmented neural networks, memory networks are known for their huge reasoning power and capability to learn from a large number of inputs rather than other networks. As the size of input datasets rapidly grows, the necessity of large-scale memory networks continuously arises. Such large-scale memory networks provide excellent reasoning power; however, the current computer infrastructure cannot achieve scalable performance due to its limited system architecture. In this paper, we propose MnnFast, a novel system architecture for large-scale memory networks to achieve fast and scalable reasoning performance. We identify the performance problems of the current architecture by conducting extensive performance bottleneck analysis. Our in-depth analysis indicates that the current architecture suffers from three major performance problems: high memory bandwidth consumption, heavy computation, and cache contention. To overcome these performance problems, we propose three novel optimizations. First, to reduce the memory bandwidth consumption, we propose a new column-based algorithm with streaming which minimizes the size of data spills and hides most of the off-chip memory accessing overhead. Second, to decrease the high computational overhead, we propose a zero-skipping optimization to bypass a large amount of output computation. Lastly, to eliminate the cache contention, we propose an embedding cache dedicated to efficiently cache the embedding matrix. Our evaluations show that MnnFast is significantly effective in various types of hardware: CPU, GPU, and FPGA. MnnFast improves the overall throughput by up to 5.38×, 4.34×, and 2.01× on CPU, GPU, and FPGA respectively. Also, compared to CPU-based MnnFast, our FPGA-based MnnFast achieves 6.54× higher energy efficiency.

From Layout to System: Early Stage Power Delivery and Architecture Co-Exploration

Abstract: With the reduced noise margin brought by relentless technology scaling, power integrity assurance has become more challenging than ever. On the other hand, traditional design methodologies typically focus on a single design layer without much cross-layer interaction, potentially introducing unnecessary guard-band and wasting significant design resources. Both issues imperatively call for a cross-layer framework for the co-exploration of power delivery (PD) and system architecture, especially in the early design stage with larger design and optimization freedom. Unfortunately, such a framework does not exist yet in the literature. As a step forward, this paper provides a run-time simulation framework of both PD and architecture and captures their interactions. Enabled by the proposed recursive run-time PD model, it can achieve smaller than 1% deviation from SPICE for an entire PD system simulation. Moreover, with seamless interactions among architecture, power and PD simulators, it can simulate actual benchmarks within reasonable time. The experimental results of running PARSEC suite have demonstrated the framework’s capability to discover the co-effect of PD and architecture for early stage design optimization. Moreover, it also shows multiple over-pessimism in traditional PD methodologies. Finally, the framework is able to investigate the impact of dynamic noise on system level oxide breakdown reliability and shows 31%–92% lifetime estimation deviations from typical static analysis.

PECS: Towards personalized edge caching for future service-centric networks

Abstract: Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience (QoE). Existing content distribution networks (CDN) and mobile content distribution networks (mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System (PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.

E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

Abstract: The future of e-mobility will consist of a large number of connected electric vehicles, smart charging stations and information systems at the intersection of electricity and mobility sector. When engineering and integrating the multitude of systems into even more complex systems-of-systems for e-mobility, interoperability and complexity handling are vital. Model-based system architectures support the engineering process of information systems with the concepts of abstraction, reduction and separation of concerns. In this paper, we contribute to the research body, by extracting requirements for managing complexity and interoperability of these systems. Further, a comparative analysis of the state-of-the-art in existing architecture models and frameworks for e-mobility is conducted. Based on the identified gaps in existing research, we propose the E-Mobility Systems Architecture (EMSA) Model, a three-dimensional systems architecture model for the e-mobility sector. Its structure originates from the well-established Smart Grid Architecture Model. We further allocate all relevant entities from the e-mobility sector to the EMSA dimensions, including a harmonized role model, functional reference architecture, component and systems allocation, as well as a mapping of data standards and communication protocols. The model then is validated qualitatively and quantitatively against the requirements with a case study approach. Our evaluation shows that the EMSA Model fulfills all requirements regarding the management of complexity and ensuring interoperability. From the case study, we further identify gaps in current data model standardization for e-mobility.

Stateful Three-Input Logic with Memristive Switches

Abstract: Memristive switches are able to act as both storage and computing elements, which make them an excellent candidate for beyond-CMOS computing. In this paper, multi-input memristive switch logic is proposed, which enables the function X OR (Y NOR Z) to be performed in a single-step with three memristive switches. This ORNOR logic gate increases the capabilities of memristive switches, improving the overall system efficiency of a memristive switch-based computing architecture. Additionally, a computing system architecture and clocking scheme are proposed to further utilize memristive switching for computation. The system architecture is based on a design where multiple computational function blocks are interconnected and controlled by a master clock that synchronizes system data processing and transfer. The clocking steps to perform a full adder with the ORNOR gate are presented along with simulation results using a physics-based model. The full adder function block is integrated into the system architecture to realize a 64-bit full adder, which is also demonstrated through simulation.

Ultra-Reliable Communications for Industrial Internet of Things: Design Considerations and Channel Modeling

Abstract: Factory automation is the next industrial revolution. 5G and IIoT are enabling smart factories to seamlessly create a network of wirelessly connected machines and people that can instantaneously collect, analyze, and distribute real-time data. A 5G-enabled communication network for IIOT will boost overall efficiency, launching a new era of market opportunities and economic growth. This article presents the 5G-enabled system architecture and ultra-reliable use cases in smart factories associated with automated warehouses. In particular, for URLLC-based cases, key techniques and their corresponding solutions, including diversity for high reliability, short packets for low latency, and on-the-fly channel estimation and decoding for fast receiver processing, are discussed. Then the channel modeling requirements concerning technologies and systems are also identified in industrial scenarios. Ray tracing channel simulation can meet such requirements well, and based on that, the channel characteristic analysis is presented at 28 and 60 GHz for licensed and unlicensed band frequencies to exploit the available degrees of freedom in the channels.

An Open Internet of Things System Architecture Based on Software-Defined Device

Abstract: The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

Distributed Security Architecture Based on Blockchain for Connected Health: Architecture, Challenges, and Approaches

Abstract: The ubiquitous nature of a connected health system imposes challenges with regard to the design of system architecture, security, and privacy. Different from traditional centralized systems, a distributed blockchain-based security protection architecture is proposed. In particular, smart contracts, as an intelligent protocol in the blockchain technology, are exploited to automatically achieve system confidentiality, integrity, and authenticity. Research challenges related to security and privacy issues in our proposed architecture are then analyzed, followed by potential solutions. Finally, security performance simulations and analyses are conducted to validate and evaluate the effectiveness of the proposed security architecture.