Showing papers on "Systems architecture published in 2021"

Optimized dynamic storage of data (ODSD) in IoT based on blockchain for wireless sensor networks

Abstract: The Industry 4.0 IoT network integration with blockchain architecture is a decentralized, distributed ledger mechanism used to record multi-user transactions. Blockchain requires a data storage system designed to be secure, reliable, and fully transparent, emerged as a preferred IoT-based digital storage on WSN. Blockchain technology is being used in the paper to construct the node recognition system according to the storage of data for WSNs. The data storage process on such data must be secure and traceable in different forensics and decision making. The primary theme of the dynamic data security is therefore for rejecting exploitation of the unauthorized user and for evaluating the mechanism in tracing and evidence of system’s data operation in a dynamic manner, growth and quality features under the stochastic state of the model; (1) a mathematical method for the secured storage of data in dynamic is built through distributed node cooperation in IoT industry. (2) the ownership transition feature and the dynamic storage of data system architecture are configured, (3) the emerging distributed storage architecture for blockchain-based WSN will substantially reduce overhead storage for each node without affecting data integrity; (4) minimize the latency of data reconstruction in distributed over storage system, and propose an effective and scalable algorithm for optimizing storage latency issue. In addition to this research, the system implements verified possession of data for replacing the evidence in original digital currency for mining and to store new data blocks that will be compared to the proof system, dramatically reduces computational capacity. The proposed ODSD framework has exceptional benefits for real-time applications while maintaining the security of the dynamic storage of data.

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

Abstract: The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from “connected things” to “connected intelligence”. However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

Multi-microgrid Energy Management Systems: Architecture, Communication, and Scheduling Strategies

Abstract: The increasing penetration of various distributed and renewable energy resources at the consumption premises, along with the advanced metering, control and communication technologies, promotes a transition on the structure of traditional distribution systems towards cyber-physical multi-microgrids (MMGs). The networked MMG system is an interconnected cluster of distributed generators, energy storage as well as controllable loads in a distribution system. And its operation complexity can be decomposed to decrease the burdens of communication and control with a decentralized framework. Consequently, the multi-microgrid energy management system (MMGEMS) plays a significant role in improving energy efficiency, power quality and reliability of distribution systems, especially in enhancing system resiliency during contingencies. A comprehensive overview on typical functionalities and architectures of MMGEMS is illustrated. Then, the emerging communication technologies for information monitoring and interaction among MMG clusters are surveyed. Furthermore, various energy scheduling and control strategies of MMGs for interactive energy trading, multi-energy management, and resilient operations are thoroughly analyzed and investigated. Lastly, some challenges with great importance in the future research are presented.

UAV-Assisted Vehicular Edge Computing for the 6G Internet of Vehicles: Architecture, Intelligence, and Challenges

Abstract: With the growing intelligence needed on the Internet of Vehicles (IoV), seamless edge computing services for the sixth generation (6G) vehicle-to-everything (V2X) applications require three-dimensional (3D) and ubiquitous networking coverage to realize the intensive computing tasks and data offloading. In the high mobility and fast-changing vehicular environment, the 6G V2X networks supporting vehicular edge computing (VEC) need to be more flexible, smart, and adaptive. In this article, an intelligent unmanned aerial vehicle (UAV)-assisted VEC system is envisioned to satisfy 6G V2X requirements and provide 3D and adaptive service coverage. We indicate that in 6G IoV networks, given the fast-changing and large-scale networks, effectively coordinating and managing massive UAVs incur several problems, which are complex to solve by conventional optimization tools. In this regard, leveraging the big data feature of historical information, artifi-cial-intelligence-based solutions are anticipated to facilitate fast, automatic, and efficient UAV deployment to support 6G V2X applications. An illustrative case study is provided to demonstrate the effectiveness of the proposed intelligent UAV-assisted VEC architecture. We also outline future research directions to realize the vision of UAV-assisted VEC for 6G IoV networks.

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Abstract: With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

Computing over Space-Air-Ground Integrated Networks: Challenges and Opportunities

Abstract: Space-air-ground integrated networks (SAGINs) have gained significant attention and become a promising architecture for ubiquitous connectivity for SG-Advanced and 6G, enabling the integration of satellite networks, aerial networks, and terrestrial networks. This integration brings tremendous communication benefits, such as non-terrestrial networks, seamless global coverage, high flexibility, and augmented system capacity. Meanwhile, computing capability becomes an indispensable part of the SAGIN ecosystem. In SAGINs, limited and unbalanced computation and communication resources of different network segments make it challenging to provide strict quality-of-service (QoS) guarantees for specific traffic (e.g., delay-sensitive traffic and outage-sensitive traffic). To fully utilize available system resources in SAGINs, cooperative computing among different network segments is a promising technology. This article presents the fundamentals and applications of computing over SAGINs by introducing the system architecture and explaining the potential technical issues related to cooperative computing. Furthermore, the potential for cooperative computing over SAGINs is showcased with a preliminary performance evaluation. Finally, future research opportunities are discussed.

Distributed Attribute-Based Access Control System Using a Permissioned Blockchain

Abstract: Auditing provides essential security control in computer systems by keeping track of all access attempts, including both legitimate and illegal access attempts. This phase can be useful in the context of audits, where eventual misbehaving parties can be held accountable. Blockchain technology can provide the trusted auditability required for access control systems. In this paper, we propose a distributed Attribute-Based Access Control (ABAC) system based on blockchain to provide trusted auditing of access attempts. Besides auditability, our system presents a level of transparency that both access requesters and resource owners can benefit from it. We present a system architecture with an implementation based on Hyperledger Fabric, achieving high efficiency and low computational overhead. The proposed solution is validated through a use case of independent digital libraries. Detailed performance analysis of our implementation is presented, taking into account different consensus mechanisms and databases. The experimental evaluation shows that our presented system can effectively handle a transaction throughput of 270 transactions per second, with an average latency of 0.54 seconds per transaction.

Collaborative Edge Computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System

Abstract: Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.

Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data

Abstract: Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”.

Aerial Swarms: Recent Applications and Challenges

Abstract: Currently, there is a large body of research on multi-agent systems addressing their different system theoretic aspects. Aerial swarms as one type of multi-agent robotic systems have recently gained huge interest due to their potential applications. However, aerial robot groups are complex multi-disciplinary systems and usually research works focus on specific system aspects for particular applications. The purpose of this review is to provide an overview of the main motivating applications that drive the majority of research works in this field, and summarize fundamental and common algorithmic components required for their development. Most system demonstrations of current aerial swarms are based on simulations, some have shown experiments using few 10 s of robots in controlled indoor environment, and limited number of works have reported outdoor experiments with small number of autonomous aerial vehicles. This indicates scalability issues of current swarm systems in real world environments. This is mainly due to the limited confidence on the individual robot’s localization, swarm-level relative localization, and the rate of exchanged information between the robots that is required for planning safe coordinated motions. This paper summarizes the main motivating aerial swarm applications and the associated research works. In addition, the main research findings of the core elements of any aerial swarm system, state estimation and mission planning, are also presented. Finally, this paper presents a proposed abstraction of an aerial swarm system architecture that can help developers understand the main required modules of such systems.

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence.

Abstract: In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between IoT and AI. By broadening the scope of the classic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities, human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two, the main theme of the article, represent the paradigm shift in communication and computing. H2M SemCom refers to semantic techniques for conveying meanings understandable by both humans and machines so that they can interact. M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network. The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches. The second part focuses on specific techniques for application areas of H2M (human and AI symbiosis, recommendation, etc.) and M2M SemCom (distributed learning, split inference, etc.) Finally, we discuss the knowledge graphs approach for designing SemCom systems. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in 6G featuring connected intelligence and integrated sensing, computing, communication, and control.

ANT-Centric IoT Security Reference Architecture – Security-by-Design for Satellite-Enabled Smart Cities

Abstract: Internet of Vehicles (IoV), a special form of Internet of Things (IoT), is an important enabler of intelligent transportation system which is one of the most strategic applications in smart city initiatives. In order to achieve its intended functionalities, IoV requires anytime anywhere connectivity which cannot be satisfied by traditional networking technologies. Space-Air-Ground Integrated Network (SAGIN) is widely believed to be an ideal infrastructure for connecting IoV. In this paper, we present an approach for understanding the security issues of complex IoT systems and propose a security reference architecture for assessing security risks and addressing the security requirements. Specifically, we propose an Activity-Network-Things (ANT)-centric security reference architecture which is based on the three architectural perspectives in studying IoT systems namely device, internet and semantic. We discuss the limitations of existing IoT system architecture models, which are mainly evolved from enterprise system architecture with some adaptation to the inherent features of IoT systems. Our approach can help manage the security risks by focusing on the critical activities performed in different micro-perimeters within an IoT system. The proposed architecture includes an organized process to understand the security requirements and select specific parameters for tailored security controls that are commensurate with organization-specific and application-specific security impacts of IoT. Our architecture is flexible enough to cater for any IoT application, and hence can be easily applied to the case of SAGIN-enabled IoV.

Integrating Edge Computing into Low Earth Orbit Satellite Networks: Architecture and Prototype

Abstract: Low Earth Orbit (LEO) satellite network is a cost-efficient way to achieve global covering for wide-area Internet of Things (IoT). As more and more IoT applications require large amounts of computing resources, cloud computing paradigm becomes one of the IoT’s main enablers. Abundant resources can be used to execute computation-intensive IoT applications in the cloud. Moreover, edge computing has emerged to alleviate the high latency and low bandwidth problem of cloud computing. The integration of edge computing into LEO networks (which is called LEC in this paper) can improve satellite IoT network’s performance. In addition, it is an effective way to support delay-sensitive and resource-hungry wide-area IoT applications. However, there are many technical challenges for LEC, which is different from edge computing in terrestrial networks. Therefore, we study LEC in depth and a novel system architecture is proposed. A LEC prototype system is implemented which verifies our design. The simulation result demonstrates that LEC can improve system performance compared with cloud computing in LEO networks.

5G RAN Slicing: Dynamic Single Tenant Radio Resource Orchestration for eMBB Traffic within a Multi-Slice Scenario

Abstract: Emerging 5G systems will need to seamlessly guarantee novel types of services in a multi-do-main ecosystem. New methodologies of network and infrastructure sharing facilitate the cooperation among the operators, exploiting the core and access sections of the system architecture. Network slicing (NS) is the operators' best technique for building and managing a network. Without NS, the 5G requirements in terms of flexibility, optimal resource utilization, and investment returns cannot materialize. Before slicing is commercially available, different sections of the 5G architecture should be modified to include NS. In this work, we present a novel dynamic radio access network slicing resource sharing method aimed to guarantee optimal service level agreements through the monitoring of each slice tenant's key performance indicators. The experiments are conducted following the 3GPP specifications, and the solution is validated using a testbed based on the main 5G functionalities.

A Generic Plug & Produce System Composed of Semantic OPC UA Skills

Abstract: Typical industrial workcells are composed of a plenitude of devices from various manufacturers, which rely on their own specific control interfaces. To reduce setup and reconfiguration times, a hardware-agnostic Plug & Produce system is required. In this paper, we present a system architecture that uses generic and semantically augmented OPC UA skills for robots, tools, and other system components. Standardized skill interfaces and parameters facilitate flexible component interchange and automatic parametrization with a focus on reusability of skills across different platforms and domains. The hierarchical composition of such skills allows for additional abstraction through the grouping of functionalities. Through the extension of OPC UA discovery services, available skills are dynamically detected whenever a manufacturing system's component is updated. The introduced Plug & Produce system is evaluated in multiple industrial workcells composed of robots, tool changer, electric parallel gripper, and vacuum gripper—all controlled via the proposed OPC UA skill interface. The evaluation of our system architecture demonstrates the applicability of the Plug & Produce concept in the domain of robot-based industrial assembly. Although it is necessary to adapt existing hardware to comply with the semantic skill concept, the initial one-time effort yields reoccurring efficiency gains during system reconfiguration. In particular, small lot production benefits from reduced changeover times.

An Autonomous Grape-Harvester Robot: Integrated System Architecture

Abstract: This work pursues the potential of extending “Industry 4.0” practices to farming toward achieving “Agriculture 4.0”. Our interest is in fruit harvesting, motivated by the problem of addressing the shortage of seasonal labor. In particular, here we present an integrated system architecture of an Autonomous Robot for Grape harvesting (ARG). The overall system consists of three interdependent units: (1) an aerial unit, (2) a remote-control unit and (3) the ARG ground unit. Special attention is paid to the ARG; the latter is designed and built to carry out three viticultural operations, namely harvest, green harvest and defoliation. We present an overview of the multi-purpose overall system, the specific design of each unit of the system and the integration of all subsystems. In addition, the fully sensory-based sensing system architecture and the underlying vision system are analyzed. Due to its modular design, the proposed system can be extended to a variety of different crops and/or orchards.

Location-Based Analytics in 5G and Beyond

Abstract: Location-based analytics leverage accurate location awareness enabled by the fifth generation (5G) mobile technology standard, as well as the integration of heterogeneous technologies, to empower a plethora of new services for 5G verticals and optimize the use of network resources. This article proposes an end-to-end architecture integrated in the 5G network infrastructure to provide location-based analytics as a service. Based on this architecture, we present an overview of cutting-edge applications in 5G and beyond, focusing on people-centric and network-centric location-based analytics.

A Dynamic Context-Aware Workflow Management Scheme for Cyber-Physical Systems Based on Multi-Agent System Architecture

Abstract: Although Cyber-Physical Systems (CPS) provides a paradigm to accommodate frequent changes in manufacturing sector, modeling and managing operations of CPS are challenging issues due to the complex interactions between entities in the system. Development of an effective context-aware workflow management system to guide the entities in the system is a critical factor to attain the potential benefits of CPS. In this paper, we will address the issue on the design of context-aware workflow management systems for CPS in IoT-enabled manufacturing environment. A CPS consists two parts, the Physical World and the Cyber World. To achieve the goal to design a context-aware information system for CPS, the Cyber World models of the entities in the system are constructed based on discrete timed Petri nets (DTPN) and a multi-agent system architecture in which each entity in the system is modeled as an agent to capture the interactions of entities in CPS. To develop context-aware workflow management systems for CPS, a Configuration/Scheduling Feasibility Problem and a Context Generation Problem in CPS are formulated. A condition for configuration/scheduling feasibility based on transformation of the Cyber World Models is established to develop an algorithm to generate contextual information to guide the operation of CPS. The proposed method is illustrated by examples. A series of experiments have been conducted to demonstrate the practicality of the proposed method in terms of computation time and response time. The results indicate that the computation time and total response time increase polynomially with respect to problem size parameters and show that the proposed method is effective in solving real problems.

A Quantitative Survey of Communication Optimizations in Distributed Deep Learning

Abstract: Nowadays, large and complex deep learning (DL) models are increasingly trained in a distributed manner across multiple worker machines, in which extensive communications between workers pose serious scaling problems. In this article, we present a quantitative survey of communication optimization techniques for data parallel distributed DL. We first identify the major communication challenges and classify the existing solutions into three levels, namely the learning algorithm, the system architecture, and the network infrastructure. We present the state-of-the-art communication optimization techniques and conduct a comparative study of seven common lossless distributed DL methods on a 32-GPU cluster with 100Gb/s InfiniBand (IB). We show that the DL models with low model intensity (such as BERT and BERT-Large) are difficult to scale out even with the best available lossless algorithm over 100Gb/s IB; and the system architecture and scheduling algorithms have a critical impact on the scaling property. We conclude the article with discussions of open issues for further investigation.

Secure Communication for Multiquadrotor Networks Using Ethereum Blockchain

Abstract: Ethereum blockchain is a powerful, open-source technology for creating decentralized and secure information sharing systems. The main contribution of this article is the experimental validation of an Ethereum blockchain-based software and hardware architecture that enables secure communication for multiple small unmanned aerial vehicles (sUAVs). The experiments involved three DJI M100 quadrotors that shared images captured during flight based on smart contracts created using Ethereum’s Turing complete programming language. The smart contract was designed so that only the intended recipient sUAV could access a specific image. The effect of image size, difficulty level, and consensus algorithms on image transfer times during flight are noted and point to the feasibility of this system in practical missions. The effects of wireless network disruptions on the Ethereum network are documented. The fully documented smart contract code is open sourced to assist readers in quick prototyping. As efforts for decentralization and security of multirobot systems continue to grow, the system architecture and implementation detailed here may serve as a guide for future research.

A scheme for intelligent blockchain-based manufacturing industry supply chain management

Abstract: Supply chain management as an indispensable component in intelligent city construction has faced great opportunities with the fast development of AI, Internet of Things, big data and mobile communication. Blockchain, as a heated and promising area, features some key characteristics that make it an optimal solution to problems in traditional supply chain management and can also act as a great platform to these cutting-edge technologies. In this paper, a design scheme of integrated platform for information service provided by participants in supply chain and based on Ethereum blockchain is proposed. System architecture and smart contracts are designed to guarantee the security enhancement from blockchain can be fully exploited. Some common and vital problems in the blockchain-based scheme are also discussed and solved, among which a data-driven credit evaluation scheme workable on chain is put forward and a cross-chain architecture is designed to make the system more secure, intelligent and scalable.

An Argumentative Dialogue System for COVID-19 Vaccine Information

Abstract: Dialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.