High-level System Architecture Research Articles

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

Investigation of Secure Health Monitoring System Using IOT

The rapid progress of technology, particularly the Internet of Things (IoT), has introduced exciting opportunities for transforming the healthcare sector. One significant area where IoT has made a significant impact is in the creation of secure health monitoring systems. These systems utilize IoT devices and sensors to gather and transmit live health data, facilitating remote monitoring and individualized healthcare.The integration of IoT in healthcare monitoring offers numerous benefits, including improved patient outcomes, enhanced access to care, and increased efficiency in healthcare delivery.To develop you would typically follow a research methodology that involves several key steps. Clearly state the objectives of your research, such as designing and implementing a secure health monitoring system using IoT. Specify the aspects you want to focus on, such as data privacy, authentication, encryption, or device communication. Develop a high-level system architecture for your health monitoring system. Define the components, their functionalities, and how they interact with each other. Consider the security aspects, such as secure data transmission, authentication, access control, and data storage.By multiplying each of our goals by a weight provided by the user, we can scale our collection of goals into a single goal using the weighted sum approach. One of the most popular strategies is this one. Finding the appropriate weights to give each aim while using the weighted sum approach is a concern. Taken as alternative parameters for HMS1, HMS2, HMS3, HMS4, HMS5. Taken as evaluation parameters for Portability,Round-The-Clock Health Surveillance,ease of use,Reliability.HMS1 performance is good when compared to others so HMS 1 is preferred except HMS 1 performed better in secure health monitoring system using IIOD.

An overview of orthogonal frequency division multiplexing principles, architecture, and its application

In this work, Orthogonal Frequency Division Multiplexing’s (OFDM) principles, implementation design or high-level system architecture and application were reviewed. The condition for orthogonality, frequency spacing between consecutive subcarriers (called frequency spacing) was established analytically with practical instance highlighted. The review also covers Fast Fourier Transform (FFT) which serves as a bank of demodulators while Inverse Fast Fourier Transform (IFFT) serves as a bank of modulators in an OFDM system, advancement in OFDM, Quadrature modulation types (Binary Phase Shift Keying, 4-Quadrature Amplitude Modulation, 8-Quadrature Amplitude Modulation, 16-Quadrature Amplitude Modulation, and 64-Quadrature Amplitude Modulation QAM) and spectrum efficiency was also covered. Several technologies that use OFDM were mentioned but attention was given to its usage in Wireless LAN 802.11a.

A Model-Based Systems Architecting and Integration Approach Using Interlevel and Intralevel Dependency Matrix

The way system architects typically decompose a high-level system architecture has an outsized impact on interface complexity of systems and system-of-systems (SoS). In existing practice, systems are decomposed based on empirical knowledge of system architects and the prevailing architecture of organizations responsible for developing systems. The decomposition of systems (and SoS) involves aspects of engineering, management, and social sciences. How the decomposition approach breaks down a complex system determines the effectiveness of the integration strategy. In this article, we recommend that a complex system (and more specifically, a complex sociotechnical system) should be decomposed and aligned along five perspectives or layers: operation, function, physical, implementation, and organization. At each layer, the system architecture exhibits a certain level of complexity. Aligning modules at each layer with modules at the level above can help reduce the complexity of the overall system making it more manageable. This article presents a system architecting methodology that employs interlevel and intralevel dependency matrix (I2DM) to enable this approach. The I2DM methodology employs stochastic optimization in combination with heuristics to align modules in the different layers and then identifies problematic interactions that could pose problems in system integration. The article also describes how techniques from pattern recognition, data analytics, and clustering can be applied to system architecture models to formulate and assess the efficacy of a system integration strategy. The ontology underlying the systems architecting and integration strategy is also presented.

Dynamic Spectrum Management with Network Function Virtualization for UAV Communication

Rapid increases in unmanned aerial vehicles (UAVs) applications are attributed to severe spectrum collision issues, especially when UAVs operate in spectrum scarce environments, such as urban areas. Dynamic air-to-ground (A2G) link solutions can mitigate this issue by utilizing programmable communication hardware in the air and real-time assignment of spectrum resources to achieve high-throughput and low-latency connectivity between UAVs and operators. To mitigate the high-computation issue among ground control station (GCS) networks and provide a broad communication coverage for large number of UAVs, we propose an advanced UAV A2G communication solution integrated with the dynamic spectrum management (DSM) and network function virtualization (NFV) technology to serve urban operations. The edge-cutting UAV communication technologies are surveyed. The proposed scheme is discussed in terms of the high-level system architecture, virtual network architecture, specific virtual functions (SVFs), and affiliated operation support databases. Some major research challenges are highlighted and the possible directions of future research are identified.

The Impact of Virtualisation Techniques on Power System Control Networks

Virtualisation is a concept successfully applied to IT systems. In this work, we analyse how virtualisation approaches, such as edge computing, brokerage and software-defined networking, can be applied in the area of electricity grid management and control systems. Power system information and communications technology is currently subject to significant changes. Networked power grid components including renewable energy units, electric vehicles and heat pumps need to be integrated into grid management systems. We studied how virtualisation techniques can support system operators in increasing an energy and communication system’s dependability and situational awareness, and how manual (mostly field-level) configuration and engineering efforts can be reduced. Starting from a working hypothesis, three concrete use-cases were implemented and the performance enhancements were benchmarked to allow for well-informed answers to the questions above. We took a close look at application-protocol-independent redundancy, grid-based routing and online system integrity control. In these study cases, we found significant improvements could be achieved with virtualisation in terms of reduced engineering effort, better system management and simplification in high-level system architecture, since implementation details are hidden by the virtualisation approach.

A secure road traffic congestion detection and notification concept based on V2I communications

Applying vehicular (V2X) communications in detecting traffic congestion is a promising approach, as smart and self-driving vehicles are equipped with sensors that can be used to detect an incident anywhere in real-time. Unfortunately, without appropriate security measures and careful design the communication can be vulnerable to malicious attacks, causing even more damage on the roads. Addressing these problems, we propose a high-level system architecture and a security protocol specifically designed for congestion detection based on vehicle-to-infrastructure (V2I) type communication. The security properties of our proposed approach are formally verified using the ProVerif tool, and its efficiency compared to the traditional traffic light systems is demonstrated through simulations with the Veins framework. Results show that our system is secure against a large set of attacks, and can have lower total travelling time compared to three traditional traffic light approaches based on induction loop, lane area detector/camera (installed near the junctions), and static lights.

Urban bus positioning: Location based services and high level system architecture

Today’s urban transport systems are dominated by private vehicles, which are significant contributors to traffic congestion and pollution. This is expected to increase as the urban population grows, predicted to account for about 68% of the world’s population by 2050. In comparison to private cars, transport systems dominated by buses produce lower traffic congestion and emissions. Therefore, improvements in bus operation activities most of which require information on bus location (i.e. location based services) should facilitate urban transport sustainability.However, to date there is no agreement globally on the location based services, their location requirements and technologies to deliver significant improvement in bus operations. Therefore, this paper creates for the first time, a comprehensive list of bus operation services and specifies the performance requirements. These are considered together with challenging spatio-temporal characteristics of the urban environment to specify a high-level location determination system architecture for urban bus operations. The services, their requirements, standards and positioning system architecture are essential for the formulation of appropriate policies, regulation, service provision, and development and procurement of urban bus positioning systems.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

Deep learning acceleration based on in-memory computing

Performing computations on conventional von Neumann computing systems results in a significant amount of data being moved back and forth between the physically separated memory and processing units. This costs time and energy, and constitutes an inherent performance bottleneck. In-memory computing is a novel non-von Neumann approach, where certain computational tasks are performed in the memory itself. This is enabled by the physical attributes and state dynamics of memory devices, in particular, resistance-based nonvolatile memory technology. Several computational tasks such as logical operations, arithmetic operations, and even certain machine learning tasks can be implemented in such a computational memory unit. In this article, we first introduce the general notion of in-memory computing and then focus on mixed-precision deep learning training with in-memory computing. The efficacy of this new approach will be demonstrated by training the MNIST multilayer perceptron network achieving high accuracy. Moreover, we show how the precision of in-memory computing can be further improved through architectural and device-level innovations. Finally, we present system aspects, such as high-level system architecture, including core-to-core interconnect technologies, and high-level ideas and concepts of the software stack .

An Automatic and early detection of the deterioration of patients in Intensive and Intermediate Care Units

In the Intensive and Intermediate Care Units of healthcare centres, many sensors are connected to patients to measure high frequency physiological data. In order to analyse the state of a patient, the medical staff requires both appropriately presented and easily accessed information. As most medical devices do not support the extraction of digital data in known formats, medical staff need to fill out forms manually. The traditional methodology is prone to human errors due to the large volume of information, with variable origins and complexity. The automatic and real-time detection of changes in parameters, based on known medical rules, will make possible to avoid these errors and, in addition, to detect deterioration early. In this article, we propose and discuss a high-level system architecture, an embedded system that extracts the electrocardiogram signal from an analog output of a medical monitor, and a real-time Big Data infrastructure that integrate Free Software products. We believe that the experimental results, obtained with a simple prototype of the system, demonstrate the viability of the techniques and technologies used, leaving solid foundations for the construction of a reliable system for medical use, able to scale and support an increasing number of patients and captured data.

An Audio-Visual System for Object-Based Audio: From Recording to Listening

Object-based audio is an emerging representation for audio content, where content is represented in a reproduction-format-agnostic way and, thus, produced once for consumption on many different kinds of devices. This affords new opportunities for immersive, personalized, and interactive listening experiences. This paper introduces an end-to-end object-based spatial audio pipeline, from sound recording to listening. A high-level system architecture is proposed, which includes novel audio-visual interfaces to support object-based capture and listener-tracked rendering, and incorporates a proposed component for objectification , that is, recording content directly into an object-based form. Text-based and extensible metadata enable communication between the system components. An open architecture for object rendering is also proposed. The system's capabilities are evaluated in two parts. First, listener-tracked reproduction of metadata automatically estimated from two moving talkers is evaluated using an objective binaural localization model. Second, object-based scene capture with audio extracted using blind source separation (to remix between two talkers) and beamforming (to remix a recording of a jazz group) is evaluated with perceptually motivated objective and subjective experiments. These experiments demonstrate that the novel components of the system add capabilities beyond the state of the art. Finally, we discuss challenges and future perspectives for object-based audio workflows.

The parasitic manipulation of an animat’s behavior

Abstract This paper uses an analogy to parasitic zombiing in nature to show how a robot’s goal-setting and behavior can be changed. The implementation of these parasite-like mechanisms is based on the emotional and temperamental architecture of the robot’s control system. A number of possible mechanisms used by parasites to manipulate hosts’ behavior have been considered, including changes to the host’s behavioral characteristics, the direct re-ordering of behavioral responses, and the reorientation of hosts’ reactions. It was determined that each of these mechanisms can be employed using a relatively primitive and restricted set of influences or parasite control signals. This approach led us to propose several variants of a high-level control system architecture. The main assumption was that the center of mass of a robot’s control system lies on the level of basic behavioral procedures while high-level control can be realized using parasite-like methods.

Nonlinear Radar for Finding RF Electronics: System Design and Recent Advancements

An extensive review of nonlinear radar systems is performed. Emphasis is placed on designs relevant to detecting RF electronics that were not intentionally manufactured as visible radar targets. The state of the art in nonlinear radar is conveyed by presenting high-level system architecture, explaining the rationale behind design decisions pertaining to that architecture, and listing the specifications that nonlinear radar designers have achieved. The authors’ recent advancements in nonlinear radar technology are summarized.

Real-time wireless multisensory smart surveillance with 3D-HEVC streams for internet-of-things (IoT)

This paper presents the design of a novel, real-time, wireless, multisensory, smart surveillance system with 3D-HEVC features. The proposed high-level system architecture of the surveillance system...

Real-time wireless multisensory smart surveillance with 3D-HEVC streams for internet-of-things (IoT)

This paper presents the design of a novel, real-time, wireless, multisensory, smart surveillance system with 3D-HEVC features. The proposed high-level system architecture of the surveillance system is analyzed. The advantages of HEVC encoding are presented. Methods for synchronization between multiple streams are presented. Available wireless standards are presented and compared. A network-adaptive transmission protocol for a reliable, real-time, multisensory surveillance system is proposed. Adaptive packet frame grouping (APFG) and adaptive quantization are deployed to maximize the quality-of-experience (QoE). Measurements of the proposed protocol have been shown to provide superior results compared to existing transport protocols.

Capacitive Biopotential Measurement for Electrophysiological Signal Acquisition: A Review

A conventional clinical biopotential measurement system requires the use of wet electrodes that are in contact with skin using conductive gel. Those systems can acquire high-quality signals but may not be feasible for a long-term purpose due to the skin irritation and allergic contact dermatitis. The emerging of human-centered monitoring calls for alternative solutions. Recent technological advances enable capacitive measurement of electrophysiological measurement, which can acquire signals through cloth or even with an air gap; however, difficulties still exist. This paper systemically reviews the recent progress in capacitive measurement from electrode design, analog front end to high-level system architecture, aiming to provide comprehensive and practical instructions for entire system design. The current development serves as the solid fundamental, raising new solutions for future improvement. In addition, the challenges and the strategies are highlighted in order to demonstrate a technical guide. Some perspectives based on our experience are also discussed hoping to inspire new idea and technique in this area.

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

The use of unmanned aerial systems (UASs) in both the public and military environments is predicted to grow significantly. As the demand for UASs grows, the availability of more robust and capable vehicles that can perform multiple mission types will be needed. In the public sector, the demand will grow for UASs to be used for agriculture, forestry, and search and rescue missions. Militaries continue to demand more UAS capabilities for diverse operations around the world. Significant research has been performed and continues to progress in the areas of autonomous UAS control. A majority of the work focuses on subsets of UAS control: path planning, autonomy, small UAS controls, and sensors. Minimal work exists on a system-level problem of multiple-scenario UAS control for integrated systems. This paper provides a high-level modular system architecture definition that is modifiable across platform types and mission requirements. A review of the current research and employment of UAS capabilities is provided to evaluate the state of the capabilities required to enable the proposed architecture.

The ATLAS EventIndex: architecture, design choices, deployment and first operation experience

The EventIndex is the complete catalogue of all ATLAS events, keeping the references to all files that contain a given event in any processing stage. It replaces the TAG database, which had been in use during LHC Run 1. For each event it contains its identifiers, the trigger pattern and the GUIDs of the files containing it. Major use cases are event picking, feeding the Event Service used on some production sites, and technical checks of the completion and consistency of processing campaigns. The system design is highly modular so that its components (data collection system, storage system based on Hadoop, query web service and interfaces to other ATLAS systems) could be developed separately and in parallel during LSI. The EventIndex is in operation for the start of LHC Run 2. This paper describes the high-level system architecture, the technical design choices and the deployment process and issues. The performance of the data collection and storage systems, as well as the query services, are also reported.

Cloud-Based Collaborative Decision Making

The complexity of many decision problems of today’s globalized world requires new innovative solutions that are built upon proven decision support technology and also recent advancements in the area of information and communication technology (ICT) such as Cloud Computing and Mobile Communication. A combination of the cost-effective Cloud Computing approach with extended group decision support system technology bears several interesting unprecedented opportunities for the development of such solutions. These opportunities include ubiquitous accessibility to decision support software and, thus, the possibility to flexibly involve remote experts in group decision processes, guided access to background information, and facilitation support to direct group decision processes. The architects of such future solutions are challenged by numerous requirements that need to be considered and reflected in an integrated architectural approach. This article presents a thorough analysis of major design considerations for software solutions for collaborative decision making from a broad range of perspectives especially including the business process management perspective and the Cloud Computing perspective. The proposed architectural approach of the GRUPO-MOD system demonstrates how one can address the requirements in one integrated system architecture that supports different deployment options of Cloud Computing. A refinement of the high-level system architecture into a corresponding implementation architecture that builds on widely adopted standards such as OSGi and industry proven technology such as the Eclipse platform is also given in the article.