Communication Architecture Research Articles

PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency

The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto algorithm for various computer vision applications. In intelligent edge devices, sensor data streams acquired by the device are processed by a DNN application running on either the edge device itself or in the cloud. However, “edge-only” and “cloud-only” execution of State-of-the-Art DNNs may not meet an application’s latency requirements due to the limited compute, memory, and energy resources in edge devices, dynamically varying bandwidth of edge-cloud connectivity networks, and temporal variations in the computational load of cloud servers. This work investigates distributed (partitioned) inference across edge devices (mobile/end device) and cloud servers to minimize end-to-end DNN inference latency. We study the impact of temporally varying operating conditions and the underlying compute and communication architecture on the decision of whether to run the inference solely on the edge, entirely in the cloud, or by partitioning the DNN model execution among the two. Leveraging the insights gained from this study and the wide variation in the capabilities of various edge platforms that run DNN inference, we propose PArtNNer , a platform-agnostic adaptive DNN partitioning algorithm that finds the optimal partitioning point in DNNs to minimize inference latency. PArtNNer can adapt to dynamic variations in communication bandwidth and cloud server load without requiring pre-characterization of underlying platforms. Experimental results for six image classification and object detection DNNs on a set of five commercial off-the-shelf compute platforms and three communication standards indicate that PArtNNer results in 10.2× and 3.2× (on average) and up to 21.1× and 6.7× improvements in end-to-end inference latency compared to execution of the DNN entirely on the edge device or entirely on a cloud server, respectively. Compared to pre-characterization-based partitioning approaches, PArtNNer converges to the optimal partitioning point 17.6× faster.

An Architecture Superposing Indefinite Causal Order and Path Superposition Improving Pauli Channels’ Parameter Estimation

Quantum Parameter Estimation (QPE) is commonly led using quantum probe states for the characterization of quantum systems. For these purposes, Quantum Fisher Information (QFI) plays a crucial role by imposing a lower bound for the parametric estimation of quantum channels. Several schemes for obtaining QFI lower bounds have been proposed, particularly for Pauli channels regarding qubits. Those schemes commonly employ either the individual channel, multiple copies of it, or arrangements including communication architectures. The present work aims to propose an architecture involving path superposition and causal indefinite order in superposition. Thus, by controlling the symmetry balance of this superposition, it reaches notable improvements in quantum parameter estimation. The proposed architecture has been tested to find the best possible QPE bounds for a representative and emblematic set of Pauli channels. Further, for the most reluctant channels, it was revisited testing the architecture again under a primary path superposition (using double teleportation) and also using entangled probe states to recombine their outputs with the original undisturbed state. Notable outcomes practically near zero were found for the QPE bounds, stating a hierarchy between the approaches, but anyway reaching a perfect theoretical QPE, particularly for the last path superposition including the proposed architecture.

CIRCA: Cloud-based intelligent robotic system for chemical analysis in streamlining and automating open-ended measurement tasks

The realm of chemical analysis has witnessed remarkable advancements through the automation of measurement techniques. This automation has played a pivotal role in reducing the labor-intensive and time-consuming aspects associated with chemical analysis, while simultaneously minimizing dependence on specialized knowledge. However, in practical analytical work, automated chemical measurement instrumentation faces persistent challenges due to uncertainties regarding the number and timing of sample measurement tasks reaching the laboratory for analysis. These challenges result in low instrument sharing rates and a deficiency in effective communication mechanisms between sample providers and administrators, impeding the further enhancement of automated chemical measurement efficiency. Here we proposed a Cloud-based Intelligent Robotic System for Chemical Analysis (CIRCA) and demonstrated its application by the analysis of soluble iron in water samples. CIRCA comprises a cloud platform and an analytical robot, which efficiently manages and executes measurement tasks using a Process Simulation Algorithm and a Task Coordination Algorithm. The Process Simulation Algorithm plans the mechanical actions of analytical robot modules, while simulating processing times for each task. Simultaneously, the Task Coordination Algorithm optimizes the timing of each measurement task. By adopting CIRCA, sample providers can remotely submit measurement tasks without negotiation, allowing for the seamless management of open-ended analytical tasks, measurement execution, and analytical result feedback, thereby enhancing the overall efficiency of the analytical workflow. CIRCA represents a pioneering cloud-based intelligent robotic system that revolutionizes chemical analysis by addressing persistent challenges in managing open-ended measurement tasks. Its innovative design and distinctive algorithms, coupled with cloud-based communication logic and architecture, as well as the robotic physical framework, empower seamless task coordination, remote synergism, automated execution, and result feedback, ultimately enhancing analytical efficiency.

A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving Techniques

Abstract: Privacy leakage that occurs when many IoT devices are utilized for training centralized models, a new distributed learning framework known as federated learning was created, where devices train models together while keeping their private datasets local. In a federated learning setup, a central aggregator coordinates the efforts of several clients working together to solve machine learning issues. The privacy of each device's data is protected by this setup's decentralized training data. Federated learning reduces traditional centralized machine learning systems' systemic privacy issues and costs by emphasizing local processing and model transfer. Client information is stored locally and cannot be copied or shared. By utilizing a centralized server, federated learning enables each participant's device to collect data locally for training purposes before sending the resulting model to the server for aggregate and subsequent distribution. As a means of providing a comprehensive review and encouraging further research into the topic, we introduce the works of federated learning from five different vantage points: data partitioning, privacy method, machine learning model, communication architecture, and systems heterogeneity. Then, we organize the issues plaguing federated learning today and the potential avenues for a prospective study. Finally, we provide a brief overview of the features of existing federated knowledge and discuss how it is currently being used in the field.

Towards the Integration and Convergence Between 5G and TSN Technologies and Architectures for Industrial Communications: A Survey

Towards the Integration and Convergence Between 5G and TSN Technologies and Architectures for Industrial Communications: A Survey

An Efficient Distributed Reinforcement Learning Architecture for Long-Haul Communication Between Actors and Learner

An Efficient Distributed Reinforcement Learning Architecture for Long-Haul Communication Between Actors and Learner

Research оf API architecture for client­server communication

API architectures are an important component of modern systems. They provide interaction between various programs and services, and also provide access to data and functionality to other systems. Equally important is the impact of APIs on business. There are several API trends that affect business: API usage expansion, API usage simplification, API security. These trends have a significant impact on business, they allow companies to: increase interaction with customers, collaborate with partners, optimize internal processes. However, choosing the right API architecture is a difficult task. There are many different API architectures, each with its own advantages and disadvantages. Also, different types of systems have different API architecture requirements. Possible problems can be a lack of sufficient documentation, which makes it difficult for developers to choose the right solution, also incompatibilities between different protocols, data formats, request and response methods, which can make it difficult to interact between different systems, and the cost of implementing an API architecture can be significant, which can be important factor for developers. Future prospects may include: creation of new architectures, expansion of API usage, API automation. This study is devoted to the study of API architectures for communication between the client and the server. The goal of the study is to determine which API architecture is best for a specific type of system with specific requirements. To achieve this research goal, an analysis of existing API architectures will be conducted. The analysis will include a study of the advantages and disadvantages of different architectures, as well as their compatibility with the requirements of different types of systems. The results of the study aim to consider the following API architectures: SOAP, REST, GraphQL, gRPC, WebSocket, WebHook, MQTT, and also help developers in choosing the optimal API architecture to meet the specific needs of their projects.

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Abstract This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.

Assessment of 5G Connectivity for UAV Operations in Urban Environments: An Analysis of Network Performance and UAV Communication Architecture

This article presents an in-depth analysis of the integration of 5G connectivity for unmanned aerial vehicle (UAV) operations in urban environments, focusing on network performance and the architectural framework necessary for optimal UAV communication. As UAVs become increasingly prevalent in urban applications, from surveillance to delivery services, the demand for robust and reliable communication systems has become paramount. This study explores the unique challenges posed by urban settings, such as signal obstruction and high-density network traffic, and examines how 5G technology offers solutions through advanced features like beamforming. By detailing the setup and evaluation of a UAV communication architecture that incorporates 5G modules, including the use of the NVIDIA Jetson AGX Orin Developer Kit for real-world testing, this article highlights the significant improvements in data rate, latency, and reliability achieved with 5G networks compared to traditional communication technologies. The findings underscore the transformative potential of 5G connectivity in enhancing UAV operational efficiency and reliability in complex urban landscapes. Through case studies and practical insights, the article also provides a glimpse into the future of urban UAV applications, paving the way for further research and development in this dynamic field.

Deep Reinforcement Learning Based Reliable Data Transmission Scheme for Internet of Underwater Things in 5G and Beyond Networks

The Internet of Underwater Things (IoUT) is an interconnected communication ecosystem for underwater devices in maritime environments. IoUT devices range from seabed sensors to ships and boats in oceans which help in diverse practical applications of underwater information ranging from marine transportation, navigation, and underwater exploration to disaster prevention with intelligent monitoring. There exist significant obstacles within the present architecture of the IoUT. This article proposes three distinct methodologies aimed at enhancing the robustness and reliability of the communication architecture for 5G and future networks. Firstly, the employment of the Deep Reinforcement Learning approach is advocated for the identification of anomalies and faulty sensors. Secondly, energy consumption is identified as a crucial factor for IoUT devices. To address this concern, the integration of renewable energy sources, namely oxygen rechargeable batteries, with nano-generators is employed. This integration allows for the replenishment of the batteries through the harnessing of kinetic energy from water. Thirdly, the process by which information can be efficiently transmitted from IoUT sensors located on the seabed to the final terrestrial network is delineated, with an emphasis on minimizing the number of hops required. Additionally, a case study is presented, focusing on the mitigation of interference within the IoUT. Lastly, the article delves into the various research challenges and unresolved matters that persist within the realm of IoUT.

INFRASTRUCTURAL INSECURITY: GEOPOLITICS IN THE STANDARDIZATION OF TELECOMMUNICATIONS NETWORKS

This paper argues that the production of ‘infrastructural insecurity’ is an inherent part of the standardization of information networks. Infrastructural insecurity is the outcome of an intentional process within infrastructural production, standardization, and maintenance that leaves end-users of the infrastructure vulnerable to attacks that benefit a particular actor. We ground this analysis in an interrogation of the responses to the disclosure of three security vulnerabilities in telecommunications networks, namely (1) a security flaw in Signaling System No. 7 (SS7) that allows for the data interception and surveillance, SMS interception and location tracking by third parties, (2) the lack of encryption of permanent identifiers that allowed for the deployment of rogue base stations, which allowed for man-in-the-middle attacks, resulting in interception of all voice and data traffic in a physical signal vicinity, and (3) the lack of forward secrecy between user-equipment and the home network, which allows for the decryption of current encrypted data stream if credentials were obtained in the past. To research the shaping of communication and infrastructure architectures in the face of insecurities, we develop a novel approach to the study of Internet governance and standard-setting processes that leverages web scraping and computer-assisted document set discovery software tools combined with document analysis. We bring these methods into conversation with theoretical approaches from material media studies, science and technology studies, and critical security studies. This is an important contribution because it asks fundamental questions about the adequacy and legitimacy of standardization processes.

Secure nano-communication framework using RSCV cryptographic circuit in IBM Q

In the cryptographic domain, quantum and its real-time hardware simulation make it easier to secure data during communication. Here, using quantum logic, a unique encryption technique called Reversible select, cross, and variation (RSCV) encryption and decryption, which involves swapping input data halves, is shown. In this article using IBM Q, we created a cryptographic encoder and decoder circuit design utilizing various quantum gates. Based on the encoder/decoder circuit, a simple nanocommunication framework is proposed. Further, to explore the application of the noise model, how to utilize this model to create noisy replicas of these quantum circuits to research the impacts of noise that occur for actual device output is shown. To reduce measurement mistakes, measurement calibration is performed using qiskit ignis model. Preparing all 2n basis input states and calculating the likelihood of counting in the other basis states are the key concepts. The percentage improvement we achieved is 40%, 30%, and 30%, respectively, compared to earlier ones, in RSCV encryption, decryption, and RSCV cryptographic communication architecture for fake provider noise error model. It is feasible to adjust the average outcomes of an additional interesting experiment using these calibrations.

Optimizing Network Reliability in Dynamic or Rapidly-Changing Topologies

With communication, power and transportation networks facing ever-greater dynamics, optimizing reliability poses significant modeling and computational challenges. Most existing reliability optimization research assumes static topologies with predefined failure probabilities and connectivity demands. This work puts forth a time-varying mathematical program to maximize expected network reliability under shifting topological uncertainties. Sets, parameters, decision variables and constraints are defined as functions of time to capture variability in links, capacities, risks and demands. The formulation adapts to detected changes through re-optimization triggered by model error thresholds. Approximation techniques based on constraint sampling and decomposition address solve efficiency for large fluid networks. Evaluations on simulated dynamic test cases demonstrate superior reliability versus periodic and static optimization approaches while fulfilling budget limits. Accuracy metrics assess model fidelity over increasing volatility levels. Implementation case studies exhibit optimized resilience in software-defined communication architectures, smart grid reconfiguration, and adaptive transportation maintenance scenarios. The mathematical programming foundation provides a pathway to achieve connectivity resilience for critical infrastructure networks facing intensifying dynamics. The integration of optimization, prediction and adaptive response provides a paradigm for decision making under modern uncertain conditions.

Dziennikarze na cyfrowych sterydach

Journalists on digital steroids The article is a media studies analysis of the processes and mechanisms of digitization as the main cause of the technological convergence from print to digital, which transformed all media, creating a new architecture of communication. The place of the old corporate media order was taken by a dozen competing concepts forming a fragmented networked media landscape. The research goal of the article is to answer the questions of how this transition from print to digital affects the status of the journalistic profession: first, what is the impact of digital on the information ecosystem and the journalist’s position in it? second, is there a relationship between the medium of press material and its quality? third, has digital acted as a steroid to accelerate the intensity of journalistic activity? These important and complex problems of modern journalism, put in the form of a metaphorical shortcut – “digital steroids” – are the subject of research in this publication.

Access Control, Key Management, and Trust for Emerging Wireless Body Area Networks.

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs.

A novel blockchain-based digital forensics framework for preserving evidence and enabling investigation in industrial Internet of Things

To address challenges in digital evidence collection and responsibility determination for industrial safety accidents involving industrial Internet of Things (IIoT) device nodes, this paper proposes a blockchain-based digital forensic scheme within the IIoT communication architecture. The scheme utilizes a decentralized blockchain storage mechanism to enable remote storage of digital forensic data. Additionally, it leverages smart contract mechanisms to facilitate efficient retrieval and tracing of related evidence chains. To enhance data security of IIoT device nodes, a token mechanism is implemented for access control. Moreover, to meet real-time evidence acquisition requirements in IIoT, an efficient batch consensus mechanism is proposed. Experimental simulations demonstrate the superiority of the novel consensus algorithm compared to the traditional Delegated Proof-of-Stake (DPOS) consensus in the proposed scheme for the IIoT environment. It meets speed requirements for evidence collection, ensuring tamper-proof, non-repudiable, and permanent storage of digital forensic data. Consequently, the application of blockchain technology for judicial access and evidence storage has made significant contributions to digital forensics within the IIoT context.

Synchronized Control of Robotic Arm based on Virtual Reality and IMU Sensing

This study introduces a robotic control system that combines virtual reality integration and inertial measurement units (IMU) using mixed reality (MR) devices. The system integrates three main modules: (1) virtual reality (VR), which simulates remote reality by the Hololens2, (2) the wearable IMUs device, which captures the operator's hand movements; and (3) the robotic arm UR5, which is controlled by the user and the VR environment. The virtual and physical systems communicate via a Message Queuing Telemetry Transport (MQQT) communication architecture to establish communication between modules. To introduce a closed-loop control system for the robotic arm, model predictive control (MPC) was achieved with precise path planning to provide a flexible, intuitive, and reliable method to operate the remote-controlled manipulator. To validate the system integration and functions, two demonstrations were conducted: (a) the offline mode, where the VR module of the robotic arm was controlled by the IMUs device to check correctness, and (b) the online mode, where the control command was transferred to UR5 to complete a target mission via artificial potential field (APF) adjustment. The primary outcome of this study was the development of virtual and real industrial robotic arms to test the developed model in VR and shop floor labs.

Intention recognition-based human–machine interaction for mixed flow assembly

Mixed-flow assembly provides a highly adaptable solution for production processes, catering to the extensive customization requirements in product manufacturing. Human–machine interaction (HMI) can significantly enhance the efficiency, flexibility, and responsiveness of mixed-flow production lines by enabling operators to collaborate with machines. However, current HMI research primarily focuses on temporal or spatial interaction, where tasks are completed independently by the operator and machine in a predetermined sequence, with emphasis on collision avoidance in a shared workspace. Research on spatial–temporal HMI is currently lacking. This paper addresses this gap through three comprehensive steps. Firstly, we develop an intention recognition model based on Gaussian mixture distribution to effectively identify the user’s operational intention. Subsequently, we design a hybrid industrial communication architecture to facilitate intent sharing among various production elements. Finally, an empirical study is conducted to assess the impact of different forms of HMI on assembly efficiency and error rates. Our experimental results demonstrate that spatial–temporal HMI can enhance assembly efficiency by approximately 15% and reduce the defective rate. In future studies, the proposed spatial–temporal cooperation platform could integrate additional perception technologies to alleviate assembly fatigue and improve operators’ well-being.

A Hybrid Millimeter-Wave and Free-Space-Optics Communication Architecture with Adaptive Diversity Combining and HARQ Techniques

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance.

Macroscopic model and statistical model to characterize electromagnetic information of a digital coding metasurface.

A digital coding metasurface is a platform connecting the digital space and electromagnetic wave space, and has therefore gained much attention due to its intriguing value in reshaping wireless channels and realizing new communication architectures. Correspondingly, there is an urgent need for electromagnetic information theory that reveals the upper limit of communication capacity and supports the accurate design of metasurface-based communication systems. To this end, we propose a macroscopic model and a statistical model of the digital coding metasurface. The macroscopic model uniformly accommodates both digital and electromagnetic aspects of the meta-atoms and predicts all possible scattered fields of the digital coding metasurface based on a small number of simulations or measurements. Full-wave simulations and experimental results show that the macroscopic model is feasible and accurate. A statistical model is further proposed to correlate the mutual coupling between meta-atoms with covariance and to calculate the entropy of the equivalent currents of digital coding metasurface. These two models can help reconfigurable intelligent surfaces achieve more accurate beamforming and channel estimation, and thus improve signal power and coverage. Moreover, the models will encourage the creation of a precoding codebook in metasurface-based direct digital modulation systems, with the aim of approaching the upper limit of channel capacity. With these two models, the concepts of current space and current entropy, as well as the analysis of information loss from the coding space to wave space, is established for the first time, helping to bridge the gap between the digital world and the physical world, and advancing developments of electromagnetic information theory and new-architecture wireless systems.