Communication Architecture Research Articles

Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.

Security in the Internet of Things (Iot): A Review, Block Chain Applications

The rise of smart technologies in various domains such as homes, cities, and other interconnected systems has propelled the Internet of Things (IoT) into a realm of immense influence, potential, and expansion. Cisco Inc. has forecasted a staggering 50 billion connected devices by 2020, underscoring the rapid proliferation of IoT. Despite this growth, a significant concern looms over the security of these IoT devices, as many are susceptible to hacking and compromise. Their limited computational, storage, and networking capabilities render them more vulnerable compared to traditional endpoint devices like smartphones, tablets, and computers.This paper examines and categorizes the major security challenges facing IoT devices. It reviews prevalent security issues within the layered architecture of IoT, encompassing networking, communication, and management protocols. The paper also delineates the security prerequisites for IoT and surveys current attacks, threats, and state-of-the-art solutions. Additionally, it presents a comparative analysis of IoT security challenges and existing solutions found in the literature.Furthermore, the paper delves into the potential of blockchain, the foundational technology behind bitcoin, as a pivotal tool for addressing numerous security issues in IoT. It discusses how blockchain can enhance security in IoT ecosystems. The paper concludes by outlining open research problems and ongoing challenges in IoT security, underscoring the need for continued innovation in this domain.

Toward Full Passive Internet of Things: Symbiotic Localization and Ambient Backscatter Communication

In this paper, we proposed a symbiotic localization and ambient backscatter communication (SLABC) architecture, which uses existing ambient backscatter communication (AmBC) hardware and received signals to help Internet of Things (IoT) localize target objects. The SLABC can be viewed as a specific realization of integrated passive/symbiotic sensing and communication, which has two mutually beneficial stages: 1) sensing stage with generalized Prony with homologous matching (GPHM) method; 2) communication stage with multi-source constant modulus algorithm (MSCMA) method. To formulate SLABC, we propose a unified TMSC signal model to characterize the complex multi-source, multi-path and multi-reflection symbiotic communication (TMSC) and facilitate the key information extraction. Utilizing the frequency domain expression of the unified signal model and the similarity between multi-path channel coefficients and tap coefficients of equalizers, our proposed GPHM and MSCMA methods are efficient to mitigate the TMSC interference and retrieve the interested information to locate targets and communicate. The proposed SLABC concept updates the symbiotic AmBC IoT with the least cost and reduces the dependence on hardware stacking. The simulation results show that: 1) information extracted by GPHM and MSCMA can help localization achieve high accuracy and reduce the complexity of multi-path interference mitigation; 2) both of the two methods have a positive influence on each other.

Experimental demonstration of a reconfigurable free-space receiver implementing polarization routing and filtering for daytime quantum key distribution.

Free-space quantum key distribution (QKD) systems are often designed to implement polarization-encoding protocols. Alternatively, time-bin/phase-encoding protocols are considerably more challenging to perform over a channel experiencing atmospheric turbulence. However, over the last decade, new and improved optical platforms have revived the interest in them. In this paper, we present a free-space multi-protocol receiver designed to work with three different time-bin/phase-encoding protocols highlighting its interoperability with different systems and architectures for potential satellite-based communications. We also present a detailed analysis of different experimental configurations when implementing the coherent one-way (COW) protocol in a free-space channel, as well as a polarization filtering technique showing how time-bin/phase-encoding protocols could be used for QKD applications in daylight conditions. We demonstrate secret key rates of several kbps for channels with a total 30 dB attenuation even with moderately high QBERs of ≈3.5%. Moreover, a 2.6 dB improvement in the signal to noise ratio is achieved by filtering background light in the polarization degree of freedom, a technique that could be used in daylight QKD.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process.We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM’s ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM.Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Topologies in distributed machine learning: Comprehensive survey, recommendations and future directions

With the widespread use of distributed machine learning (DML), many IT companies have established networks dedicated to DML. Different communication architectures of DML have different traffic patterns and different requirements on network performance, which is closely related to network topology. However, traditional network topologies usually pursue general goals and are agnostic to the special communication pattern of the applications. The mismatch between network topology and the applications will directly affect the training performance. Although some studies have analyzed the effect of topology on training performance, the topologies and communication architectures involved are not comprehensive, and it is still not known which topology is appropriate for which communication architecture. This survey investigates typical topologies and analyzes whether they meet the requirements of three commonly used communication architectures (i.e., Parameter Server (PS), Tree and Ring architectures) of DML. Specifically, the topology requirements of each communication architecture and two common topology requirements (i.e., high scalability and fault tolerance) for DML are studied firstly. Next, whether these topologies meet the topology requirements is analyzed. Then, this paper discusses potential technologies and approaches to construct the appropriate scheme for each topology requirement, and then presents DMLNet, a novel network topology that suits the three communication architectures. Finally, several potential directions for future research are outlined.

A Distributed Learning Architecture for Semantic Communication in Autonomous Driving Networks for Task Offloading

A Distributed Learning Architecture for Semantic Communication in Autonomous Driving Networks for Task Offloading

Situational model of bullying in business communication

The subject of the study is speech activity affecting the participants of bullying situations in the context of business communications; linguistic means of implementing business bullying strategies and tactics when creating a negative media image of companies and persons in the context of information attacks; media linguistic characteristics of texts related to bullying phenomena in business communication (structure, vocabulary, style, etc.); extralinguistic factors influencing the manifestation and perception of bullying in the business environment, including socio-cultural, psychological and economic aspects. The object of the study is destructive verbal content with signs of business bullying placed in open communication channels, including its media and linguistic manifestations. Within the framework of this work, the emphasis is placed on the specifics of the media linguistic analysis of business communication texts in the context of bullying, as well as on external factors that can influence this process. Special attention is paid to the structure of subject-object relations in the practice of information aggression directed against business. The methodology has developed under the influence of modern media linguistics, when an activity-procedural, praxeological approach to the analysis of the media text is implemented. The thematic discourse of business bullying is distinguished by the length of time, complex interactions, and multicomponent structure of the linguistic architecture of media communication, so the basic method was the praxeological transdisciplinary and interdisciplinary integration of research methods, procedures and techniques – formed the basis of the work. The relevance of the research is due to the combination of the rapidly changing digital landscape, the social responsibility of business and the need for effective strategies to prevent or respond to bullying cases in business communication. The scientific novelty lies in the identification of linguistic signs of business bullying and the construction of communication models of business bullying. The results obtained can be used both in academic research and in the practical activities of the business community. The author proposes to build communication situational models of bullying in the context of business communication on the basis of subject-object relations, which allow to determine patterns of information aggression, analyze components, specify the role of participants, isolate manipulative practices, build business risk strategies, formulate strategies to combat and overcome the consequences of business bullying.

Distributed swarm system with hybrid-flocking control for small fixed-wing UAVs: Algorithms and flight experiments

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker-Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs.

Introduction to the Special Issue on Next-Generation On-Chip and Off-Chip Communication Architectures for Edge, Cloud and HPC

Introduction to the Special Issue on Next-Generation On-Chip and Off-Chip Communication Architectures for Edge, Cloud and HPC

Real-time signal detection system and reliability analysis of wireless networks under 5G communication architecture

Abstract This paper first analyzes the network for 5G communication architecture and builds a framework for signal detection systems for wireless networks based on this architecture. Secondly, to better ensure the reliability of the signal detection system, the AE-ELM algorithm is built for wireless network signal detection by introducing an automatic coding machine and an overrun learning mechanism. Finally, an example is used to verify the reliability of the wireless network signal detection system in real-time. The results show that the signal detection rate of the real-time signal detection system with data volume over 300GB is 93% correct, and the maximum rate of missed rejection is about 6%. When SNR=25dB, the BER of the AE-ELM algorithm fluctuates around 10-5 with a variation of only about 0.1%. This shows that the signal detection system of a wireless network under 5G communication architecture has good reliability.

Real-time monitoring system for damage diagnosing on civil structures using the Internet of Things (IoT)

This article presents the evaluation of a real-time monitoring system based on acceleration data obtained from a reduced-scale 5-story building prototype. Development is achieved under the Internet of Things (IoT) approach through the cloud using the MQTT messaging protocol. The damage evaluation is achieved by comparing 2 different states of the structural system, for this, the parameters of the civil structure model are used, which are estimated using the normalized least squares algorithm with forgetting factor. In this stage, the algorithms are programmed in Matlab-Simulink and executed on a RaspBerry PI 4. The experimental results confirm the versatility of the proposal and the relevance of the used decentralized communication architectures for real-time monitoring.

Exploration of cross-border e-commerce live streaming and local culture dissemination model based on cluster analysis

Abstract This paper first constructs the logical architecture and implementation mechanism of cross-border e-commerce live streaming and local cultural communication and proposes a cultural communication influence model based on clustered ellipsoid and spatial cluster analysis. By using first-order linear approximation and PMA for non-probabilistic uncertainty cultural communication analysis, the optimal global solution of the system response is solved. Finally, the results of cross-border e-commerce live streaming and local cultural communication power are discussed by specifically analyzing the characteristic quantities of cultural communication structure and local cultural communication effect. The results show that the maximum value is 39.8034 in the TOP20 of local culture communication power, the mean value is 17.3431 in the communication effect dimension, and the mean value is 16.17 in the communication structure dimension. This paper provides a new model to explore for cross-border e-commerce live streaming and local culture communication.

ComEdge: Cloud-Native Platform for Integrated Computing and Communication in Satellite–Terrestrial Network

Leveraging technological advancements such as containers, microservices, and service mesh, cloud-native edge computing (CNEC) has become extensively discussed and applied in both academia and industry. The integration of mobile edge computing and communication is crucial for the future communication architecture in order to fully utilize distributed and fragmented communication resources and computing power. The potential for cloud-native integration can help merge mobile edge computing and communication, enhancing network flexibility and resource utilization. This paper investigates the implementation plan for extending cloud-native capabilities to integrated computing and communication (INCCOM) in the satellite–terrestrial network. We construct an experimental verification platform called ComEdge in a real-world setting. Subsequently, we analyze the architecture, functional characteristics, and deployment of the platform in a real-world environment. Furthermore, we explore the solution of deep reinforcement learning in the deployment of cloud-native core network and conduct a preliminary verification of the platform’s potential to enable artificial intelligence in a real production environment, which will provide guidance to both academic and industry sectors. Finally, we conduct an analysis on the challenges and opportunities encountered by the cloud-native INCCOM network system.

Design and Implementation of Multi-operative Reversible Gate for Even/Odd Parity Generators in Quantum based Technologies

Quantum technology is graciously budding in nano-communication due to its properties and logical function, having the momentous prosperity of being reversible. It has gained an appeal to future-generation research owing to those sole aspects that may not be explored in the classical realm. A reliable nano-communication system utilizes varied error detection and correction techniques. Beyond low device density, authentic random number generation is a crucial issue in the cryptographic aspects of future communication architecture. To our knowledge, this is the innate study of an intriguing prospect: the design and implementation based on the lower level of power 'even/odd parity generator' using a single multi-operative reversible gate that has been achieved and functionally authenticated in the QCA nanotechnology, likewise in the IBMQ experience allied to quantum-based technologies. This breakthrough in nanotechnology and quantum-based technologies could have significant implications for blooming more efficient, secure communication systems in post-quantum cryptography.

Intelligent Encoding through OCDM for High Capacity and Connectivity in Wireless 5G and Beyond Networks

Multioperator multiservice support over mobile front haul networks would be an important requirement in the evolving 5G Wireless Communication architectures. This in turn dictates the need for high capacity and high connectivity front-haul options. Optical Code Division Multiplexing over Wavelength Division Multiple Access could be one of the most promising system architectures to support the last/first-mile bottleneck of the Mobile Networks. This paper reports a comparison impact of the energy efficiency and electromagnetic radiation effects for a multioperator multiservice OCDM-based Spectrally Amplitude Encoded Analog Radio over Fiber front-haul for a small cell configuration. The WDM multiplexed mm-wave signals at 60GHz and 28GHz and Prime code-based spectral amplitude patterns assigned for conventional services are analyzed and compared with theWavelength Division Multiple Access system of the same kind. The comparison results confirm that the proposed architecture will be a suitable candidate to meet the 5G challenges of Mobile fronthaul Networks.

Fedcs: Efficient communication scheduling in decentralized federated learning

Decentralized federated learning is a training approach that prioritizes user data privacy protection, while also offering improved scalability and robustness. However, as the number of edge devices participating in training increases, a significant communication overhead arises among devices located in different geographical locations. Therefore, designing a well-thought-out gradient synchronization strategy is crucial for minimizing the overall communication overhead of training.To tackle this issue, this article introduces a 2D-Ring network structure based parameter synchronization strategy and a 2D-attention-based device placement algorithm, aiming to minimize communication overhead. The parameter synchronization strategy devises a two-layer circular communication architecture for the devices involved in training, thereby reducing the overall frequency of parameter synchronization in decentralized federated learning. By taking into account the total communication overhead and the device placement strategy, an optimization problem is formulated. Specifically, a 2D-attention neural network is constructed to optimize the device placement solution based on 2D-Ring network structure, leading to reduced communication overhead. Moreover, an evaluation model is designed to assess the communication overhead in a complex decentralized system during federated training. This enables precise determination of the total communication overhead throughout the training process, providing valuable insights for devising the device placement strategy. Extensive simulations confirm that the proposed approach achieves a substantial reductions of 55% and 64% in the total communication overhead for decentralized federated learning training with 50 and 100 devices, respectively.

D-BLAC: A dual blockchain-based decentralized architecture for authentication and communication in VANET

With all the advancements in the last decade in Intelligent Transportation systems (ITS), there has been an exponential increase in the number of applications provided to the Vehicular Ad Hoc Networks (VANETs). These applications provide unmatched security and safety services. As a result, there is a huge surge in the amount of data being generated and transmitted within the VANETs. In this paper, we have proposed a dual blockchain based decentralized architecture for authentication of the vehicles and secure & efficient communication within the network. The use of individual blockchains for authentication and message sharing makes the network more efficient and fast while also helping to increase the network security. Both the blockchains use a different consensus algorithm which adds another layer of security. The security and performance evaluation of the proposed architecture proves that it is resistant to a number of security attacks and has a lower computational cost, higher throughput, lower transmission delay and higher vehicle verification rate by a significant amount when compared to existing schemes.

Exploring Blockchain Data Analysis and Its Communications Architecture: Achievements, Challenges, and Future Directions: A Review Article

Blockchain technology is relatively young but has the potential to disrupt several industries. Since the emergence of Bitcoin, also known as Blockchain 1.0, there has been significant interest in this technology. The introduction of Ethereum, or Blockchain 2.0, has expanded the types of data that can be stored on blockchain networks. The increasing popularity of blockchain technology has given rise to new challenges, such as user privacy and illicit financial activities, but has also facilitated technical advancements. Blockchain technology utilizes cryptographic hashes of user input to record transactions. The public availability of blockchain data presents a unique opportunity for academics to analyze it and gain a better understanding of the challenges in blockchain communications. Researchers have never had access to such an opportunity before. Therefore, it is crucial to highlight the research problems, accomplishments, and potential trends and challenges in blockchain network data analysis and communications. This article aims to examine and summarize the field of blockchain data analysis and communications. The review encompasses the fundamental data types, analytical techniques, architecture, and operations related to blockchain networks. Seven research challenges are addressed: entity recognition, privacy, risk analysis, network visualization, network structure, market impact, and transaction pattern recognition. The latter half of this section discusses future research directions, opportunities, and challenges based on previous research limitations.

Fully parallel low‐density parity‐check code‐based polar decoder architecture for 5G wireless communications

AbstractA hardware architecture is presented to decode (N, K) polar codes based on a low‐density parity‐check code‐like decoding method. By applying suitable pruning techniques to the dense graph of the polar code, the decoder architectures are optimized using fewer check nodes (CN) and variable nodes (VN). Pipelining is introduced in the CN and VN architectures, reducing the critical path delay. Latency is reduced further by a fully parallelized, single‐stage architecture compared with the log N stages in the conventional belief propagation (BP) decoder. The designed decoder for short‐to‐intermediate code lengths was implemented using the Virtex‐7 field‐programmable gate array (FPGA). It achieved a throughput of 2.44 Gbps, which is four times and 1.4 times higher than those of the fast‐simplified successive cancellation and combinational decoders, respectively. The proposed decoder for the (1024, 512) polar code yielded a negligible bit error rate of 10−4 at 2.7 Eb/No (dB). It converged faster than the BP decoding scheme on a dense parity‐check matrix. Moreover, the proposed decoder is also implemented using the Xilinx ultra‐scale FPGA and verified with the fifth generation new radio physical downlink control channel specification. The superior error‐correcting performance and better hardware efficiency makes our decoder a suitable alternative to the successive cancellation list decoders used in 5G wireless communication.