Communication Cost Research Articles

3D network-on-chip data acquisition system mapping based on reinforcement learning and improved attention mechanism

The three-dimensional Network-on-Chip (NoC) data acquisition system is designed to create a time-interleaved data acquisition system using NoC technology. In the design of NoC application systems, optimizing the mapping algorithm can effectively reduce network communication latency. Aiming at the mapping challenge of a large number of functional IP nodes in 3D NoC data acquisition system, the reinforcement learning and improved attention mechanism mapping algorithm (RA-Map) is proposed. The RA-Map mapping algorithm employs node function encoding and node position encoding to express the properties of an IP node in the task graph preprocessing. The local attention mechanism is used in the mapping network encoder, and the fusion of dynamic key node information is proposed in the decoder. The mapping result evaluation network achieves unsupervised training of the mapping network. These targeted improvements improve the quality of the mapping. Experimental results show that the RA-Map mapping algorithm can effectively model the IP core mapping. Compared with the DPSO algorithm and SA algorithm, the average communication cost of RA-Map mapping algorithm is reduced by 6.5 % and 8.5 %, respectively.

Performance and Communication Cost of Hardware Accelerators for Hashing in Post-Quantum Cryptography

SPHINCS+ is a signature scheme included in the first NIST post-quantum standard, that bases its security on the underlying hash primitive. As most of the runtime of SPHINCS+ is caused by the evaluation of several hash- and pseudo-random functions, offloading this computation to dedicated hardware accelerators is a natural step. In this work, we evaluate different architectures for hardware acceleration of such a hash primitive with respect to its use-case and evaluate them in the context of SPHINCS+. We attach hardware accelerators for different hash primitives (SHAKE128 and Ascon-Xof for both, full and round-reduced versions) to CPU interfaces having different transfer speeds. We show, that for most use-cases, data transfer determines the overall performance if accelerators are equipped with FIFOs and that reducing the number of rounds in the permutation does not necessarily lead to significant performance improvements when using hardware acceleration. This work extends on a conference paper accepted at COSADE’24, first published in [19], and written by the same authors, where different architectures for hardware accelerators of hash functions are benchmarked and evaluated for SPHINCS+ as a case study. In this paper, we provide results for additional parameter sets for SPHINCS+ and improve the performance of one of the accelerators by adding an additional RISC-V instruction for faster absorption. We then extend the performance benchmark by including the algorithms CRYSTALS-Kyber, CRYSTALS-Dilithium and Falcon. Finally we provide a power/energy comparison for the accelerators.

Digitalization of the national economy as a driver of virtualization of communications among dairy market stakeholders

Theoretical and methodological approaches to the study of key trends in the digitalisation of the national economy, which act as a driver of virtualisation of communications of dairy market stakeholders, have been studied and substantiated. It is established the different speed and slow implementation of digital accounting technologies in the conditions of differentiation of requests of interested organisations and customers, providing time saving, and communication costs are calculated. On the one hand, the involvement of local computer networks in the internal activities of national organisations, the internet – in the environment of virtual external interaction, rather with suppliers than with consumers, and e-mail, websites – in all spheres of their virtual activity is predominantly developed. On the other hand, there is a big lag in the implementation of such key elements of digital technologies as “big data”, “internet of things”, artificial intelligence models, radio-frequency identification, “digital twins”. The feasibility of creating a web showcase site for virtual interactions between innovatively active dairy enterprises and customers is substantiated.

Observer-based bipartite output containment control of heterogeneous multi-agent systems over structurally unbalanced signed graphs

In this paper, we propose state feedback and output feedback observer-based protocols that solve the bipartite output containment (BOC) problem for heterogeneous multi-agent systems over structurally unbalanced signed graphs. The proposed protocols rely solely on the exchange of output information among neighboring agents, resulting in a significant reduction or complete elimination of communication costs. Two design methods for determining controller parameters based on algebraic Riccati equations and linear matrix inequalities (LMIs) are proposed. Each method offers distinct advantages while ensuring that the closed-loop system poles are placed within a predefined region in the complex plane. Furthermore, an extensive solvability analysis is conducted, demonstrating that the solvability of the BOC problem using the proposed approach can be pre-guaranteed for a broad range of agent dynamics.

Design and Implementation of Lightweight Certificateless Secure Communication Scheme on Industrial NFV-Based IPv6 Virtual Networks

With the fast growth of the Industrial Internet of Everything (IIoE), computing and telecommunication industries all over the world are moving rapidly towards the IPv6 address architecture, which supports virtualization architectures such as Network Function Virtualization (NFV). NFV provides networking services like routing, security, storage, etc., through software-based virtual machines. As a result, NFV reduces equipment costs. Due to the increase in applications on Industrial Internet of Things (IoT)-based networks, security threats have also increased. The communication links between people and people or from one machine to another machine are insecure. Usually, critical data are exchanged over the IoE, so authentication and confidentiality are significant concerns. Asymmetric key cryptosystems increase computation and communication overheads. This paper proposes a lightweight and certificateless end-to-end secure communication scheme to provide security services against replay attacks, man-in-the-middle (MITM) attacks, and impersonation attacks with low computation and communication overheads. The system is implemented on Linux-based Lubuntu 20.04 virtual machines using Java programming connected to NFV-based large-scale hybrid IPv4-IPv6 virtual networks. Finally, we compare the performance of our proposed security scheme with existing schemes based on the computation and communication costs. In addition, we measure and analyze the performance of our proposed secure communication scheme over NFV-based virtualized networks with regard to several parameters like end-to-end delay and packet loss. The results of our comparison with existing security schemes show that our proposed security scheme reduces the computation cost by 38.87% and the communication cost by 26.08%.

Probability-based mapping approach for an application-aware networks-on-chip architectures

In a digital and automation era, on-chip multi-core architecture plays a vital role in effective communication in the field of very large-scale integrated circuits (VLSI). In this paper, we propose a unique mapping approach in which a probability-based core selection from the application benchmark into the center to eccentric way of placement of cores in the standard network architecture improves the performance of networks-on-chip (NoC). The proposed approach utilizes a structured mapping strategy, in contrast to the random mapping. This characteristic renders the proposed method a robust solution for a diverse range of NoC architectures irrespective of scale. The proposed approach provides better quality of service (QoS) with optimal total communication bandwidth and average hop count. The performance of the proposed mapping approach is validated with various experiments over standard and real-time benchmarks. The investigation results indicate that the total communication cost over real-time NoC benchmarks for the proposed mapping approach offers 43.06%, 22.75%, and 16.69% average improvement over CastNet, NMAP, and mapGtoM respectively. Furthermore, we adopt uniform geometric and shuffled traffic patterns to identify the latency and throughput of the proposed probability-based mapping approach. The investigation results indicate that the proposed mapping approach outperforms existing mapping procedures.

FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption

Unprotected gradient exchange in federated learning (FL) systems may lead to gradient leakage-related attacks. CKKS is a promising approximate homomorphic encryption scheme to protect gradients, owing to its unique capability of performing operations directly on ciphertexts. However, configuring CKKS security parameters involves a trade-off between correctness, efficiency, and security. An evaluation gap exists regarding how these parameters impact computational performance. Additionally, the maximum vector length that CKKS can once encrypt, recommended by Homomorphic Encryption Standardization, is 16384, hampers its widespread adoption in FL when encrypting layers with numerous neurons. To protect gradients’ privacy in FL systems while maintaining practical performance, we comprehensively analyze the influence of security parameters such as polynomial modulus degree and coefficient modulus on homomorphic operations. Derived from our evaluation findings, we provide a method for selecting the optimal multiplication depth while meeting operational requirements. Then, we introduce an adaptive segmented encryption method tailored for CKKS, circumventing its encryption length constraint and enhancing its processing ability to encrypt neural network models. Finally, we present FedSHE, a privacy-preserving and efficient Federated learning scheme with adaptive Segmented CKKS Homomorphic Encryption. FedSHE is implemented on top of the federated averaging (FedAvg) algorithm and is available at https://github.com/yooopan/FedSHE. Our evaluation results affirm the correctness and effectiveness of our proposed method, demonstrating that FedSHE outperforms existing homomorphic encryption-based federated learning research efforts in terms of model accuracy, computational efficiency, communication cost, and security level.

Federated learning for spiking neural networks by hint-layer knowledge distillation

In recent years, research on the federated Spiking Neural Network (SNN) has attracted increasing attention because of its advantages of low-power consumption and privacy security. However, existing federated SNN researches rely primarily on the Federated Average (FedAvg) strategy, transferring full network parameters between the server and clients and incurring substantial communication. To address this issue, we propose a Hint-layer Distillation-based Spiking Federated Learning (HDSFL) framework that reduces the communication cost by transferring knowledge and losslessly compressing the spiking tensor. To compensate for the information loss due to the binary representation of spikes in knowledge distillation, we introduce the hint-layer information instead of just soft-label distribution. To aggregate the knowledge effectively across clients on the server, we process the weighted knowledge aggregation on the spiking knowledge representation based on local performance. The experimental results on four classical and large-scale benchmarks show that our method reduces the communication cost by about 1–2 orders of magnitude compared to conventional methods, while achieving comparable accuracies. Especially on the CIFAR-10 dataset, our method achieves the same accuracy as FedAvg using only 20% of the communications, and consumes only 5.8% communications of FedAvg in a single round.

DNA TRIKKY Based Security Mechanism for Radio Frequency Identification Protocol

Radio frequency identification (RFID) technology has seen rapid expansion in recent years, used in a variety of applications like tracking, monitoring, ticketing, supply-chain management, contactless payments, etc. Several researchers have recommended elliptical curve cryptography (ECC) as a great cryptosystem to pair with RFID technology to increase security and privacy. However, RFID systems have major problems with insecure communication routes, which pose security concerns. In this research, a DNA TRIKKY based security mechanism has been proposed for RFID protocol to mutually authenticate the RFID tag and reader through insecure channels. The proposed solution uses a sponge structure and DNA-XOR to enhance the hash function generation process, preventing sensitive data from being leaked from the backend server. Informal security evaluations provide proof that the proposed method can withstand many attacks. According to performance evaluation, the proposed method performs better than the current protocols, making it ideal for use with RFID systems since it necessitates less processing time, communication costs, and storage costs particularly for the tag. According to the results, the proposed authentication paradigm works well with RFID tags that have limited resources. A comparison of the proposed protocol to other protocols shows that it performs more securely. Studies have found that this method outperforms other existing strategies.

A joint communication and computation design for semantic wireless communication with probability graph

In this paper, the problem of joint communication and computation design for probability graph-based semantic communication over wireless networks is investigated. In the considered model, the base station (BS) extracts the compressed small-sized semantic data by removing redundant information based on the shared knowledge base between the transceivers. In particular, the knowledge base is represented as a probability graph, which summarizes the statistic relations of massive knowledge graphs. On the user side, the compressed information is accurately inferred on the basis of the same probability graph as the BS. Although this approach brings additional computation resource consumption for semantic information extraction, it effectively reduces communication resource consumption through the transmission of small-sized data. Both the communication and computation cost models are derived based on the inference process of the probability graph. Based on the formulated models, the problem of joint communication and computation resource allocation is proposed to minimize the total energy consumption of the network considering both latency and power limitations. To solve this problem, the closed-form solution of the transmission power is obtained with fixed semantic compression ratio. Then, an effective linear search-based algorithm is proposed to obtain the optimal solution of the considered problem with low complexity. Simulation results demonstrate the effectiveness of the proposed system compared with the conventional non-semantic schemes.

Neuron grouping and mapping methods for 2D-mesh NoC-based DNN accelerators

Deep Neural Networks (DNNs) have gained widespread adoption in various fields; however, their computational cost is often prohibitively high due to the large number of layers and neurons communicating with each other. Furthermore, DNNs can consume a significant amount of energy due to the large volume of data movement and computation they require. To address these challenges, there is a need for new architectures to accelerate DNNs. In this paper, we propose novel neuron grouping and mapping methods for 2D-mesh Network-on-Chip (NoC)-based DNN accelerators considering both fully connected and partially connected DNN models. We present Integer Linear Programming (ILP) and simulated annealing (SA)-based neuron grouping solutions with the objective of minimizing the total volume of data communication among the neuron groups. After determining a suitable graph representation of the DNN, we also apply ILP and SA methods to map the neurons onto a 2D-mesh NoC fabric with the objective of minimizing the total communication cost of the system. We conducted several experiments on various benchmarks and DNN models with different pruning ratios and achieved an average of 40-50% improvement in communication cost.

Quantum‐safe Lattice‐based mutual authentication and key‐exchange scheme for the smart grid

AbstractThe smart grid network (SGN) is expected to leverage advances in the Internet of Things (IoT) to enable effective delivery and monitoring of energy. By integrating communication, computing, and information tools like smart sensors and meters to facilitate the process of monitoring, predictions, and management of power usage, the SGN can improve competence of power‐grid architecture. However, the effective deployment of IoT‐powered SGNs hinges on the deployment of strong security protocols. With the advent of quantum computers, classic cryptographic algorithms based on integer factorization and the Diffie‐Hellman assumptions may not be suitable to secure the sensitive data of SGNs. Therefore, in this paper, a secure quantum‐safe mutual authentication and key‐exchange (MAKe) mechanism is proposed for SGNs, that make use of the hard assumptions of small integer solution and inhomogeneous small integer solution problems of lattice. The proposed protocol is intended to offer confidentiality, anonymity, and hashed‐based mutual authentication with a key‐exchange agreement. Similarly, this scheme allows creation and validation of the mutual trust among the smart‐meters (SMs) and neighbourhood‐area network gateway over an insecure wireless channel. A random oracle model is then used to perform the formal security analysis of the proposed approach. A thorough formal analysis demonstrates proposed algorithm's ability to withstand various known attacks. The performance analysis shows that the proposed approach outperforms other comparative schemes with respect to at least 22.07% of minimal energy utilization, 51.48% effective storage and communications costs, as well as 76.28% computational costs, and thus suitable for resource‐constrained SGNs.

FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations

Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential privacy (DP) mechanisms or data heterogeneity, which can be attributed to the inconsistency between the local and global objectives. To address this issue, we propose FedLAP-DP, a novel privacy-preserving approach for FL. Our formulation involves clients synthesizing a small set of samples that approximate local loss landscapes by simulating the gradients of real images within a local region. Acting as loss surrogates, these synthetic samples are aggregated on the server side to uncover the global loss landscape and enable global optimization. Building upon these insights, we offer a new perspective to enforce record-level differential privacy in FL. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes while achieving an improved trade-off between privacy and utility. Extensive experiments validate the superiority of our approach across various datasets with highly skewed distributions in both DP and non-DP settings. Beyond the promising performance, our approach presents a faster convergence speed compared to typical gradient-sharing methods and opens up the possibility of trading communication costs for better performance by sending a larger set of synthetic images. The source is available at https://github.com/a514514772/FedLAP-DP.

AnoPay: Anonymous Payment for Vehicle Parking With Updatable Credential

Many existing anonymous parking payment schemes lack high efficiency and flexibility. For instance, the calculation and communication costs involved in payment may linearly increase with the payment amount. In this paper, we propose an anonymous payment system (dubbed AnoPay) for vehicle parking, which leverages updatable attribute-based anonymous credentials and efficient zero-knowledge proof (ZKP) to achieve user anonymity and constant overhead for parking fee payment. To further improve the efficiency, we design a secure parking fee aggregation protocol based on linear homomorphic encryption to aggregate parking transactions, where the amount of each parking transaction is hidden and the privacy of the parking lot in terms of its revenue is guaranteed. AnoPay achieves both unlinkability and accountability, malicious payments can be efficiently traced when it is necessary. We provide a security model and rigorous proof for each security property of AnoPay. Extensive experiments and comparisons demonstrate the efficiency and practicality of the system.

FlashSwift: A Configurable and More Efficient Range Proof With Transparent Setup

Bit-decomposition-based zero-knowledge range proofs in the discrete logarithm (DLOG) setting with a transparent setup, e.g., Bulletproof (IEEE S&P 18), Flashproof (ASIACRYPT 22), and SwiftRange (IEEE S&P 24), have garnered widespread popularity across various privacy-enhancing applications. These proofs aim to prove that a committed value falls within the non-negative range [0, 2^N-1] without revealing it, where N represents the bit length of the range. Despite their prevalence, the current implementations still suffer from suboptimal performance. Some exhibit reduced communication costs at the expense of increased computational costs while others experience the opposite. Presently, users are compelled to utilize these proofs in scenarios demanding stringent requirements for both communication and computation efficiency. In this paper, we introduce, FlashSwift, a stronger DLOG-based logarithmic-sized alternative. It stands out for its greater shortness and significantly enhanced computational efficiency compared with the cutting-edge logarithmic-sized ones for the most common ranges where N is no more than 64. It is developed by integrating the techniques from Flashproof and SwiftRange without using a trusted setup. The substantial efficiency gains stem from our dedicated efforts in overcoming the inherent incompatibility barrier between the two techniques. Specifically, when N=64, our proof achieves the same size as Bulletproof and exhibits 1.1 times communication efficiency of SwiftRange. More importantly, compared with the two, it achieves 2.3 times and 1.65 times proving efficiency, and 3.2 times and 1.7 times verification efficiency, respectively. At the time of writing, our proof also creates two new records of the smallest proof sizes, 289 bytes and 417 bytes, for 8-bit and 16-bit ranges among all the bit-decomposition-based ones without requiring trusted setups. Moreover, to the best of our knowledge, it is the first configurable range proof that is adaptable to various scenarios with different specifications, where the configurability allows to trade off communication efficiency for computational efficiency. In addition, we offer a bonus feature: FlashSwift supports the aggregation of multiple single proofs for efficiency improvement. Finally, we provide comprehensive performance benchmarks against the state-of-the-art ones to demonstrate its practicality.

디지털 대전환 시대의 합리적 가계통신비 산정 및 통신비 차이 비교분석

With the digital transformation, the market structure of the telecommunications market changes rapidly, and whether the current level of household telecommunications costs is appropriate is controversial in the process of implementing telecommunications policies. In the midst of this, as the arguments for each stakeholder on the components and levels of appropriate household communication costs differ, it is intensifying into social confrontation rather than rational problem solving. As the controversy over objective household communication costs grows, the need to quantitatively analyze household communication costs based on state-approved statistics is being emphasized. Therefore, this study aims to explore policy implications for reasonable household communication costs estimation by analyzing household communication costs in detail and accurately interpreting their meaning with state-approved statistical data published by the National Statistical Office. In response to changes in the digital environment, we plan to redefine the UN's COICOP system, which takes into account the increase in spending on platforms, content, and digital devices, in the calculation method of household communication expenses to suit Korea. In addition, in order to correct that existing household communication costs do not reflect the contents of device content, this study suggests the necessity of switching to the concept of “household digital cost” instead of “household communication cost” and estimates reasonable “household digital cost”.

Cost minimization approach with hybrid optimization based task scheduling in Geo-distributed cloud

ABSTRACT To manage the huge data, cloud computing necessitates a significant number of disc I/O, network bandwidth and CPU cycles. To handle the massive volumes of data, the programming paradigm known as data flow integrates the tasks to a graph structure. Task scheduling is one of the main areas of current study that primarily aims to allocate the tasks to resources. It is essential to organise the tasks in a way which saves time and makes the most use of available resources. In recent years, scholars have offered different job scheduling techniques. This work intends to introduce a novel cost minimisation for big data processing with respect to task allocation or scheduling. Initially, the big data is processed under Map Reduce framework. This allocation processes is taken as the optimisation issue, which is solved under the consideration of minimisation factors like execution cost, makespan, communication cost, and energy consumption and risk. For solving this issue, a new hybrid optimisation algorithm known as Hybrid bald eagle and Archimedes Optimisation algorithm (HBE-ArCA) is developed. The betterment of HBE-ArCA is proven regarding cost, makespan and so on.

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

VESecure: Verifiable authentication and efficient key exchange for secure intelligent transport systems deployment

The Intelligent Transportation Systems (ITS) is a leading-edge, developing idea that seeks to revolutionize how people and things move inside and outside cities. Internet of Vehicles (IoV) forms a networked environment that joins infrastructure, pedestrians, fog, cloud, and vehicles to develop ITS. The IoV has the potential to improve transportation systems significantly, but as it is networked and data-driven, it poses several security issues. Numerous solutions to these IoV issues have recently been put forth. However, significant computing overhead and security concerns afflict the majority of them. Moreover, malicious vehicles may be injected into the network to access or use unauthorized services. To improve the security of the IoV network, the Mayfly algorithm is used to optimize the private keys continuously. To address these difficulties, we propose a novel VESecure system that provides secure communication, mutual authentication, and key management between vehicles, roadside units (RSU), and cloud servers. The scheme undergoes extensive scrutiny for security and privacy using the Real-or-Random (ROR) oracle model, Tamarin, and Scyther tools, along with the informal security analysis. An Objective Modular Network Testbed in OMNet++ is used to simulate the scheme. We prove our scheme's efficiency by comparing it with other existing methods regarding communication and computation costs.

Distributed Single-Source Shortest Path with Only Local Relaxation

Finding the shortest path from a source vertex to any other vertices on a graph (single-source shortest path, SSSP) is used in a wide range of applications. With the rapid expansion of graph data volume, graphs are too large to be stored and processed in a standalone machine. Therefore, performing SSSP distributively in the computer clusters becomes an inevitable way. We found that the performance of existing distributed SSSP algorithms is limited by the communication cost between workers, which is caused by global relaxation. To eliminate the expensive communication cost, we propose an efficient distributed SSSP algorithm LR-SSSP that replaces global relaxation with local relaxation. Furthermore, we propose two optimizations, i.e., lazy synchronization and forward relaxation, to reduce invalid synchronization and communication. Our results show that LR-SSSP can achieve up to 6–20× speedup over the state-of-the-art Δ-stepping++ algorithm.