Communication Cost Research Articles

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification. We establish rigorous convergence guarantees of FA-HMC on non-iid distributed datasets, under the strong convexity and Hessian smoothness assumptions. Our analysis investigates the effects of parameter space dimension, noise on gradients and momentum, and the frequency of communication (between the central node and local nodes) on the convergence and communication costs of FA-HMC. Beyond that, we establish the tightness of our analysis by showing that the convergence rate cannot be improved even for continuous FA-HMC process. Moreover, extensive empirical studies demonstrate that FA-HMC outperforms the existing Federated Averaging-Langevin Monte Carlo (FA-LD) algorithm. Supplementary materials for this article are available online.

An efficient federated learning solution for the artificial intelligence of things

Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.

Impact assessment of blockchain-based improved PBFT consensus mechanism for improving supply chain efficiency and security

In modern supply chain management, it is crucial to be able to track the flow of goods in real-time and maintain data integrity. Research focuses on the transparency, security, and efficiency improvements brought about by blockchain technology, and how these improvements can help address information asymmetry issues in supply chain management and improve overall operational efficiency. At the same time, the study explored the working principle of the practical Byzantine fault-tolerant consensus mechanism, proposed relevant improvement measures, and analyzed how these improvements can be effectively applied in the scenario of supply chain information sharing to improve the stability and efficiency of the overall system. The research results show that the overall latency of improved PBFT is maintained at the millisecond level, while the latency of traditional PBFT can reach several seconds or exceed 10 seconds. In terms of CPU resource consumption, the demand for improving PBFT only shows linear growth, while the demand for traditional PBFT increases sharply. In addition, improving PBFT also has significant advantages in communication costs. In an 8-node network, the communication volume of improved PBFT is only 11.27 KB, while traditional PBFT is 43.67 KB. In terms of throughput and latency performance, the improved PBFT can handle over 16000 requests per second in a 16 node network, significantly better than the traditional PBFT’s approximately 8000 requests. Meanwhile, at 64 nodes, the improved version has a latency of 301.7 milliseconds, much lower than the traditional version’s 510.2 milliseconds. These improvements demonstrate the advantages of improving PBFT in enhancing supply chain transparency, security, and efficiency, providing effective technical support for the digitization and intelligence of supply chain management. We hope that research can promote the digitization and intelligence of supply chain management, and facilitate the efficient operation and optimization of the supply chain.

A lightweight and secure authentication protocol for visually impaired and handicapped people in the telehealth system

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact.

Security protocol for securing notifications about dangerous events in the agglomeration

Our everyday lives cannot function without intelligent devices, which create the so-called Internet of Things networks. Internet of Things devices have various sensors and software to manage the work environment and perform specific tasks without human intervention. Internet of Things networks require appropriate security at various levels of their operation. In this article, we present a new security protocol that protects communication in IoT networks and enables interconnected devices to communicate and exchange information to increase the security of people living in urban agglomerations. The Control Station device evaluates the collected data about events that may threaten the life or health of residents and then notifies the Emergency Notification Center about it. The protocol guarantees the security of devices and transmitted data. We verified this using automatic verification technology, formal verification using Burrows, Abadi and Needham logic and informal analysis. The proposed protocol ensures mutual authentication, anonymity and revocation. Also, it is resistant to Man-in-the-middle, modification, replay and impersonation attacks. Compared to other protocols, our solution uses simple cryptographic techniques that are lightweight, stable and do not cause problems related to high communication costs. It does not require specialist equipment, so we can implement it using typical hardware. At each stage of protocol execution, communication occurs between two entities, so it does not require interaction between different entities, which may limit its adaptability in the context of interoperability.

Fog-assisted de-duplicated data exchange in distributed edge computing networks

The Internet of Things (IoT) generates substantial data through sensors for diverse applications, such as healthcare services. This article addresses the challenge of efficiently utilizing resources in resource-scarce IoT-enabled sensors to enhance data collection, transmission, and storage. Redundant data transmission from sensors covering overlapping areas incurs additional communication and storage costs. Existing schemes, namely Asymmetric Extremum (AE) and Rapid Asymmetric Maximum (RAM), employ fixed and variable-sized windows during chunking. However, these schemes face issues while selecting the index value to decide the variable window size, which may remain zero or very low, resulting in poor deduplication. This article resolves this issue in the proposed Controlled Cut-point Identification Algorithm (CCIA), designed to restrict the variable-sized window to a certain threshold. The index value for deciding the threshold will always be larger than the half size of the fixed window. It helps to find more duplicates, but the upper limit offset is also applied to avoid the unnecessarily large-sized window, which may cause extensive computation costs. The extensive simulations are performed by deploying Windows Communication Foundation services in the Azure cloud. The results demonstrate the superiority of CCIA in various metrics, including chunk number, average chunk size, minimum and maximum chunk number, variable chunking size, and probability of failure for cut point identification. In comparison to its competitors, RAM and AE, CCIA exhibits better performance across key parameters. Specifically, CCIA outperforms in total number of chunks (6.81%, 14.17%), average number of chunks (4.39%, 18.45%), and minimum chunk size (153%, 190%). These results highlight the effectiveness of CCIA in optimizing data transmission and storage within IoT systems, showcasing its potential for improved resource utilization and reduced operational costs.

Entropy production in communication channels.

In many complex systems, whether biological or artificial, the thermodynamic costs of communication among their components are large. These systems also tend to split information transmitted between any two components across multiple channels. A common hypothesis is that such inverse multiplexing strategies reduce total thermodynamic costs. So far, however, there have been no physics-based results supporting this hypothesis. This gap existed partially because we have lacked a theoretical framework that addresses the interplay of thermodynamics and information in off-equilibrium systems. Here we present the first study that rigorously combines such a framework, stochastic thermodynamics, with Shannon information theory. We develop a minimal model that captures the fundamental features common to a wide variety of communication systems, and study the relationship between the entropy production of the communication process and the channel capacity, the canonical measure of the communication capability of a channel. In contrast to what is assumed in previous works not based on first principles, we show that the entropy production is not always a convex and monotonically increasing function of the channel capacity. However, those two properties are recovered for sufficiently high channel capacity. These results clarify when and how to split a single communication stream across multiple channels.

The impact of indirect structure on functional connectivity in schizophrenia using a multiplex brain network

It is known that abnormal functional connectivity (FC) in schizophrenia (SZ) is closely related to structural connectivity (SC). We speculate that indirect SC also have an impact on FC in SZ patients. Conventional single-layer network has limitations for studying the relationship between indirect SC and FC. Thus, this study constructed a multiplex network based on structural connectivity and functional connectivity (SC-FC). The SC-FC bandwidth and SC-FC cost are used to analyze the impact of indirect SC on FC. Moreover, this paper proposed mediation ability, mediation cost, mediated strength and mediated cost to quantify the effects of mediator nodes and mediated nodes on indirect SC. The results show that SZ patients exhibit lower SC-FC bandwidth and SC-FC cost compared to healthy controls (HC), which could be caused by the limbic and subcortical network (LSN), default mode network (DMN) and visual network (VN). The mediator and mediated nodes in indirect SC of SZ patients also showed diminished effects. These findings suggest that functional communication ability and cost in SZ patients are influenced by indirect SC. This study provides new perspectives for understanding the relationship between indirect SC and FC, and provides strong evidence for interpreting the physiological mechanisms of SZ patients.

SparseComm: An Efficient Sparse Communication Framework for Vehicle-Infrastructure Cooperative 3D Detection

Collaborative perception, aiming at achieving a comprehensive perception range through inter-agent communication, faces challenges such as high communication costs and domain gaps between multiple agents. This paper introduces SparseComm, an innovative sparse communication collaborative perception framework designed to mitigate these challenges. SparseComm efficiently operates in sparse feature spaces and aggregates features related to the same objects by a sparse instance communication module. Meanwhile, a sparse 3D cooperation module is incorporated to enhance 3D feature representation during communication, thus improving detection performance. Furthermore, a bounding box restoration module is designed to recover undetected bounding boxes due to feature fusion and to address the quality drop issue caused by domain gaps at minimal additional communication cost. Extensive experiments conducted on the DAIR-V2X and V2XSet demonstrate the efficacy of SparseComm, achieving 47.12% at 211.46 communication bytes on DAIR-V2X and 78.03% at 216.63 communication bytes on V2XSet. Notably, SparseComm reduces communication consumption from 10× to 1000× compared with the prior methods while maintaining the detection performance.

An efficient certificateless blockchain-enabled authentication scheme to secure producer mobility in named data networks

Named Data Networking (NDN) aims to establish an efficient content delivery architecture. In NDN, secure and effective identity authentication schemes ensure secure communication between producers and routers. Currently, there is no feasible solution to perform identity authentication of mobile producers in NDNs. Identity authentication schemes in other networks are either weak in security or performance, such as privacy leakage, difficulty to establish cross-domain trust, and long handover delays, and are not fully adaptable to the security requirements of NDNs. Additionally, the mobility of producers was not fully considered in the initial design of NDNs. This paper first revises the structure of packets and routers to support the identity authentication and mobility of producers. On this basis, this paper proposes a secure and efficient certificateless ECC-based producer identity authentication scheme (CL-BPA), which includes initial authentication and re-authentication, aimed at achieving rapid switch authentication and integrating blockchain to solve single-point failure issues. Using the Canetti and Krawczyk (CK) adversarial model and informal security analysis, the proposed CL-BPA scheme is demonstrated to be resistant to anonymity attacks, identity forgery attacks, and man-in-the-middle attacks. The performance analysis demonstrates that the proposed CL-BPA scheme exhibits excellent capabilities in terms of computation delay, communication cost, smart contract execution time, average response delay, and throughput.

Digital Divide and Classifying Social Class in Rural Development: Voices from the Secondary Data of Field

This paper aims to investigate the digital divide and to classify social class as a significant indicator in the context of rural development. The study employed secondary data analysis derived from the Data Desa Presisi (DDP). The researcher meticulously analyzed and reinterpreted statistical data to identify new potential avenues for development planning, with a particular focus on Neglasari Village. The findings indicate that the predominant social class in the research area consists largely of lower-class individuals or casual laborers, who are categorized as impoverished in terms of rural development. This categorization aligns with the conclusion that poverty, as revealed through social class mapping, contributes to the digital divide, which is predominantly influenced by the upper social class. Consequently, this dynamic tends to create a segmentation of development information at the initial level. Conversely, it is noteworthy that the lower class exhibits the highest purchasing power for communication expenses, raising questions about the effectiveness and utility of network access and communication costs. This study serves as a valuable resource for village development planners, emphasizing the importance of considering the dynamics of social class, the implications of the digital divide, and the necessity for evaluative and accountable development monitoring models in the distribution of targeted programs.

Resource prioritization and allocation in fog computing using hybrid optimization

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs.

Using Focus Groups to Evaluate a Self-Assessment Approach to Coproduction Value Creation in an International Community of Practice: Phase 2 of the CO-VALUE Study.

Coproduction learning health system models clearly define the use of clinical and patient-reported data for system learning and quality improvement, but less is known about how to document formative learning about coproduction value creation over the course of a quality improvement initiative. The authors aimed to 1) assess the feasibility, utility, and acceptability of novel self-assessment tools for coproduction value creation and 2) identify domains of coproduction value creation. The authors conducted 4 focus groups with quality improvement teams from 4 health systems in the United States and Sweden between June 2021 and September 2023. A single analyst coded transcripts and proposed themes, with investigator triangulation validating results. Participants found the self-assessment tools acceptable and useful. The improvement passport was seen as more feasible for routine use than the full self-assessment guide. Peer learning within the community of practice, diverse multidisciplinary improvement teams, and leadership support facilitated teams' work. Domains of coproduction value creation included communication, self-efficacy, interconnectedness, direct and indirect costs of health care utilization, health professional experience, and access to the right care. Peer learning and camaraderie within the community of practice maintained momentum among participating teams during a challenging time of limited resources and mounting responsibilities in health care settings, suggesting enhanced resiliency through approaching difficult tasks in community. The authors identified themes of coproduction value creation and drivers of engagement. Future research will draw on the measurement domains established in this study to inform the development of measures of coproduction value creation. Those measures could then be incorporated into the data-rich environments of coproduction learning health systems to enhance focus on value from service user and professional perspectives.

Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation

Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is to minimize team communication costs by selecting agents with the required skills. However, finding an optimal team is a computationally hard problem. This study introduces a novel hybrid approach called I-PSO-Jaya (improved PSO-Jaya, which combines PSO (Particle Swarm Optimization) and the Jaya algorithm with the Modified Swap Operator to form efficient teams. A potential application scenario of the algorithm is to build a team of engineers for an IT project. The implementation results show that our approach gives an improvement of 73% in the Academia dataset and 92% in the ACM dataset compared to existing methods.

Zero gradient sum algorithm of arbitrary initial value with constraints and communication delay based on directed graph

The distributed convex optimisation issue with inequality constraints and box constraints on the directed communication topology is examined in this paper. In the case of the communication delay between agents, we propose a piecewise zero-gradient-sum triggered control algorithm that allows arbitrary initial values. Using the log-barrier penalty method, we only need to study an unconstrained approximation problem of the original problem. Firstly, each agent's state converges to the optimal value of the related local objective function in a fixed time. Simultaneously, the upper bound of fixed time in this paper is smaller than some existing results. Then, in the case of communication delay, time-triggered and event-triggered methods with non-uniform sampling interval are proposed in this paper, which make the communication cost lower and the application broader. Using the Lyapunov function method, the sufficient conditions for each agent's state to converge to the optimal point are given, and the Zeno behaviour is excluded. Finally, to confirm the algorithm's effectiveness, we provide two numerical examples and one machine learning example as demonstrations.

Distributed event-triggered algorithm for convex optimization with coupled constraints

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of O(1/k) for general convex objective functions, where k>0 is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

A different base approach for better efficiency on range proofs

Zero-knowledge range proofs (ZKRPs) are commonly used to prove the validation of a secret integer lies in an interval to some other party in a secret way. In many ZKRPs, the secret is represented in binary and then committed via a suitable commitment scheme or represented as an appropriate encryption scheme. This paper is an extended version of the conference paper presented at the 14th IEEE International Conference on Security of Information and Networks. To this end, after summarizing the conference paper, we first analyze the proof proposed by Mao in 1998 in the elliptic-curve setting. Mao’s proof contains a bit commitment scheme with an OR construction as a sub-protocol. We have extended Mao’s range proof to base-u with a modified OR-proof. We investigate and compare the efficiency of different base approaches on Mao’s range proof with both Pedersen commitment and ElGamal encryption. Later, we analyze the range proof proposed by Bootle et al. in both finite fields and elliptic-curve settings. This proof contains polynomial commitment with matrix row operations. We take the number of computations in modulo exponentiation and the cost of the number of exchanged integers between parties. Then, we generalize these costs for u-based construction. We show that compared with the base-2 representation, different base approach provides efficiency in communication cost or computation cost, or both.

Language Barriers in Multinationals and Knowledge Transfers

Abstract We study communication frictions within multinationals (MNCs), hypothesizing that language barriers reduce management knowledge transfers within the organization. A distinct feature of such MNCs is a three-tier hierarchy: foreign managers (FMs) supervise domestic managers (DMs) who supervise production workers. Tailored surveys from our setting – MNCs in Myanmar – reveal that language barriers impede interactions between FMs and DMs. A first experimental protocol offers DMs free English courses and confirms that lowering communications costs increases their interactions with FMs. A second experimental protocol that asks human-resource managers at domestic firms to rate hypothetical resumes reveals that multinational experience and, specifically, DM-FM interactions are valued in the domestic labor market. Together, these results suggest that reducing language barriers can improve transfers of management knowledge, an interpretation supported by improvements in soft skills among treatment DMs in the first experiment. A model in which communication within MNCs is non-contractible – a realistic feature of workplace life – reveals that the experimental results are consistent with underinvestment in language training and provide a rationale for policy intervention.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.

A PUF secured lightweight mutual authentication protocol for multi-UAV networks

Unmanned aerial vehicles, initially developed for military use, have evolved to play vital roles in civilian applications including photography, agriculture, disaster management, and delivery services. Their agility, precision, and ad-hoc formation make them indispensable, particularly in time-sensitive tasks such as search-and-rescue missions. However, the widespread use of UAVs has raised security concerns, including unauthorized access, cyberattacks, and physical threats. In addition, the dynamic nature of these networks provides adversaries with opportunities to exploit node failures leading to potential data breaches. To address these risks, implementing robust security measures such as authentication, encryption, physical security, and proactive monitoring is essential even amidst the inherent resource limitations faced by UAVs. This paper proposes a lightweight authentication and key agreement protocol for multi-UAV networks, incorporating physically unclonable technology for securing the data sent over the network. The protocol also addresses security risks during UAV failures and the unauthorized access to data. The scheme has been validated using the Scyther simulation tool, with the PUF implemented on the Xilinx FPGA platform. An informal security analysis is also presented that demonstrates its adherence to security requirements. Additionally, the performance of the proposed scheme is compared with state-of-the-art approaches by evaluating network latency in terms of computational and communication costs, affirming its effectiveness in resource-constrained applications.