Improve Communication Efficiency Research Articles

Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years. However, cyberattack detection in smart grids now faces new challenges, including privacy preservation and decentralized power zones with strategic data owners. To address these technical bottlenecks, this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC, that enables Discrimination between power System disturbances and Cyberattacks. Specifically, we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data. Secondly, we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency. Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information. Furthermore, we find that the gradient quantization scheme utilized improves communication efficiency by 40% when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

Privacy-preserving federated learning on lattice quantization

Federated learning (FL) is an important approach to cooperate with multiple devices for learning without exchanging data between devices and central server. However, due to bandwidth and other reasons, the communication efficiency should be considered when the volume of information transmitted is limited. In this paper, we utilize the tool of lattice quantization form quantization theory and the variable intercommunication interval to improve communication efficiency. Meanwhile, to make strong privacy guarantee, we incorporate the notion of differential privacy (DP) to the FL framework with local SGD algorithm. By adding calibrated noises, we propose a universal lattice quantization for differentially private federated averaging algorithm (ULQ-DP-FedAvg). We provide tight privacy bound by using some privacy techniques. We also analyze the convergence bound of ULQ-DP-FedAvg based on bits rate constraints and the growing inter-communication interval as well as the data are non-independent identically distribution (Non-IID). It turns out that the algorithm converges and preserves that the privacy has scarcely influenced on the convergence rate. The effectiveness of our algorithm is demonstrated by synthetic and real datasets.

Interactive Digital Notice Boards for Universities in Ghana

Purpose: The purpose of this study was to design and implement an Interactive Digital Notice Board (IDNB) for universities to address the limitations of traditional notice boards and improve communication among students, faculty, and staff.
 Design/Methodology/Approach: The study employed a mixed-methods approach, combining quantitative data collection through surveys and qualitative data collection through interviews with key stakeholders. The data were analyzed using descriptive statistics and thematic analysis.
 Findings: The study found that the IDNB was an effective communication tool that enhanced information dissemination and facilitated interaction among students, faculty, and staff. The IDNB was also perceived as user-friendly and visually appealing.
 Research Limitation/ Implication: The study was limited to the CCTU campus, and the findings may not be generalizable to other contexts. The study also did not evaluate the long-term sustainability and cost-effectiveness of the IDNB.
 Practical Implications: The IDNB has practical implications for CCTU as it can improve communication efficiency and reduce paper wastage associated with traditional notice boards. The IDNB can also serve as a platform for targeted advertising, event promotion, and student engagement.
 Social Implications: The IDNB can enhance social interactions and create a sense of community among students, faculty, and staff. It can also promote inclusivity by providing access to information in multiple languages and formats, catering to the diverse student body at CCTU.
 Originality/Value/ Novelty: The IDNB is novel in its interactive and user-friendly design, which sets it apart from traditional notice boards. It also integrates multimedia features, such as videos and images, to enhance engagement and information retention. The IDNB can serve as a blueprint for other institutions looking to adopt innovative communication strategies.

LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization

Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The primary concerns in FedOpt can be attributed to the model divergence and communication efficiency, which significantly affect the performance. In this article, we propose a new method, i.e., LoSAC, to learn from heterogeneous distributed data more efficiently. Its key algorithmic insight is to locally update the estimate for the global full gradient after each regular local model update. Thus, LoSAC can keep clients’ information refreshed in a more compact way. In particular, we have studied the convergence result for LoSAC. Besides, the bonus of LoSAC is the ability to defend the information leakage from the recent technique Deep Leakage Gradients (DLG). Finally, experiments have verified the superiority of LoSAC comparing with state-of-the-art FedOpt algorithms. Specifically, LoSAC significantly improves communication efficiency by more than 100% on average, mitigates the model divergence problem, and equips with the defense ability against DLG.

Ekart - Smart Shopping PushCart

In newfangled wen, snap up at large emporium is a quotidian divertissement. On commemoration and on induct salvage days, one can descry a facund fleet at emporium. kins chaffer profuse chattels, hauls them onto gigs, and then prosecute to the ackers register to compel reckoning. They obliged to halt in procession in the interim that time for their chattels to be scrutinize to wield a RFID reader and to receive a bill a barcode scrutinizer are wield in edict . To transposed that, patrons must profuse while booty in arcade. When put in a smart snap up e-kart, every totality commodities has an RFID tag adhere, which can be instantaneously peruse by a gig with an RFID reader so that exhibiting can be crisp from the snap up gig itself.. kins can circumvent endurance in procession at the appraise in antithetical manner. Extra smart shelving that is RFID reader equipped and can oversee the repertory as well as refurbished the central server, and it can also cognizant of the stature of the commodity in the store, can be append. Because all the merchandise can be instantaneously scrutinize by an RFID scrutinizer somewhat than had operated scrutinize by labor, catalogue admin also flatter much unadorned. The current method of shopping is time-consuming and involves many steps, such as selecting the goods, standing in line, scanning them, and checking out. This is a time-consuming procedure, and IOT can be used to speed it up and address some of its problems. The EKART is the answer to the aforementioned issue; this cart is fitted with sophisticated microcontrollers and sensors that shorten the billing process by scanning the items instantly as soon as they are added to the cart, which also ends lines in shopping malls. Every cart could be fitted with inexpensive sensors and microcontrollers, and a security scanner would be installed at the exit to sound an alarm if money had not been received. By doing this, the market's recent challenges would be resolved. A digital shelf would be one of this system's applications. This logically results in the following advantages. The artufacts accomodate in a smart snap up gig (which has RFID reading capabilities) can be hurriedly scrutinize, and the smart gig can also procure the exhibhiting information. kins no prolonged obliged to wait in extensive procession at the bursar. All replete commodity can be oversee by the smart shelves with RFID scrutinizer, so that can also be televise server-side commodity statures amend. The server can alert staff to restock when an object runs out. As all items can be automatically read and readily logged, inventory management for the shop is made simple. wherefore we conclude, that a circumspect smart snap up system that compel the employ of RFID techno. Our study is a trailblazer in the creation of a smart snap up gadget because it is anticipated that future stores will be outfitted with RFID technology. The present system can be improved, for instance, by lowering the computational overhead at the smart cart side for greater efficiency, or by figuring out how to improve communication efficiency.

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.

Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.

FedCos: A Scene-Adaptive Enhancement for Federated Learning

As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensively studied. However, the heterogeneity of client data severely degrades the performance of FL compared with that in centralized training. It causes the locally trained models of clients to move in different directions. On the one hand, it slows down or even stalls the global updates, leading to inefficient communication. On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance. Fortunately, these shortcomings can be mitigated by reducing the angle between the directions that local models move in. Based on this fact, we propose FedCos, which reduces the directional inconsistency of local models by introducing a cosine-similarity penalty. It promotes the local model iterations towards an auxiliary global direction. Moreover, our approach is auto-adapt to various non-IID settings without an elaborate selection of hyperparameters. The experimental results show that FedCos outperforms the well-known baselines and can enhance them under a variety of FL scenes, including varying degrees of data heterogeneity, different number of participants, and cross-silo and cross-device settings. Besides, FedCos improves communication efficiency by 2 to 5 times. With the help of FedCos, multiple FL methods require significantly fewer communication rounds than before to obtain a model with comparable performance.

The Improvement of Federated Learning in communication efficiency

In recent years, the need to protect user privacy and data security as well as multi-party data cooperation has become increasingly strong. Privacy computing is considered a fundamental solution that is growing rapidly and gaining unprecedented popularity. Currently, federated learning is the most popular product in privacy computing. With its lightweight technology route and deployment, it has become a mainstream solution and product of choice for many privacy computing applications, such as providing advanced medical image collection segmentation technology in the medical field to protect patient privacy, where it can play a unique role. But Federated learning faces a need to improve communication efficiency. This paper introduces FedAvg, the most basic algorithm for federated learning, including its basic process, advantages and disadvantages. We analyze algorithms that optimize federated learning by adding local computation, Communication Compression (including model Compression, Periodic Averaging and Quantization Compression, and gradient Compression), computation and communication overlap, and compare the strengths and weaknesses of representative algorithms for each approach. At the end of this paper, the summary and prospect are made.

Automated non-branching protrusion design with evolutionary algorithm in communication via diffusion

Molecular communication via diffusion enables information transmission between nanomachines in nanonetworking. Although the interconnection of multiple bodies expands their capabilities to a certain extent, it still poses some challenges to improve communication efficiency. The degradation in the molecular reception performance of the receiver over long channels is the one that can be mitigated through availability of protrusions. In this paper, we propose special version of protrusion location optimization extended with non-branching protrusions which consist of multiple interdependent segments. For the given problem, we present novel evolutionary optimization algorithm (i.e. GADP: Genetic Algorithm Distributed Protrusions) to maximize the probability of molecule hit as an objective. The motivation is to utilize the evolving and dynamic nature of evolutionary optimization to handle different scenarios including varying distances between the transmitter and the receiver. The proposed algorithm is subjected to extensive experimental study where it outperforms the existing algorithms from the literature in majority of the cases. Furthermore, we analyse the final versions of protrusions at the end of evolution to reveal their structural and locational properties.

Toward Intelligent and Efficient 6G Networks: JCSC Enabled On-Purpose Machine Communications

Driven by the vision of "intelligent connection of everything" towards 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches. In this article, we propose an on-purpose machine communication framework enabled by joint communication, sensing and computation (JCSC) technology, which employs machine semantics as the interactive information flow. Naturally, there are potential technical barriers to be solved before the widespread adoption of on-purpose communications, including the conception of machine purpose, fast and concise networking strategy and semantics-aware information exchange mechanism during the process of task-oriented cooperation. Hence, we discuss enabling technologies complemented by a range of open challenges. Simulation result shows that the proposed framework can significantly reduce networking overhead and improve communication efficiency.

Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios.

Coalition Game Based Distributed Clustering Approach for Group Oriented Unmanned Aerial Vehicle Networks

Clustering is an effective solution to improve the management efficiency of large-scale systems. One one hand, UAVs performing the same task have a similar moving tendency. One the other hand, the network topology of the UAV swarms is dynamically changing. Considering the above two aspects, our main contributrons are designing a group-oriented distributed clustering algorithm based on the coalition game that couples task attributes and communication attributes. The clustering goal is to divide UAVs performing the same task into one cluster and make each cluster have more UAVs under the cluster size limits, thus improving communication efficiency. The proposed algorithm comprehensively considers the task group information and communication link stability as the coalition value. UAVs decide whether to leave their current coalition based on coalition values. Through periodic parallel switch operations selection, UAVs are divided into the desired clustering structure. Simulations verify that our clustering algorithm is effective and better than the existing ones, especially in communication link stability, cluster numbers, and load balance.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network.

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Energy-Efficient Multi-UAVs Cooperative Trajectory Optimization for Communication Coverage: An MADRL Approach

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%.

Multi-Disciplinary Design and Implementation of a Mass Vaccination Clinic Mobile Application to Support Decision-Making.

(1) To develop a mobile application (app) that can address the information needs of vaccination clinic stakeholders for end-of-day doses decision-making in mass immunization settings; and (2) to understand usability and clinical implementation among multi-disciplinary users. Contextual inquiry guided 71.5 hours of observations to inform design characteristics. Rapid iterative testing and evaluation were performed to validate and improve the design. Usability and integration were evaluated through observations, interviews, and the system usability scale. Designing the app required consolidating contextual factors to support information and workload needs. Twenty-four participants used the app at four clinics who reported its effectiveness in reducing stress and improving communication efficiency and satisfaction. They also discussed positive workflow changes and design recommendations to improve its usefulness. The average system usability score was 87 (n = 22). There is significant potential for mobile apps to improve workflow efficiencies for information sharing and decision-making in vaccination clinics when designed for established cultures and usability, thereby providing frontline workers with greater time to focus on patient care and immunization needs. However, designing and implementing digital systems for dynamic settings is challenging when healthcare teams constantly adapt to evolving complexities. System-level barriers to adoption require further investigation. Future research should explore the implementation of the app within global contexts.

SEMANTIC-PRAGMATIC CATEGORY OF POLITENESS IN THE DISCOURSE PRACTICE OF INTERVIEW MEDIA TEXTS

The article is devoted to the study of the semantic-pragmatic specifics and the role of the category of politeness in the German printed media discourse. The relevance of this work is due to the lack of knowledge of the language means of representing the category of politeness of the speech behavior of communicants, which in their totality contribute to increasing the effectiveness of interpersonal media communication and preventing not only conflict, but also awkward situations in relation to dialogic media texts in the perspective of their discursive practice. Also, the significance of this study is due to the appeal to the study of politeness in the dialogical texts of magazine interviews that are actively published in the media, which contribute to the rapid andsuccessful transmission of timely information, improve communication efficiency and achieve the desired pragmatic impact on addressees. Special attention is paid to culturally specific parameters, which are based on the cultural values of Germany and its national and cultural characteristics, in particular, the attitude towards communication. Сommunicants, and above all, journalists, choose the appropriate and, if possible, the most appropriate language means in a particular situation of speech communication. Undoubted markers of positive politeness in the discursive practice of media texts of German interviews are a polite address to the interviewee (Herr, Frau), polite third person pronoun Sie, conjunctive, modal particles (mal, nur, fast, vielleicht, gern), verb danken, combination besten (vielen) Dank, nouns Verzeihung, Entschuldigung.

UbiNN: A Communication Efficient Framework for Distributed Machine Learning in Edge Computing

Deployment of distributed machine learning at the edge is conducive to reducing latency and protecting privacy associated with transmitting data back to the cloud. Nonetheless, as machine learning models scale, bandwidth resources are limited and heterogeneity in data collection from heterogeneous edge devices is visible, resulting in low model accuracy and high communication overhead. In this paper, a novel distributed deep learning framework called ubiquitous neural network (UbiNN) is proposed to improve communication efficiency without affecting the accuracy of the local neural network model at the edge or the global neural network model in the cloud. As for the accuracy of the neural network model, a common dataset with a small portion of insensitive data is constructed for training the neural network model, and its accuracy is enhanced by a new algorithm based on knowledge distillation and covariance computation (KDCC). Experimental results demonstrate that the test accuracy of UbiNN is extremely close to the centralized machine learning and non-federated learning scheme DDNN and up to 18.74% better than that of other classic federated learning schemes when using public datasets such as MNIST, CIFAR-10, and REUTERS-21578. Meanwhile, communication overheads are substantially reduced in terms of data transmission volume and latency.

Robust Hierarchical Federated Learning with Anomaly Detection in Cloud-Edge-End Cooperation Networks

Federated learning (FL) enables devices to collaborate on machine learning (ML) model training with distributed data while preserving privacy. However, the traditional FL is inefficient and costly in cloud–edge–end cooperation networks since the adopted classical client-server communication framework fails to consider the real network structure. Moreover, malicious attackers and malfunctioning clients may be implied in all participators to exert adverse impacts as abnormal behaviours on the FL process. To address the above challenges, we leverage cloud–edge–end cooperation to propose a robust hierarchical federated learning (R-HFL) framework to enhance inherent system resistance to abnormal behaviours while improving communication efficiency in practical networks and keeping the advantages of the traditional FL. Specifically, we introduce a hierarchical cloud–edge–end collaboration-based FL framework to reduce communication costs. For the framework, we design a detection mechanism as partial cosine similarity (PCS) to filter adverse clients to improve performance, where the proposed lightweight technique has high computation parallelization. Besides, we theoretically discuss the influence of the proposed PCS on the convergence and stabilization of FL. Finally, the experimental results show that the proposed R-HFL always outperforms baselines in general cases under malicious attacks, which further shows the effectiveness of our scheme.