Improve Communication Efficiency Research Articles

A Study on Utilizing Social Media Advertising through “Instagram Aesthetics” to Drive Purchase Intention

Due to various factors, the number of start-up firms is constantly increasing. Social media is vital for promoting businesses, especially in this age. It helps companies to improve communication efficiency, foster creativity, and provide helpful information, among other things. Social media advertising is known for its low costs; hence it enables start-ups to launch various promotions that increase purchase intent. This study aims to know if several advertising factors, including uniqueness, clarity, and interactivity, influence a customer’s purchase intention and understand a significant relationship between these. Thus, a survey will be used through Google Forms to gather data. Indeed, online businesses continuously grow, especially as this becomes the source of income for some, unleashes one's passion towards businesses, provides more accessibility and convenience, and many more reasons, particularly during the pandemic. Using creative materials in digital marketing would help enterprises raise their brand awareness, increase overall sales, and expand businesses. Instagram Aesthetics significantly affects the purchase intention of consumers. The uniqueness of these outputs is the most important since it portrays not just originality but be able to gain a competitive advantage in the market, which reflects the operation of the business. However, it is also essential to have interactivity and clarity. The study would be important mainly for business owners, specifically those who operate solely on Instagram, to understand how creativity in advertising would help them market their business online. Moreover, this would be a powerful aid in attracting their target market and potential customers through their artistic visuals.

Efficient and Anonymous Authentication With Succinct Multi-Subscription Credential in SAGVN

In this paper, we propose an efficient and anonymous authentication protocol with a succinct multi-subscription credential (AnMsc) in Space-air-ground integrated vehicular networks (SAGVN). First, we adopt a subscription-based service model in SAGVN. Specifically, vehicular users (VEs) can subscribe to network services and conduct direct mutual authentication with subscribed access points (APs) to avoid message exchanges with VEs’ home network. Early application data can also be transmitted with authentication messages to improve communication efficiency. Second, we carefully tailor the design of the redactable signature and propose an efficient credential management mechanism in SAGVN. Multiple service subscriptions can be embedded into a succinct (constant-size) credential. With the credential, VEs can anonymously access any subscribed AP without revealing other subscription information. Thorough security analysis and comprehensive performance evaluation demonstrate that AnMsc can guarantee key-exchange security, VE anonymity, and service fairness while ensuring credential management and authentication efficiency.

Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks' rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This paper introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$ selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$ largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods' effectiveness.

Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the channel and data distortion induced by channel fading and data-aware scheduling, which introduces significant bias and harms the training performance. To solve these problems, we propose a dynamic data and channel adaptive sensor scheduling and power control algorithm combining a residual feedback mechanism. Instead of discarding the gradients not transmitted to the central server, each sensor keeps track of a local residual to store these gradients. Furthermore, by connecting the model update iterations to a dynamic evolution process, we utilize the Lyapunov drift optimization method to analyze the relationship between the training gain and resource allocation. The derived decentralized optimal solution is adaptive to both the channel state information and data importance to seize good transmission opportunity and important gradients. Theoretical analysis is provided on the convergence of the proposed algorithm in practical training scenarios. Simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and lower training loss compared to existing baselines.

Decentralized Federated Learning: Balancing Communication and Computing Costs

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. The convergence rate of DFL can be optimized to achieve the balance of communication and computing costs under constrained resources. For improving communication efficiency of DFL, compressed communication is further introduced to the proposed DFL as a new scheme, named DFL with compressed communication (C-DFL). The proposed C-DFL exhibits linear convergence for strongly convex objectives. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods and show that C-DFL further enhances communication efficiency.

Digital Transformation for Efficient Communication in the Workplace: Analyzing the Flow Coworking Tool

Objectives: The coronavirus disease (COVID-19) pandemic has necessitated new ways of conducting business, including hybrid work, which requires the active digital transformation of workplaces. This study examined cases in which the coworking tool Flow was adopted and explained Flow’s characteristics and user experiences. Methods: Flow is a coworking tool developed by Madras Check to improve communication efficiency between workers. This case report was based on interviews and surveys of Flow users. Results: Flow was designed to increase productivity related to tasks and efficient management. According to client interviews, surveys, and our case analysis, Flow appears to achieve its purpose. In a survey of 1,429 respondents, Flow was found to reduce the amount of time spent checking messages via email and messenger applications, and respondents felt that Flow was a user-friendly tool and were willing to recommend Flow to others. Conclusions: The present case report shows how the Flow coworking tool contributed to fostering a digital work environment. Our analysis of the benefits and user experiences of Flow indicates that such coworking tools are necessary for digital transformation so that organizations can create a hybrid work environment, which is the primary work format following the COVID-19 pandemic.

Security-Aware and Efficient Data Deduplication for Edge-Assisted Cloud Storage Systems

Data deduplication at the network edge significantly improves communication efficiency in edge-assisted cloud storage systems. With the increasing concern about data privacy, secure deduplication has been proposed to provide data security while supporting deduplication. Since conventional secure deduplication schemes are mainly based on deterministic encryption, they are vulnerable to frequency analysis attacks. Some recent research has focused on this problem, where several works studied the trade-off between deduplication efficiency and resistance to frequency analysis attacks. However, no existing work can provide different deduplication efficiency and protection for different data chunks. In this paper, we propose a security-aware and efficient data deduplication scheme for edge-assisted cloud storage systems. It not only improves the efficiency of deduplication but also reduces information leakage caused by frequency analysis attacks. In particular, we first define the security level for chunks to measure the security needs of users. Then, we develop an encryption scheme with multiple levels of security for deduplication. It provides higher security protection for chunks with higher security levels, while sacrificing security to achieve higher deduplication efficiency for chunks with lower security levels. We also analyze the security of our proposed scheme. Evaluations on the real-world datasets show the efficiency of our design.

How to Improve Communication Efficiency in the Presence of Political Correctness Concerns?

How to Improve Communication Efficiency in the Presence of Political Correctness Concerns?

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input. Under such a platoon model, the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader, while attaining improved communication efficiency. Toward this aim, a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed. One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status. Then, a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived. Finally, simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency.

Towards Communication-Efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which significantly promotes the development of Industrial 4.0. However, FEL faces two critical challenges: communication overhead and data privacy. FEL suffers from expensive communication overhead when training large-scale multi-node models. Furthermore, due to the vulnerability of FEL to gradient leakage and label-flipping attacks, the training process of the global model is easily compromised by adversaries. To address these challenges, we propose a communication-efficient and privacy-enhanced asynchronous FEL framework for edge computing in IIoT. First, we introduce an asynchronous model update scheme to reduce the computation time that edge nodes wait for global model aggregation. Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes. Third, we design a cloud-side malicious node detection mechanism to detect malicious nodes by testing the local model quality. Such a mechanism can avoid malicious nodes participating in training to mitigate label-flipping attacks. Extensive experimental studies on two real-world datasets demonstrate that the proposed framework can not only improve communication efficiency but also mitigate malicious attacks while its accuracy is comparable to traditional FEL frameworks.

A new fog-based routing strategy (FBRS) for vehicular ad-hoc networks

Vehicular ad-hoc network (VANET) plays a significant role in future intelligent transportation systems. The main objective of vehicular ad hoc networks (VANETs) is to improve driver safety and traffic efficiency. Many researchers proposed different schemes to improve communication efficiency. It is quite challenging where vehicles’ speed, Direction, and density of neighbors on the move are not consistent. Although several routing protocols have been introduced to manage data exchange among vehicles in VANETS, they still suffer from many drawbacks such as lost packets or time penalties. This paper introduced a new Fog Based Routing Strategy, which constructs a reliable system of adaptive, stable, and efficient routing networks. FBRS consists of two main phases: System Setup Phase (SSP) and System Operation Phase (SOP). SSP creates a cluster network, collects its nodes’ data, mining routes between them, and ranking paths using Dijkstra’s algorithm into a simplified table. Although, SOP generates a reliable route between the request of any two nodes for a communication channel and maintains the route against any simultaneous crashes. Recent VANET routing protocols have been compared against FBRS. Experimental results have proven the outperforming of the proposed algorithm against recent routing protocols in terms of packet delivery ratio and routing overhead.

Improving Efficiency of Primary Care Provider Communication for Uncomplicated Admissions.

Communication between inpatient pediatric hospital medicine (HM) and primary care providers (PCPs) is important for quality care. As provider workload increases, it is important to focus on a means to improve communication efficiency. Our goal was to increase the percentage of HM admissions using 1-way communication from 0% to 35% over a 16-month period. HM providers and PCPs collaborated to identify 12 admission diagnoses for which 1-way communication could be used. Using quality improvement methods, we studied the implementation of "Leave a Message" (LAM) calls for 1-way communication and providing PCPs with the option to place a return call. Control charts were used to track LAM call use and balancing measures of PCP return phone calls, additional PCP communications, and 7-day readmissions over time. A total of 778 LAM calls were placed by HM providers over 16 months. The percentage of LAM calls out of all PCP calls placed ranged from 0% to 35% during this time, increasing significantly during winter months and before the coronavirus disease 2019 pandemic. Only 0.4% (n = 3) of LAM calls were returned by PCPs. Estimated PCP return phone calls were reduced by 11.1 calls per week. We created a system for 1-way telephone communication between HM providers and PCPs for common, simple admissions and reduced the need for PCP return phone calls. The low percentage of LAM calls returned by PCPs may suggest that 1-way communication is adequate for most simple admissions.

QMCR: A Q-learning-based multi-hop cooperative routing protocol for underwater acoustic sensor networks

Routing plays a critical role in data transmission for underwater acoustic sensor networks (UWSNs) in the internet of underwater things (IoUT). Traditional routing methods suffer from high end-toend delay, limited bandwidth, and high energy consumption. With the development of artificial intelligence and machine learning algorithms, many researchers apply these new methods to improve the quality of routing. In this paper, we propose a Q-learning-based multi-hop cooperative routing protocol (QMCR) for UWSNs. Our protocol can automatically choose nodes with the maximum Q-value as forwarders based on distance information. Moreover, we combine cooperative communications with Q-learning algorithm to reduce network energy consumption and improve communication efficiency. Experimental results show that the running time of the QMCR is less than one-tenth of that of the artificial fish-swarm algorithm (AFSA), while the routing energy consumption is kept at the same level. Due to the extremely fast speed of the algorithm, the QMCR is a promising method of routing design for UWSNs, especially for the case that it suffers from the extreme dynamic underwater acoustic channels in the real ocean environment.

From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems

Existing communication systems are mainly built based on Shannon's information theory, which deliberately ignores the semantic aspects of communication. The recent iteration of wireless technology, 5G and beyond, promises to support a plethora of services enabled by carefully tailored network capabilities based on contents, requirements, as well as semantics. This has sparked significant interest in semantic communication, a novel paradigm that involves the meaning of messages in communication. In this article, we first review classic semantic communication frameworks and then summarize key challenges that hinder its popularity. We observe that some semantic communication processes such as semantic detection, knowledge modeling, and coordination can be resource-consuming and inefficient, especially for communication between a single source and a destination. We therefore propose a novel architecture based on federated edge intelligence for supporting resource-efficient semantic-aware networking. Our architecture allows each user to offload computationally intensive semantic encoding and decoding tasks to edge servers and protect its proprietary model-re-lated information by coordinating via intermediate results. Our simulation result shows that the proposed architecture can reduce resource consumption and significantly improve communication efficiency.

SpaceDML: Enabling Distributed Machine Learning in Space Information Networks

Space information networks (SINs) have become a rapidly growing global infrastructure service. Massive volumes of high-resolution images and videos captured by low-orbit satellites and unmanned aerial vehicles have provided a rich training data source for machine learning applications. However, SIN devices' limited communication and computation resources make it challenging to perform machine learning efficiently with a swarm of SIN devices. In this article, we propose Spacedml, a distributed machine learning system for SIN platforms that applies dynamic model compression techniques to adapt distributed machine learning training to SINs' limited bandwidth and unstable connectivity. Spaced-ml has two key algorithm:s adaptive loss-aware quantization, which compresses models without sacrificing their quality, and partial weight averaging, which selectively averages active clients' partial model updates. These algorithms jointly improve communication efficiency and enhance the scalability of distributed machine learning with SIN devices. We evaluate Spacedml by training a LeNet-S model on the MNIST dataset. The experimental results show that Spacedml can increase model accuracy by 2-3 percent and reduce communication bandwidth consumption by up to 60 percent compared to the baseline algorithm.

Differentially Private and Communication Efficient Collaborative Learning

Collaborative learning has received huge interests due to its capability of exploiting the collective computing power of the wireless edge devices. However, during the learning process, model updates using local private samples and large-scale parameter exchanges among agents impose severe privacy concerns and communication bottleneck. In this paper, to address these problems, we propose two differentially private (DP) and communication efficient algorithms, called Q-DPSGD-1 and Q-DPSGD-2. In Q-DPSGD-1, each agent first performs local model updates by a DP gradient descent method to provide the DP guarantee and then quantizes the local model before transmitting it to neighbors to improve communication efficiency. In Q-DPSGD-2, each agent injects discrete Gaussian noise to enforce DP guarantee after first quantizing the local model. Moreover, we track the privacy loss of both approaches under the Renyi DP and provide convergence analysis for both convex and non-convex loss functions. The proposed methods are evaluated in extensive experiments on real-world datasets and the empirical results validate our theoretical findings.

Gradient-Free Scheduling of Fog Computation for Marine Data Feedback

With the development of the marine Internet of Things (IoT), more sensors are deployed for different marine applications. Abundant data are generated that are required by terrestrial users. However, nowadays, the marine Internet cannot provide enough communication resources and high service quality for the feedback of those data. Thereby, fog computation is a potential method to solve this problem via data compression before transmission. To improve transmission efficiency while guaranteeing Quality of Service (QoS) of applications, how to efficiently schedule limited computation resources at fog nodes, such as buoys for different applications should be addressed. This article proposes an online computation scheduling algorithm to improve the communication resource efficiency with guaranteed QoS via appropriate compression. This scheme needs neither the cost function nor the constraint function at the scheduling node. What is more, the gradient information, which is usually needed in learning strategies, is not required, which is the estimation based on first-order and second-order schemes, respectively. Results show that the proposed scheme achieves the goal of improving communication efficiency while guaranteeing the data quality constraint of different applications.

Experimental study on correlation between amplitude and phase of electromagnetic wave affected by time-varying plasma by amplitude-modulated radio frequency plasma generator

The time-varying plasma channel can increase the bit error rate of the phase modulation signal. The essential reason is that there is a correlation between the amplitude variation and phase variation caused by time-varying plasma. It can cause constellation rotation, and traditional communication methods cannot deal with this special interference. In this paper, the relationship between the amplitude and phase of electromagnetic wave affected by time-varying plasma is investigated. The correlation coefficient is derived. The result shows that the close correlation exists and the signs of correlation coefficients are different under different parameters. A verification experiment is also designed, and the time-varying transmission coefficients, constellations of QPSK signals, and correlation coefficients are measured under different conditions. The experimental results and the theoretical results are in good agreement. The conclusions can provide prior knowledge for communication methods and contribute to improving communication efficiency.

Event-triggered [formula omitted] control for networked spar-type floating production platforms with active tuned heave plate mechanisms and deception attacks

The service life of floating production platforms can be substantially shortened due to undesirable excessive vibrations caused by dynamic loads. It is thus necessary to develop effective vibration reduction methods for floating production platforms. This article proposes a network-based active control approach for a spar-type floating production platform (SP) against wave exciting loads and deception attacks. First, a novel active tuned heave plate (ATHP) mechanism based on the concept of the active tuned mass damper is developed for the SP. Second, in the context of networked control of the SP-ATHP system, an event-triggering transmission mechanism is introduced to significantly improve communication efficiency. Meanwhile, a Bernoulli distribution and a nonlinear function are employed to character possible deception attacks in shared communication channels. Then, by modelling the network-based closed-loop SP-ATHP system as a time-delay stochastic system, its stability and H∞ performance analysis is derived. Besides, some criteria are obtained to co-design the triggering mechanism and the H∞ controller. Finally, simulation studies demonstrate that compared with some existing heave plate mechanisms, the designed event-triggered H∞ controllers under this ATHP mechanism are more effective to suppress heave motions of the platform and save network resources and control expenditure. Furthermore, even though there are deception attacks, the proposed scheme can still guarantee satisfactory system performance.

High-Performance Secure Access and Data Privacy Protection Method for 5G Border Routing Gateway

With the substantial increase in the amount of data information and the development of 5G technology, boundary computing technology based on 5G, as a real-time and efficient data processing method, has broad application prospects. The aggregation gateway further aggregates the power injection data of all electric energy storage units and sends it to the power company. The power company uses its own private key to access the aggregated data. Row decryption to obtain the total power data recovered in each time slot. The paper proposes a new solution using Lagrangian interpolation to ensure data privacy and security. The security shows that the algorithm can not only protect the privacy of the electric energy storage unit, but also realize the confidentiality and integrity of the injected data. Efficiency analysis shows that this algorithm can significantly reduce computational complexity and improve communication efficiency.