Limited Network Bandwidth Research Articles

Cluster Synchronization Control for Discrete-Time Complex Dynamical Networks: When Data Transmission Meets Constrained Bit Rate.

In this article, the cluster synchronization control problem is studied for discrete-time complex dynamical networks when the data transmission is subject to constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth, and the effects from the constrained bit rate onto the control performance of the cluster synchronization are evaluated. A sufficient condition is first proposed to guarantee the ultimate boundedness of the error dynamics of the cluster synchronization, and then, a bit-rate condition is established to reveal the fundamental relationship between the bit rate and the certain performance index of the cluster synchronization. Subsequently, two optimization problems are formulated to design the desired synchronization controllers with aim to achieve two distinct synchronization performance indices. The codesign issue for the bit-rate allocation protocol and the controller gains is further discussed to reduce the conservatism by locally minimizing a certain asymptotic upper bound of the synchronization error dynamics. Finally, three illustrative simulation examples are utilized to validate the feasibility and effectiveness of the developed synchronization control scheme.

IncEFL: A sharing incentive mechanism for edge-assisted federated learning in industrial IoT

As the information sensing and processing capabilities of IoT devices increase, a large amount of data is being generated at the edge of Industrial IoT (IIoT), which has become a strong foundation for distributed Artificial Intelligence (AI) applications. However, most users are reluctant to disclose their data due to network bandwidth limitations, device energy consumption, and privacy requirements. To address this issue, this paper introduces an Edge-assisted Federated Learning (EFL) framework, along with an incentive mechanism for lightweight industrial data sharing. In order to reduce the information asymmetry between data owners and users, an EFL model-sharing incentive mechanism based on contract theory is designed. In addition, a weight dispersion evaluation scheme based on Wasserstein distance is proposed. This study models an optimization problem of node selection and sharing incentives to maximize the EFL model consumers' profit and ensure the quality of training services. An incentive-based EFL algorithm with individual rationality and incentive compatibility constraints is proposed. Finally, the experimental results verify the effectiveness of the proposed scheme in terms of positive incentives for contract design and performance analysis of EFL systems.

METHOD OF LOCAL RESTORATION OF SEGMENTS OF INFORMATION SIGNALS

The use of centralized and decentralized electricity supply schemes, the introduction of feedback between the consumer of electricity and its producer led to a rapid increase in information flows at all stages of the functioning of the electricity industry of Ukraine, starting with the production of electricity and before its consumption. This necessitates the processing, storage and transmission of large data sets. On the other hand, the time constraints imposed on information processing and decision-making efficiency, as well as the limited bandwidth of communication networks require effective optimization of information flows in terms of their compression and compact storage, transmission and recovery without loss. At the same time, the determining quantitative and qualitative information parameters are electricity consumption modes and the quality of electric energy, and the main mode parameter for solving many problems of planning, management and carrying out commercial calculations is the graphs of electric load. Based on the analysis of the functional relationships of wavelet coefficients according to the levels of wavelet decomposition, the robot presents a modified reconstruction scheme and developed a model based on which a method of local (segmental) restoration of information signals according to the levels of wavelet detail is synthesized using the example of the electric load graph. In the paper, a model of local recovery of time segments of information signals is proposed and a sequential algorithm of actions synthesized on its basis. This technique allows for effective segmentation of information signals, reduces the duration of mathematical processing, simplifies database analysis, increases the effectiveness of controlling the reliability of initial data recovery by reserving recovery paths with subsequent comparison of results.

Event-triggered predefined-time H∞ formation control for multiple underactuated surface vessels with error constraints and input quantization

In this paper, an event-triggered predefined-time H∞ formation control strategy is proposed for multiple underactuated surface vessels (USVs) with error constraints and input quantization. A modified asymmetric barrier Lyapunov function (MABLF) is employed to constrain the tracking errors to a predefined range. Both event-triggered and input quantization are simultaneously employed to guarantee that the controller achieves satisfactory performance even with a limited network bandwidth. H∞ control is introduced to ensure that the system L2 gain is less than or equal to a given ς, which effectively improves the robustness to the lumped disturbance. Theorem analysis shows that the proposed controller guarantees that the tracking errors of the system converge to an arbitrarily small region in a predefined time. The effectiveness of the proposed controller is confirmed by simulation examples and comparisons.

Enabling Edge-Cloud Video Analytics for Robotics Applications

Emerging deep learning-based video analytics tasks demand computation-intensive neural networks and powerful computing resources on the cloud to achieve high accuracy. Due to the latency requirement and limited network bandwidth, edge-cloud systems adaptively compress the data to strike a balance between overall analytics accuracy and bandwidth consumption. However, the degraded data leads to another issue of poor tail accuracy, which means the extremely low accuracy of a few semantic classes and video frames. Autonomous robotics applications especially value the tail accuracy performance but suffer using the prior edge-cloud systems. We present Runespoor, an edge-cloud video analytics system to manage the tail accuracy and enable emerging robotics applications. We train and deploy a super-resolution model tailored for the tail accuracy of analytics tasks on the server to significantly improves the performance on hard-to-detect classes and sophisticated frames. During online operation, we use an adaptive data rate controller to further improve the tail performance by instantly adjusting the data rate policy according to the video content. Our evaluation shows that Runespoor improves class-wise tail accuracy by up to 300%, frame-wise 90%/99% tail accuracy by up to 22%/54%, and greatly improves the overall accuracy and bandwidth trade-off.

Adaptive Event-Triggered Sliding-Mode Control for Consensus Tracking of Nonlinear Multiagent Systems With Unknown Perturbations.

The adaptive tracking control problem of leader-following nonlinear multiagent systems (MASs) subject to unknown perturbations and limited network bandwidth is investigated by the robust adaptive event-triggered sliding-mode control method. A distributed integral sliding mode is established to realize the finite-time reachability of the states of the leader-following nonlinear MAS. An adaptive triggering control mechanism is then put forward to dynamically adjust the triggering interval, thus reducing the actuator wear and unnecessary network resource consumption. The positions and velocities of the leader-following nonlinear MAS subject to unknown external disturbances are, respectively, driven to the equilibrium point by constructing a distributed event-based robust adaptive sliding-mode protocol. Via the Lyapunov stability theory and Barbalat lemma, sufficient conditions to ensure the adaptive tracking performance are derived for leader-following nonlinear MASs. Three simulation examples to verify the efficacy of the proposed event-based robust adaptive sliding-mode controller design are presented.

Multisensory 360 Videos Under Varying Resolution Levels Enhance Presence.

Omnidirectional videos have become a leading multimedia format for Virtual Reality applications. While live 360 videos offer a unique immersive experience, streaming of omnidirectional content at high resolutions is not always feasible in bandwidth-limited networks. While in the case of flat videos, scaling to lower resolutions works well, 360 video quality is seriously degraded because of the viewing distances involved in head-mounted displays. Hence, in this paper, we investigate first how quality degradation impacts the sense of presence in immersive Virtual Reality applications. Then, we are pushing the boundaries of 360 technology through the enhancement with multisensory stimuli. 48 participants experimented both 360 scenarios (with and without multisensory content), while they were divided randomly between four conditions characterised by different encoding qualities (HD, FullHD, 2.5K, 4K). The results showed that presence is not mediated by streaming at a higher bitrate. The trend we identified revealed however that presence is positively and significantly impacted by the enhancement with multisensory content. This shows that multisensory technology is crucial in creating more immersive experiences.

Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

Home Surveillance and Alert System using Raspberry Pi Zero W and GSM Modem with MQTT Protocol

Today’s modern era has made it essential for everyone, even for the privacy of their homes, to have controllable, affordable, and easy-to-use security systems. The creation of a cost-effective, straightforward, and feature-rich home security alarm system using a Raspberry Pi Zero W microprocessor and MQTT protocol is discussed in this paper. The proposed system is primarily intended for outlying areas with limited or insufficient network bandwidths. Together with sensors such a PIR (passive infrared sensor), sound sensor, gas leakage sensor, obstacle/proximity sensor (vibration sensor), fire/flame sensor, GSM module, and a surveillance pi camera, it makes use of a Raspberry Pi Zero W as a microprocessor. [1]. In addition, the system will promptly alert the homeowner through phone calls, SMS, or mail anytime it senses the presence of an intruder or other safety hazards. Hence, anyone who wants to make their space (bank, home, workplace, jewelry shops, and cabins) safe and to protect themselves from theft and infiltration can use this system at a fair price.

Quality of Service (QoS) Performance Analysis in a Traffic Engineering Model for Next-Generation Wireless Sensor Networks

Quality of Service (QoS) refers to techniques that function on a network to dependably execute high-priority applications and traffic reliably run high-priority applications and traffic even when the network’s capacity is limited. It is expected that data transmission over next-generation WSNs (Wireless Sensor Networks) 5G (5th generation) and beyond will increase significantly, especially for multimedia content such as video. Installing multiple IoT (Internet of Things refers to the network of devices that are all connected to each other) nodes on top of 5G networks makes the design more challenging. Maintaining a minimal level of service quality becomes more challenging as data volume and network density rise. QoS is critical in modern networks because it ensures critical performance metrics and improves end-user experience. Every client attempts to fulfill QoS access needs by selecting the optimal access device(s). Controllers will then identify optimum routes to meet clients’ core QoS needs in their core network. QoS-aware delivery is one of the most important aspects of wireless communications. Various models are proposed in the literature; however, an adaptive buffer size according to service type, priority, and incoming communication requests is required to ensure QoS-aware wireless communication. This article offers a hybrid end-to-end QoS delivery method involving customers and controllers and proposes a QoS-aware service delivery model for various types of communication with an adaptive buffer size according to the priority of the incoming service requests. For this purpose, this paper evaluates various QoS delivery models devised for service delivery in real time over IP networks. Multiple vulnerabilities are outlined that weaken QoS delivery in different models. Performance optimization is needed to ensure QoS delivery in next-generation WSN networks. This paper addresses the shortcomings of the existing service delivery models for real-time communication. An efficient queuing mechanism is adopted that assigns priorities based on input data type and queue length. This queuing mechanism ensures QoS efficiency in limited bandwidth networks and real-time traffic. The model reduces the over-provisioning of resources, delay, and packet loss ratio. The paper contributes a symmetrically-designed traffic engineering model for QoS-ensured service delivery for next-generation WSNs. A dynamic queuing mechanism that assigns priorities based on input data type and queue length is proposed to ensure QoS for wireless next-generation networks. The proposed queuing mechanism discusses topological symmetry to ensure QoS efficiency in limited bandwidth networks with real-time communication. The experimental results describe that the proposed model reduces the over-provisioning of resources, delay, and packet loss ratio.

Observer-based event-triggered non-PDC control for networked T-S fuzzy systems under actuator failures and aperiodic DoS attacks

This article addresses the observer-based event-triggered non-parallel distribution compensation (PDC) H∞ control issue for networked T-S fuzzy systems (TSFSs) under actuator failures and aperiodic denial-of-service (DoS) attacks. In order to alleviate the interference of network transmission, a fuzzy observer following the asynchronous premise variables as the system is constructed, and we design an observer-based switched fuzzy controller through a non-PDC scheme. And an event-triggered scheme (ETS) is designed to save the limited network bandwidth. Then, based on switching strategy and quantifying the DoS attacks signal indexes, the networked TSFSs can be converted into a new switched augmented system. Moreover, in virtue of piecewise Lyapunov-Krasovskii functional (LKF) method, we propose sufficient conditions to guarantee that the networked TSFS is exponential stability (ES) with a desired H∞ performance. Further, a collaborative design project is presented to obtain triggering parameters, observer gains, and controller gains, and we derive the quantitative relationship between sampling period, triggering parameters, system parameters, actuator fault matrix, and DoS attacks signal indexes. At last, two practical examples are employed to certify the effectiveness and superiority of the presented techniques.

Event-triggered distributed online convex optimization with delayed bandit feedback

This paper is concerned with an online distributed convex-constrained optimization problem over a multi-agent network, where the limited network bandwidth and potential feedback delay caused by network communication are considered. To cope with the limited network bandwidth, an event-triggered communication scheme is introduced in information exchange. Then, based on the delayed (i.e., single-point and two-point) bandit feedback, two event-triggered distributed online convex optimization algorithms are developed by utilizing the Bregman divergence in the projection step. Meanwhile, the convergence of the two developed algorithms is analyzed according to the provided static regret bounds achieved by the algorithm. The obtained results show that a sublinear static regret with respect to the time horizon T can be ensured if the triggering threshold gradually approaches zero. In this case, the corresponding order of the regret bounds is also determined by choosing suitable triggering thresholds. Finally, a distributed online regularized linear regression problem is provided as an example to illustrate the effectiveness of the proposed two algorithms.

Long-Term Matching Optimization With Federated Neural Temporal Difference Learning in Mobility-on-Demand Systems

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k$ </tex-math></inline-formula> NN) bipartite graph where each order is connected with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.

Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments

With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV services. To overcome these problems, we design a cloud-edge cooperative content-delivery strategy in asymmetrical IoV environments to minimize network latency by providing optimal computing, caching and communication resource allocation. We abstract the joint allocation issue of heterogeneous resources as a queuing theory-based latency minimization objective. Next, a new deep reinforcement learning (DRL) scheme works in each network node to achieve optimal content caching and request routing on the basis of the perceptive request history and network state. Extensive simulations show that our proposed strategy has lower network latency compared with the current solutions in the cloud-edge collaboration system and converges fast under different scenarios.

Compressed Deepfake Detection using Spatio-Temporal Approach with Model Pruning

Deepfake is a deep learning technology that replaces source person face photos with the target person face photos in a movie to create a video of the target by executing the actions performed by source person. Due to the limited storage capacity and network bandwidth constraints, compressed media is now commonly employed in social networks. The goal of this research study is to determine whether or not a given compressed video is a deepfake. To do this, both the spatial and temporal components of the video should be considered, and the findings will be integrated by using an appropriate fusion approach. Hence, the deep learning model used in the spatial approach is pruned using the Network Pruning technique to achieve a better performance. The combined prediction of the spatial and temporal approaches indicates whether the given video is deepfake or not.

A Super-Resolution Flexible Video Coding Solution for Improving Live Streaming Quality

In the context of the latest growing popularity of live video streaming, ensuring high video quality has become one of the most significant challenges faced by all live streaming platforms. Insufficient uplink bandwidth is an important factor that influences these live video transmissions, affecting their bitrate and latency and consequently the associated video streaming quality. This paper proposes a novel flexible super-resolution-based video coding and uploading framework (FlexSRVC) that improves the quality of live video streaming in limited uplink network bandwidth conditions. FlexSRVC includes a flexible video coding scheme, which compresses high-resolution key and non-key video frames to a lower bitrate in order to reduce the upload delay. A new flexible bitrate adaptation algorithm is also proposed to select dynamically the number of frames to be compressed and the compression ratio by jointly considering uplink network conditions and available cloud computing resources. Trace-driven emulations demonstrate that FlexSRVC provides the same quality while reducing up to 25% of the required bandwidth compared to the original encoding method (H.264). FlexSRVC improves users' QoE by at least 50% compared to a super resolution-based method which employs reconstruction of all video frames in uplink bandwidth constrained conditions.

ANALYSIS OF POTENTIAL RISKS IN IMPLEMENTING A SMART CITY DEVELOPMENT PROJECT

The potential risks associated with the implementation of a smart city development project, utilizing integrated digital technologies and data processing, are analyzed in the article. The main problematic aspects identified include technical difficulties, cybersecurity issues, legislative instability in the field of smart technologies, and insufficient integration between different systems and platforms. The reliability of smart city systems in emergencies is also discussed in the article, where serious consequences for citizens and city infrastructure could result from inadequate response. Additionally, challenges related to the dependence of smart cities on information technologies are highlighted, addressing potential network bandwidth limitations and the need for solutions to ensure the efficiency and availability of services. Ethical aspects are recognized as an important factor in the development and implementation of smart cities, particularly in establishing ethical standards for data usage and interaction with the public. The concept of a smart city index to assess the level of intelligence and resilience of cities in Ukraine is proposed in the article. It is comprised of two evaluation stages: digitization and conceptualization. The second stage measures citizens' quality of life, relations with authorities, environmental impact, and the city's role in the global city system. The analysis of recent publications in the field of smart technologies is also incorporated in the article, unresolved issues are identified, and the need for further research and legislative improvement is emphasized. The complexity of tasks related to the implementation of the smart city concept is highlighted, and the necessity of a systematic risk management approach to ensure sustainable and secure urban development is underscored.

Heterogeneous Network Access and Fusion in Smart Factory: A Survey

With the continuous expansion of the Industrial Internet of Things (IIoT) and the increasing connectivity among the various intelligent devices or systems, the control of access and fusion in smart factory networks has significantly gained importance. However, the contradiction between the high Quality of Service (QoS) requirements of massive data and the limited network bandwidth and the heterogeneous network is becoming deeper and deeper. The heterogeneity of smart factory networks brings many challenges to unified access and fusion, real-time transmission, and centralized control and management. This article provides a survey on heterogeneous networks in smart factories. We first study and discuss the heterogeneity of smart factory networks, and then discuss the existing mainstream wired and wireless network technologies, as well as promising future technologies, including 5G, OLE for Process Control Unified Architecture (OPC UA), and Time-Sensitive Networking (TSN). In addition, we also analyze current heterogeneous network fusion architecture and discuss the enabling technologies of heterogeneous network fusion in view of the shortcoming of the current solutions. Finally, we conclude with a discussion of open challenges and future research directions towards the effective realization of the smart factory.

Medical Image Compression Based on Variational Autoencoder

With the rapid growth of medical image data, it has become a current research hotspot that how to realize the large amounts of the real-time upload and storage of medical images with limited network bandwidth and storage space. However, currently, medical image compression technology cannot perform joint optimization of rate (the degree of compression) and distortion (reconstruction effect). Therefore, this study proposed a medical image compression algorithm based on a variational autoencoder. This algorithm takes rate and distortion as the common optimization goal and uses the residual network module to directly transmit information, which alleviates the contradiction between improving the degree of compression and optimizing the reconstruction effect. At the same time, the algorithm also reduces image loss in the medical image compression process by adding the residual network. The experimental results show that, compared with the traditional medical image compression algorithm and the deep learning compression algorithm, the algorithm in this study has smaller distortion, better reconstruction effect, and can obtain higher quality medical images at the same compression rate.

Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming Over HTTP (DASH)

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.