Improve Bandwidth Utilization Research Articles

Mentor: A Memory-Efficient Sparse-dense Matrix Multiplication Accelerator Based on Column-Wise Product

Sparse-dense matrix multiplication (SpMM) is the performance bottleneck of many high-performance and deep-learning applications, making it attractive to design specialized SpMM hardware accelerators. Unfortunately, existing hardware solutions do not take full advantage of data reuse opportunities of the input and output matrices or suffer from irregular memory access patterns. Their strategies increase the off-chip memory traffic and bandwidth pressure, leaving much room for improvement. We present Mentor , a new approach to designing SpMM accelerators. Our key insight is that column-wise dataflow, while rarely exploited in prior works, can address these issues in SpMM computations. Mentor is a software-hardware co-design approach for leveraging column-wise dataflow to improve data reuse and regular memory accesses of SpMM. On the software level, Mentor incorporates a novel streaming construction scheme to preprocess the input matrix for enabling a streaming access pattern. On the hardware level, it employs a fully pipelined design to unlock the potential of column-wise dataflow further. The design of Mentor is underpinned by a carefully designed analytical model to find the tradeoff between performance and hardware resources. We have implemented an FPGA prototype of Mentor . Experimental results show that Mentor achieves speedup by geomean 2.05× (up to 3.98×), reduces the memory traffic by geomean 2.92× (up to 4.93×), and improves bandwidth utilization by geomean 1.38× (up to 2.89×), compared with the state-of-the-art hardware solutions.

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

ABSTRACTThe demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

A novel adaptive event‐triggered security consensus control mechanism for leader‐following multi‐agent systems under hybrid random cyber attacks

AbstractAiming at the security consensus control problem of leader‐following multi‐agent systems (MASs) under hybrid random cyber attack, this article proposes a novel sampled information related adaptive event‐triggered control mechanism (SIRAETCM). While ensuring the safety performance of the MASs, the mechanism adaptively and dynamically adjusts the trigger threshold of every agent to achieve discontinuous communication by using only the current and latest sampled signals. According to the MASs communication mode and Bernoulli attack model, a security consensus control protocol is constructed, and a bilateral sampled‐interval Lyapunov functional (BSILF) method is introduced to obtain more sampling interval information and establish sufficient conditions for the leader‐following state error system to stabilize asymptotically under hybrid random cyber attacks. Meanwhile, under a large sampling interval, the controller gain and adaptive event‐triggered parameters are designed and obtained. The simulation of the tunnel diode circuit system shows that the SIRAETCM can reduce the number of communications between agents to improve bandwidth utilization, and the adopted safety cooperative control protocol can improve the safety and effectiveness of the MASs.

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

Service blockage on the downlink in large-scale satellite optical networks: a multi-downlink scheduling for routing selection

Over years of space laser communication technology advances, satellite optical networks (SONs) have emerged as a pivotal component in 6 G networks. Satellite services are transmitted from the global view, undergoing transmission through SONs, and being downloaded to the targeted areas. However, the transmission capacity of satellites passing through the areas where users are concentrated may be insufficient to download services transmitted worldwide. This problem exists in various kinds of satellite networks and may cause a large amount of service congestion. In this paper, we propose a multi-downlink delivery routing selection (MDD-RS) strategy to study the total utilization of transmission capacity of SONs. We construct an integer linear programming (ILP) model to establish an optimal case study for minimal network capacity occupation. Also, we design an online option, MDD-RS heuristic algorithm, dynamically calculating path routes, considering bandwidth allocation and resource constraints. A comparative analysis against the conventional single-downlink scheme reveals superior performance of the MDD-RS heuristic algorithm, with a reduction in blocking probability of 0.129 and an improvement in bandwidth utilization of 0.032.

Delay-sensitive task offloading and efficient resource allocation in intelligent edge–cloud environments: A discretized differential evolution-based approach

The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resource-intensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay-aware resource-constrained offloading problem for edge–cloud systems is mathematically formulated as a 0–1 integer linear programming, and it is shown to be NP-complete. Then, a delay-aware resource-constrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests—analysis of variance, post-hoc, and Friedman tests—are also performed.

Fog Computing-Based Smart Consumer Recommender Systems

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

Traffic-based adaptive bandwidth adjustment for flexible OTN connectivity in optical networks.

In conventional optical transport networks, the service form is the fixed bandwidth connectivity, which is not flexible for carrying bursting traffic. To support the time-varying traffic in an efficient way, researchers are studying the optical service units for building the more flexible optical transport network (OTN) connectivity, which is capable of dynamic hitless bandwidth adjustment. To better utilize the benefits of flexible connectivity, network operators need efficient algorithms to adjust the flexible connectivity bandwidth, especially in the network with a massive number of connections. In this paper, based on max-min fair bandwidth allocation criteria, we propose two traffic-based adaptive bandwidth adjustment algorithms to make bandwidth adjustment decisions, with the aim to improve bandwidth effectiveness. Simulation results show that the proposed algorithms can improve bandwidth utilization by up to 16%. Additionally, under high load conditions, it can reduce the loss in traffic bitrate by a maximum of 10%.

A Comparative Study of Data Hiding Techniques for Different Image Categories

The study focuses on enhancing the perceptual appearance of shortened pictures that contain concealed content within them. It proposes a two-phase data concealment approach that integrates natural visual abilities to achieve this enhancement. The context in which this study is conducted is in intelligent capitals, where the Internet of Things (IoT) plays a significant role in gathering various sources of visual data for enhanced administration. When transmitting data over a public network, safety concerns are crucial. To address this, the study introduces a unique information concealing procedure for picture decompression using Absolute Moment Block Truncation Codes (AMBTC). AMBTC is utilized as a means to improve bandwidth utilization while satisfying safety requirements. By employing AMBTC, the study aims to maintain the original moments of the image while reducing mean squared errors (MSE) more effectively than with Block Truncation Code (BTC). The study also highlights the importance of being able to restore the original picture after data concealment. However, the amount of recoverable knowledge may be reduced, or additional knowledge might be required for recovery from the stego picture (the image with concealed data) to maintain the ability to revert to the associated stego image. The approach known as Least Significant Bit (LSB) is mentioned, which involves altering the LSB of images to incorporate digital hidden information into the host picture. This technique does not require additional information for restoration and recovery, but it may not fully restore the stego picture to its original state. In summary, this study presents a two phase data concealment approach using AMBTC to enhance the perceptual appearance of shortened pictures containing concealed content. The ability to restore the original picture is emphasized, and the LSB approach is mentioned as a means of digital information concealment.

Review of Orthogonal Frequency Division Multiplexing-Based Modulation Techniques for Light Fidelity

Light Fidelity (LiFi) technology has gained attention and is growing rapidly today. Utilizing light as a propagation medium allows LiFi to promise a wider bandwidth than existing Wireless Fidelity (WiFi) technology and enables the implementation of cellular technology to improve bandwidth utilization. In addition, LiFi is very attractive because it can utilize lighting facilities consisting of light-emitting diodes (LEDs). A LiFi system that uses intensity modulation and direct detection requires the signal of orthogonal frequency division multiplexing (OFDM) to have a real and non-negative value; therefore, certain adjustments must be made. The proposed methods for generating unipolar signals vary from adding a direct current, clipping the signal, superposing several unipolar signals, and hybrid methods as in DC-biased optical (DCO)-OFDM, asymmetrically clipped optical (ACO)-OFDM, layered ACO (LACO)-OFDM, and asymmetrically clipped DC-biased optical (ADO)-OFDM, respectively. In this paper, we review and compare various modulation techniques to support the implementation of LiFi systems using commercial LEDs. The main objective is to obtain a modulation technique with good energy efficiency, efficient spectrum utilization, and low computational complexity so that it is easy for us to apply it in experiments on a laboratory scale.

Efficient Real-time Video Conferencing with Adaptive Frame Delivery

Real-time video conferencing systems have recently become indispensable tools. However, existing commodity video conferencing systems often fail to deliver a satisfactory quality of experience (QoE) due to discrepancies between the actual network throughput and the average bitrate of encoded videos. In this paper, we present a system called Tyrus that aims to achieve efficient real-time video conferencing. Tyrus adapts its delivery of B-frames based on the real network bandwidth and the average bitrate of encoded videos, effectively addressing the QoE loss caused by mismatch issues. This system can be easily implemented on top of prevalent video conferencing systems that utilize standard video encoders. Unlike traditional approaches that treat all frame types equally, Tyrus enables adaptive delivery of B-frames, prioritizing the allocation of bandwidth to other crucial video frames. By doing so, it reduces end-to-end conferencing latency, especially when the network throughput experiences significant fluctuations. Additionally, Tyrus defines the playing deadline for B-frames by considering factors such as delivery time, buffering time, and the progress of preceding frames. It accurately classifies frame types according to video coding standards and estimates bandwidth allocation on a frame-by-frame basis. Moreover, Tyrus proactively handles potential errors in B-frame playing deadline estimation, minimizing their impact on video conferencing performance. Our evaluation results, obtained through real implementation on top of WebRTC, demonstrate the effectiveness of Tyrus. On average, it reduces per-frame latency by 19.03%, video stalls by 22.36%, and improves bandwidth utilization by at least 10.29%.

An AI-Driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively.

AdaptChain: Adaptive Scaling Blockchain With Transaction Deduplication

Although existing schemes improve blockchain throughput by allowing concurrent blocks to be appended to the blockchain, little attention has been devoted to adjusting blockchain throughput dynamically and deduplicating transactions between concurrent blocks. In this article, we propose AdaptChain, an adaptive scaling blockchain with transaction deduplication. When the transaction demand of users in the network is high, the blockchain expands to meet the demand; when the transaction demand is low, the blockchain shrinks to save communication and storage costs. Our transaction deduplication mechanism ensures that no duplicate transactions are added to the blockchain, thereby improving bandwidth utilization and achieving higher effective throughput. Besides, we randomly split the mining power of the system to achieve mining power load balancing and resist attacks. We formally analyze the blockchain security and implement the proposed prototype on Amazon EC2. Experimental results show that AdaptChain achieves dynamic and higher effective blockchain throughput.

Adaptive distribution of control messages for improving bandwidth utilization in multiple NoC

Adaptive distribution of control messages for improving bandwidth utilization in multiple NoC

TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-Based FPGAs

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM's characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBM-based FPGAs. In the first phase, 16 merge trees work in parallel to fully utilize 32 HBM channels' bandwidth. In the second phase, TopSort reuses the logic from phase one to form a wider merge tree to merge the partially sorted results from phase one. TopSort also adopts HBM-specific optimizations to reduce resource overhead and improve bandwidth utilization. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7× and 2.7× faster than state-of-the-art CPU and FPGA sorters.

An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization.

BoB: Bandwidth Prediction for Real-Time Communications Using Heuristic and Reinforcement Learning

Bandwidth prediction is critical in any Real-time Communication (RTC) service or application. This component decides how much media data can be sent in real time. Subsequently, the video and audio encoder dynamically adapts the bitrate to achieve the best quality without congesting the network and causing packets to be lost or delayed. To date, several RTC services have deployed the heuristic-based Google Congestion Control (GCC), which performs well under certain circumstances and falls short in some others. In this paper, we leverage the advancements in reinforcement learning and propose BoB (Bang-on-Bandwidth) — a hybrid bandwidth predictor for RTC. At the beginning of the RTC session, BoB uses a heuristic-based approach. It then switches to a learning-based approach. BoB predicts the available bandwidth accurately and improves bandwidth utilization under diverse network conditions compared to the two winning solutions of the ACM MMSys'21 grand challenge on bandwidth estimation in RTC. An open-source implementation of BoB is publicly available for further testing and research.

An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network

Blockchain is an emerging computing platform that provides recording and tracking facilities to substantially increase the security issues in healthcare systems. The evolution of wireless body area networks requires the continuous monitoring of the health parameters of traveling patients while traveling on road. The health parameter data of each patient are sent to the Road Side Units (RSUs) for generating the blocks by computing the required hash functions. A major challenge in such a network is to efficiently exchange the data blocks between mining RSUs and vehicles using a medium access protocol with a reduced number of collisions. The medium access problem becomes more challenging due to the vehicle mobility, high vehicle density and the varying nature of the data generated by the vehicles. In this work, a TDMA-based MAC protocol to meet an Adaptive Patients Data traffic for Vehicular Network (TAPDVN) is proposed. TAPDVN is specifically designed for patients in a vehicular network by considering the frequent entry and exit of vehicles in a mining node’s coverage area. It allows mining nodes to adjust time slots according to the sensitive patient’s data and allows the maximum number of patient vehicular nodes by considering their sensitivity to send their data in a session to compute their hash values accordingly. Simulation results verify that the proposed scheme accommodates the maximum number of high-risk patient data and improves bandwidth utilization by 20%.

Keyframe Extraction for Low-Motion Video Summarization Using K-Means Clustering

The rate of increase in multimedia data required the need for an improved bandwidth utilization and storage capacity. However, low-motion videos come with a large number of feature-related frames due to its static background. These redundant frames result to difficulty in terms of video streaming, retrieval, and transmission. In other to improve the user experience, video summarization technologies were proposed. These techniques were presented to select representative frames from a full-length video and remove the duplicated ones. Though, an improvement was recorded in the keyframe extraction process. However, a large number of redundant frames were observed to be extracted as keyframes. Therefore, this study presents an improved keyframe extraction scheme for low-motion video summarization. The proposed scheme utilizes a k-means clustering approach to group the feature-related frames within a given video data into number of clusters. Furthermore, a representative frame from each cluster was extracted as keyframe. The results obtained shown that the proposed scheme outperforms the existing scheme in terms of compression ratio, precision and recall rates with a value of 26.62%, 13.78%, and 6.63% respectively

Towards Deep Q-Network Based Resource Allocation in Industrial Internet of Things.

With the increasing adoption of Industrial Internet of Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation environments are time-varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. To this end, in this paper we propose a Deep -Network (DQN) based scheme to address both bandwidth utilization and energy efficiency in an IIoT system. In detail, we design a DQN model that consists of two deep neural networks (DNN) and a -learning model. The DNN network abstracts the features from the highly dimensional inputs and obtains the approximate -function for the -learning model. Based on the -function, the -learning model can generate the -table and reward function. After the training process, the DQN model can select appropriate actions for the agents (i.e., robots in a smart warehouse in this study) to improve bandwidth utilization and energy efficiency. To evaluate our proposed scheme, we design a simulation environment to investigate a typical IIoT scenario: the actuation of robotics in a smart warehouse. We then implement the DQN model and conduct extensive experiments to validate the efficacy of our scheme. Our experimental results confirm that our scheme can improve both bandwidth utilization and energy efficiency, as compared to other representative schemes.