Reduce Transmission Latency Research Articles

Hierarchical Mixed-Precision Post-Training Quantization for SAR Ship Detection Networks

Convolutional neural network (CNN)-based synthetic aperture radar (SAR) ship detection models operating directly on satellites can reduce transmission latency and improve real-time surveillance capabilities. However, limited satellite platform resources present a significant challenge. Post-training quantization (PTQ) provides an efficient method for pre-training neural networks to effectively reduce memory and computational resources without retraining. Despite this, PTQ faces the challenge of maintaining model accuracy, especially at low-bit quantization (e.g., 4-bit or 2-bit). To address this challenge, we propose a hierarchical mixed-precision post-training quantization (HMPTQ) method for SAR ship detection neural networks to reduce quantization error. This method encompasses a layerwise precision configuration based on reconstruction error and an intra-layer mixed-precision quantization strategy. Specifically, our approach initially utilizes the activation reconstruction error of each layer to gauge the sensitivity necessary for bit allocation, considering the interdependencies among layers, which effectively reduces the complexity of computational sensitivity and achieves more precise quantization allocation. Subsequently, to minimize the quantization error of the layers, an intra-layer mixed-precision quantization strategy based on probability density assigns a greater number of quantization bits to regions where the probability density is low for higher values. Our evaluation on the SSDD, HRSID, and LS-SSDD-v1.0 SAR Ship datasets, using different detection CNN models, shows that the YOLOV9c model with mixed-precision quantization at 4-bit and 2-bit for weights and activations achieves only a 0.28% accuracy loss on the SSDD dataset, while reducing the model size by approximately 80%. Compared to state-of-the-art methods, our approach maintains competitive accuracy, confirming the superior performance of the HMPTQ method over existing quantization techniques.

Performance analysis of multiuser mmWave DCT-spread CP-Less OFDM communication system

In this paper, we propose a framework for multiuser mmWave DCT-Spread CP-less OFDM communication system and analyze it comprehensively. Due to excessive cyclic prefix (CP) usage in conventional multicarrier systems, spectral efficiency reduces and increases transmission latency. Our proposed system enhances spectral efficiency and reduces transmission latency. We introduce an image encryption algorithm based on DNA encoding combined with a chaotic map is introduced to enhance physical layer security (PLS). The impact of block diagonalization (BD) channel precoding for multiuser interference (MUI) reduction and designed subcarrier mapping with time-domain Tukey windowing for out-of-band (OOB) power emission reduction are investigated. In addition, the paper studies the applicability of discrete cosine transforms (DCT)-Spreading for peak-to-average power ratio (PAPR) reduction. Simulation results demonstrate that Turbo and Repeat and Accumulate (RA) channel coding and minimum mean square error (MMSE) and Cholesky decomposition (CD)-based zero-forcing (ZF) signal detection schemes improve bit error rate (BER) performance of the proposed system for different users.

Energy-efficient quad tree-based clustering using edge-assisted UAV-relay to enhance network lifetime in WSN

Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Design and implementation for a UAV-based streaming media system

The Unmanned Aerial Vehicle (UAV)-based video streaming transmission is critical for real-time communication (RTC) applications in flying ad-hoc networks (FANET). The communication links between the nodes, however, are often unstable, especially in harsh network environments. This article first presents an architecture of a UAV-based streaming media system (USMS) for more reliable transmission, where the UAVs play multiple roles simultaneously in terms of video capturing, encoding, transmission, caching, decoding, and evaluation. To promote fluency of video transmission, we propose a distributed algorithm to explore the Tradeoff between the bandWidth, the Load, and the Video parameters (TWLV) based on the UAV flying status and Quality of Service (QoS). To enhance the Quality of Experience (QoE) significantly, we design an application-oriented multi-link transmission system with a novel multi-module stacking technology. Leveraging this design, we implement an extended splitting-merging streaming (ESMS) algorithm to deal with heavy payloads and reduce transmission latency. We also propose a multi-link retransmission (MLR) algorithm to eliminate consecutive packet losses and achieve more efficient video rebuffering. Sufficient experimental results verify that compared with the commercially available products that utilize application-layer forward error correction (AL-FEC), MultiPath TCP (MPTCP), and Deadline-aware Transport Protocol (DTP), our proposed USMS outperforms in throughput promotion, latency reduction, congestion control, packet recovery, and rebuffering efficiency.

Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods.

Importance-aware data selection and resource allocation for hierarchical federated edge learning

Compared to traditional machine learning approaches, federated learning (FL) is effective in dealing with mobile device data privacy issues. Apart from utilizing the cloud computing server as the model aggregation center, edge computing servers can also be advocated as intermediaries to perform model aggregation near the devices, which can reduce transmission latency and energy consumption. In this paper, we consider a multi-layer federated edge learning framework where both cloud and edge servers are used for FL and design a Data Importance-aware Hierarchical Federated Edge Learning (DHFL) scheme. We develop a joint data selection and resource allocation algorithm based on data importance to maximize learning efficiency in DHFL. To solve this problem, we decompose it into three sub-problems including edge-device association, resource allocation and data selection. By presenting the optimal strategy for each edge-device group, the optimal association between devices and edge servers is achieved through an iterative global cost reduction adjustment process, and data selection is performed by using convex optimization scheme. Extensive simulations are carried out to verify the proposed scheme and show that our proposal can achieve smaller training loss using less than 1/6 of the data and reduce latency by 80% compared to FedAvg.

Digital Twin and Artificial Intelligence-Empowered Panoramic Video Streaming: Reducing Transmission Latency in the Extended Reality-Assisted Vehicular Metaverse

Digital Twin and Artificial Intelligence-Empowered Panoramic Video Streaming: Reducing Transmission Latency in the Extended Reality-Assisted Vehicular Metaverse

Mobility-aware multi-user service placement and resource allocation in edge computing

The rise of Mobile Edge Computing (MEC) has brought us its enormous potential and value in mobile service applications. By pushing computing and storage resources from the cloud to the network edge, it reduces transmission latency, and supports applications that require low latency, such as virtual reality and video analytics IoT services. However, many existing works only consider the problem of service placement in MEC, and the problem of computing resource allocation has received less attention. This paper focuses on the joint optimization of Service Placement and computational Resource Allocation (SPRA) in the MEC environment, with the goal of minimizing the total cost of service latency, communication latency, and service migration. For offline cases, we propose an optimal algorithm, D-SPA , based on dynamic programming and an improved algorithm, S-SPA, based on state sampling, which effectively alleviates the state explosion problem in D-SPA. In the online case, due to the unpredictability of future environmental information, we propose the online greedy algorithm OGA, and theoretically show the approximate ratio of OGA. Extensive experiments show that our algorithm is more efficient than other baseline algorithms, and it reduces the total cost by 28.6% on average.

An efficient scheme for in-orbit remote sensing image data retrieval

The conflict between real-time retrieval of remote sensing image data (RSID) and the limited satellite network transmission resources is becoming more and more prominent. To address it, this paper proposes a novel scheme for RSID retrieval from the remote sensing satellite network, which exploits the spatio-temporal characterization (STC) of sensing images to improve data retrieval efficiency. In particular, based on the spatio-temporal characterization, a data content naming scheme is introduced and an algorithm is accordingly proposed for RSID segmentation in satellite nodes. With the naming scheme, RSID retrieval can be implemented by making use of the Named Data Networking (NDN) architecture. The study aims to investigate the effectiveness of making use of the STC of RSID for the retrieval under the NDN architecture. To the best of our knowledge, this paper is the first to exploit STC to establish connections between user demands, RSID, and data naming for NDN. As a result, the proposed scheme makes the network aware of RSID’s STC and consequently improves the data retrieval performance of the satellite network using NDN. The evaluation results show that the proposed scheme delivers about 65% and 7% higher retrieval hit rates than a retrieval scheme based on the original NDN proposal and a recently proposed scheme — CAP, respectively. In addition, comparing with these two schemes, the proposed one reduces transmission latency by about 20% and 15%, and traffic load by about 48% and 40%, all respectively. These imply that the proposed scheme outperforms in terms of higher data retrieval efficiency and better link utilization.

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

The construction of the Industrial Internet has become a concrete implementation and an essential starting point in accelerating the digital transformation and intelligent upgrading of industrial manufacturing enterprises. The problem of addressing and forwarding for resource-constrained devices restricts data acquisition and dissemination on the Industrial Internet. In order to retrieve content reasonably, we propose IDEANE, an identity-differentiated content acquisition and multipath forwarding scheme with network coding and edge computing. In IDEANE, content requests are disseminated based on the identity and location information carried in a multi-identifier network, which can improve the efficiency of interest message requests and reduce idle links in the network. Moreover, the content acquisition computation is offloaded to multiple edge nodes, and the encoded data are transmitted to the edge nodes for recovery. IDEANE offloads the less demanding computing tasks to the edge nodes close to the content requesters, to relieve the pressure on providers. In addition, a collaboration method among multiple edge nodes is also studied. Multiple edge nodes collaborate to support the mobility of content requesters, save energy consumption in terminal devices, reduce transmission latency, and ensure data security. The experimental results show that IDEANE can avoid duplicated transmission, reduce the network link overhead, improve network throughput, and enhance network robustness and reliability.

1-Bit Compressive Sensing for Efficient Federated Learning Over the Air

For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog-aggregation transmissions. To facilitate design parameter optimization, we analyze the efficacy of the proposed scheme by deriving a closed-form expression for the expected convergence rate. Our theoretical results unveil the tradeoff between convergence performance and communication efficiency as a result of the aggregation errors caused by sparsification, dimension reduction, quantization, signal reconstruction and noise. Then, we formulate a joint optimization problem to mitigate the impact of these aggregation errors through joint optimal design of worker scheduling and power scaling policy. An enumeration-based method is proposed to solve this non-convex problem, which is optimal but becomes computationally infeasible as the number of devices increases. For scalable computing, we resort to the alternating direction method of multipliers (ADMM) technique to develop an efficient implementation that is suitable for large-scale networks. Simulation results show that our proposed 1-bit CS based FL over the air achieves comparable performance to the ideal case where conventional FL without compression and quantification is applied over error-free aggregation, at much reduced communication overhead and transmission latency.

Nonquantal transmission at the vestibular hair cell–calyx synapse: KLV currents modulate fast electrical and slow K+ potentials

Vestibular hair cells transmit information about head position and motion across synapses to primary afferent neurons. At some of these synapses, the afferent neuron envelopes the hair cell, forming an enlarged synaptic terminal called a calyx. The vestibular hair cell-calyx synapse supports a mysterious form of electrical transmission that does not involve gap junctions, termed nonquantal transmission (NQT). The NQT mechanism is thought to involve the flow of ions from the presynaptic hair cell to the postsynaptic calyx through low-voltage-activated channels driven by changes in cleft [K+] as K+ exits the hair cell. However, this hypothesis has not been tested with a quantitative model and the possible role of an electrical potential in the cleft has remained speculative. Here, we present a computational model that captures experimental observations of NQT and identifies features that support the existence of an electrical potential (ϕ) in the synaptic cleft. We show that changes in cleft ϕ reduce transmission latency and illustrate the relative contributions of both cleft [K+] and ϕ to the gain and phase of NQT. We further demonstrate that the magnitude and speed of NQT depend on calyx morphology and that increasing calyx height reduces action potential latency in the calyx afferent. These predictions are consistent with the idea that the calyx evolved to enhance NQT and speed up vestibular signals that drive neural circuits controlling gaze, balance, and orientation.

DEEP LEARNING-DRIVEN DIFFERENTIATED TRAFFIC SCHEDULING IN CLOUD-IOT DATA CENTER NETWORKS

The development of 5G technology has enabled the cloud-internet of things (IoT) to impact all areas of our lives. Sensors in cloud-IoT generate large-scale data, and the demand for massive data processing is also increasing. The performance of a single machine can no longer meet the needs of existing users. In contrast, a data center (DC) integrates computing power and storage resources through a specific network topology and satisfies the need to process massive data. Regarding large-scale heterogeneous traffic in DCs, differentiated traffic scheduling on demand reduces transmission latency and improves throughput. Therefore, this paper presents a traffic scheduling method based on deep Q-networks (DQN). This method collects network parameters, delivers them to the environment module, and completes the environment construction of network information and reinforcement learning elements through the environment module. Thus, the final transmission path of the elephant flow is converted based on the action given by DQN. The experimental results show that the method proposed in this paper effectively reduces the transmission latency and improves the link utilization and throughput to a certain extent.

A dynamic decentralized strategy of replica placement on edge computing

Smart phone and its applications are used more and more extensively in our daily life. Short delay of arriving data is important to these applications, especially to some time-sensitive ones. To reduce transmission latency and improve user experience, a dynamic decentralized data replica placement and management strategy which works in edge nodes is proposed in this article. It studies the location, access frequency, latency improvement, and cost spent on placing replicas on edge nodes to seek a balance between cost spent for storage and reduced latency. Specifically, dynamic and decentralized replica placement strategy algorithm has load guarantee for edge nodes to avoid overload; it dynamically create or delete data replicas on edge nodes according to the request frequency. Dynamic and decentralized replica placement strategy is decentralized to relieve transmission cost. Experiment results show that dynamic and decentralized replica placement strategy algorithm in edge computing environments can greatly reduce transmission latency, balance edge nodes load, and improve system performance. Dynamic and decentralized replica placement strategy also considers the cost spent for storage, and it pursues a balance between many factors.

Coded Caching for Broadcast Networks with User Cooperation.

Caching technique is a promising approach to reduce the heavy traffic load and improve user latency experience for the Internet of Things (IoT). In this paper, by exploiting edge cache resources and communication opportunities in device-to-device (D2D) networks and broadcast networks, two novel coded caching schemes are proposed that greatly reduce transmission latency for the centralized and decentralized caching settings, respectively. In addition to the multicast gain, both schemes obtain an additional cooperation gain offered by user cooperation and an additional parallel gain offered by the parallel transmission among the server and users. With a newly established lower bound on the transmission delay, we prove that the centralized coded caching scheme is order-optimal, i.e., achieving a constant multiplicative gap within the minimum transmission delay. The decentralized coded caching scheme is also order-optimal if each user’s cache size is larger than a threshold which approaches zero as the total number of users tends to infinity. Moreover, theoretical analysis shows that to reduce the transmission delay, the number of users sending signals simultaneously should be appropriately chosen according to the user’s cache size, and always letting more users send information in parallel could cause high transmission delay.

Multicarrier-Division Duplex: A Duplexing Technique for the Shift to 6G Wireless Communications

In band full duplex (IBFD) has the potential to not only double spectral efficiency (SE) but also greatly reduce transmission latency. However, the immaturity of existing self-interference cancellation (SIC) techniques renders IBFD impractical for near-future wireless applications. To inherit the merits of FD but with practical SIC requirements, multicarrier-division duplex (MDD) was recently proposed and studied. In this article, we demonstrate the advantages of MDD over the IBFD mode and the conventional half-duplex (HD) modes of frequency-division duplex (FDD) and time-division duplex (TDD) from several essential aspects, including SIC capability, resource integration, and the support for high-mobility communications. Several numerical results are included to show that MDD outperforms IBFD in terms of energy efficiency (EE) and SIC. Compared with the HD modes, MDD is capable of efficiently integrating DL/UL resources to achieve higher SE and significantly outperforms TDD when communicating over fast time-varying channels. Finally, some implementation challenges of MDD systems are discussed.

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution.

Visual-auditory sensory substitution has demonstrated great potential to help visually impaired and blind groups to recognize objects and to perform basic navigational tasks. However, the high latency between visual information acquisition and auditory transduction may contribute to the lack of the successful adoption of such aid technologies in the blind community; thus far, substitution methods have remained only laboratory-scale research or pilot demonstrations. This high latency for data conversion leads to challenges in perceiving fast-moving objects or rapid environmental changes. To reduce this latency, prior analysis of auditory sensitivity is necessary. However, existing auditory sensitivity analyses are subjective because they were conducted using human behavioral analysis. Therefore, in this study, we propose a cross-modal generative adversarial network-based evaluation method to find an optimal auditory sensitivity to reduce transmission latency in visual-auditory sensory substitution, which is related to the perception of visual information. We further conducted a human-based assessment to evaluate the effectiveness of the proposed model-based analysis in human behavioral experiments. We conducted experiments with three participant groups, including sighted users (SU), congenitally blind (CB) and late-blind (LB) individuals. Experimental results from the proposed model showed that the temporal length of the auditory signal for sensory substitution could be reduced by 50%. This result indicates the possibility of improving the performance of the conventional vOICe method by up to two times. We confirmed that our experimental results are consistent with human assessment through behavioral experiments. Analyzing auditory sensitivity with deep learning models has the potential to improve the efficiency of sensory substitution.

Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

Packet-Level Slot Scheduling MAC Protocol in Underwater Acoustic Sensor Networks

With the development of the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have become an enabling system to support real-time and continuous data transmission. Existing scheduling-based medium access control (MAC) protocols ignored the limitations between the generation time and the transmission time of forwarding packets. This results in unavailability of some scheduled slots, and makes packets endure more waiting time on relay nodes and thereafter longer end-to-end delay. To reduce transmission latency, this article develops a novel scheduling-based MAC protocol, which schedules slots in the packet level. All packets to be generated and transmitted in a frame are predicted and scheduled. Considering our defined packet collision constraint and traffic-flow constraint, we formulate the slot scheduling problem in a frame into a combinatorial optimization problem, which ensures that all source packets transmitted in a frame can be delivered to the sink node with the minimum average end-to-end delay within the same frame. To solve this problem, two algorithms are proposed, namely, an optimal packet-level slot scheduling (PLSS) algorithm and a heuristic approximate PLSS (PLSS-A) algorithm. The performances of our proposed protocol using both algorithms are evaluated with different network scales, packet lengths, and offered traffic loads. Numerical results demonstrate that both PLSS and PLSS-A perform well in terms of average end-to-end delay and service fairness, and have an advantage in the network throughput in large-scale networks. Our proposed PLSS MAC protocol is predicted to be promising in large-scale UASNs with demand for real-time and long-term monitoring.

Edge-Network-Assisted Real-Time Object Detection Framework for Autonomous Driving

Computer vision tasks such as object detection are crucial for the operations of autonomous vehicles (AVs). Results of many tasks, even those requiring high computational power, can be obtained within a short delay by offloading them to edge clouds. However, although edge clouds are exploited, real-time object detection cannot always be guaranteed due to dynamic channel quality. To mitigate this problem, we propose an edge-network-assisted real-time object detection framework (EODF). In an EODF, AVs extract the region of interest (Rols) of the captured image when the channel quality is not sufficiently good for supporting real-time object detection. Then AVs compress the image data on the basis of the Rols and transmit the compressed one to the edge cloud. In so doing, real-time object detection can be achieved due to the reduced transmission latency. To verify the feasibility of our framework, we evaluate the probability that the results of object detection are not received within the inter-frame duration (i.e., outage probability) and their accuracy. From the evaluation, we demonstrate that the proposed EODF provides the results to AVs in real time and achieves satisfactory accuracy.