The convergence of Controller Area Network with Flexible Data-Rate (CAN-FD) and Time-Sensitive Networking (TSN) presents a critical pathway to enable deterministic cross-domain communication in next-generation intelligent vehicles. However, different transmission mechanisms are raising significant challenges in maintaining low jitter and guaranteed latency. This paper proposes a low jitter framework based on a CANFD-TSN gateway to address these limitations through three key innovations: 1) A CQF-based gateway architecture integrating cyclic queuing with deadline-aware traffic scheduling, 2) An ILP model optimizing queue switching cycles, and 3) A bidirectional phase alignment mechanism that compensates asymmetric queuing delays through gateway timestamp synchronization, achieving microsecond-level jitter suppression. Extensive OMNeT++ simulations demonstrate the framework’s effectiveness, 18% higher scheduling success rates compared to conventional methods (RCSF, SPs, EDF) under 160-flow scenarios, while reducing end-to-end jitter by 42% through alignment time compensation.

Time-sensitive networking (TSN) is an important research direction for the transformation and upgrading of industrial internet infrastructure. In future industrial sites, TSN and traditional industrial networks will coexist in the same network, and this integration will be inevitable. Ensuring reliable and deterministic transmission of data flows in the converged network of Profinet and TSN will be a key research topic. This paper presents a compatible way for the Cyclic Queuing and Forwarding (CQF) queuing model of TSN and the Isochronous Real-Time (IRT) communication of Profinet. Firstly, we propose a Delay Reservation Mechanism based on CQF (DRM-CQF). This mechanism achieves reliable and deterministic transmission by delaying the sending time of cross-domain data flows in the Profinet and reserving transmission opportunities for cross-domain data flows in TSN. Secondly, we construct a mathematical optimization model based on DRM-CQF to schedule data flows in the converged network to seek the optimal schedule. Experimental results show that DRM-CQF can ensure the reliable transmission of cross-domain data flows in the Profinet and TSN converged network, and the end-to-end average delay is reduced by 49% compared with other CQF scheduling methods.

With the rapid growth of Industry 4.0, time-sensitive networking (TSN) has emerged as the new infrastructure for future industrial Internet of Things (IoT) communication. Ensuring the compatibility between TSN and legacy networks is inevitable. The ideal compatibility is to achieve deterministic interconnection and interoperability without changes in hardware and communication protocols, in other words, only using standard devices with software management. This paper targets the ideal compatibility of TSN and Profinet Isochronous Real Time (IRT). First, we propose an inter-domain Multiple Transmission Opportunity Mechanism (MTOM) to enable the asynchronous converged network of TSN and Profinet. The mechanism reserves multiple transmission time slots for cross-domain data flows to reduce their end-to-end delay and jitter. Second, we formulate an asynchronous scheduling model (ASM) based on MTOM to coschedule flows in inter-and-intra domains. Finally, a case study is performed on a typical industrial network. The experiment results demonstrate that the proposed MTOM can only use standard devices to achieve deterministic transmission of Profinet and TSN converged networks. Compared with previous asynchronous converged networks, the delay and jitter are reduced by 86% and 80% on average, respectively.

Overcharging is an important safety issue in the charging process of electric vehicle power batteries, and can easily lead to accelerated battery aging and serious safety accidents. It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various charging modes, the traditional charging time prediction method often encounters modeling difficulties and low accuracy. In response to the above problems, data drivers and machine learning theories are applied. On the basis of fully considering the different electric vehicle battery management system (BMS) charging modes, a charging time prediction method with charging mode recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and random forest fusion is proposed to classify vehicle charging modes. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance charging time prediction method is constructed by fully integrating long short-term memory (LSTM) and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed charging time prediction algorithm. Experimental results show that the new method can effectively distinguish the charging modes of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.

The explosive development of short video applications has brought severe pressure on radio resources at hotspot areas. The features of short video recommendations-and-pushing techniques provide us an opportunity to relieve the radio resource pressure via wireless multicast: An edge server can be deployed at the base station, which receives short video lists recommended by remote video server and then pushes such mobile video services to local users through wireless multicast. In this paper, we study how to reshuffle the video lists received from remote server so as to facilitate wireless multicast to maximally reduce the required wireless resource while considering the fact that a user client can only buffer one short video for watching based on off-the-shelf short video APPs. We formulate the problem of video list reshuffling for minimizing the total wireless resources consumption as an integer programming problem. We design a Minimum degree of Freedom based Maximum Filling video reshuffling algorithm (MFMF) to address this problem. MFMF moves videos from the original video lists into same sized but reshuffled video lists in a greedy manner, once for a video, whose moving can satisfy the most reshuffled video lists, and if multiple such choices exist, selects the one having the least position options. This process continues until all the videos are moved. We deduce the computation complexity of MFMF. Numerical results demonstrate the significantly high performance of MFMF.

How to effectively organize various heterogeneous clients for effective model training has been a critical issue in federated learning. Existing algorithms in this aspect are all for single model training and are not suitable for parallel multi-model training due to the inefficient utilization of resources at the powerful clients. In this paper, we study the issue of multi-model training in federated learning. The objective is to effectively utilize the heterogeneous resources at clients for parallel multi-model training and therefore maximize the overall training efficiency while ensuring a certain fairness among individual models. For this purpose, we introduce a logarithmic function to characterize the relationship between the model training accuracy and the number of clients involved in the training based on measurement results. We accordingly formulate the multi-model training as an optimization problem to find an assignment to maximize the overall training efficiency while ensuring a log fairness among individual models. We design a Logarithmic Fairness based Multi-model Balancing algorithm (LFMB), which iteratively replaces the already assigned models with a not-assigned model at each client for improving the training efficiency, until no such improvement can be found. Numerical results demonstrate the significantly high performance of LFMB in terms of overall training efficiency and fairness.

As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through Q learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).

Free-viewpoint television enables users to view a scenario from arbitrary viewpoint as if they are physically in the scenario and can watch the scene freely. Thus, Free-viewpoint television can provide immersive experience of physical event broadcast, especially for large-scale live vocal concert broadcasts. However, huge bandwidth demand is a major challenge faced by free-viewpoint television broadcast since many streaming with different view angles are needed for users' selection. In this paper, we propose a scheme named P2P transcoder to realize free-viewpoint video streaming transmissions. It selects a subset of users to work as transcoders and these transcoders will produce video rates/angles that other remaining users request. Thus the total amount of traffic to deliver is greatly reduced and more saved bandwidth can be used to improve the video quality. We further build a model for achieving optimal bandwidth allocation using this scheme. Numerical results show that the proposed scheme can significantly improve the video quality as compared with existing work.

The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high‐quality streaming Video‐on‐Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low‐quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay.

Aggregating multiple access interfaces of a client is a promising way to satisfy the high bandwidth demand by high-definition video streaming services. However, how to efficiently use such aggregated bandwidth to improve the reliability of timely fetching video contents needs to be further studied. In this paper, we propose a partial overlapping chunk based dual-path transmission scheme, which uses partial redundancy based transmissions to optimize the playback performance at the client side. Specifically, we schedule the transmissions of different sized chunks with partial overlapping according to the delivery capabilities of different paths. We then build an optimal model to compute the optimal overlapping ratio between the transmitted chunks to maximize the probability of timely fetching of video contents. Numerical results demonstrate that, our scheme can improve the probability of timely fetching video contents, by up to 19.3%, compared to the traditional scheme without chunk overlapping, while the incurred average transmission redundancy is below 14.4%.

P2P systems require neighbor peers to frequently exchange buffer-map (BM) messages for efficient content sharing and distribution, which, however, can result in considerable communication overhead. A big problem in the BMs exchanged between neighbor peers is that a lot of information in them is redundant. To reduce the redundancy, some P2P systems have adopted certain block-level compression schemes (e.g., Huffman encoding) to compress each BM in isolation. However, these schemes simply treat each BM separately and as a single block of data, which largely affects their compression efficiency. In this paper, we propose a novel relevant-window-based (RW) compression framework, which takes advantage of the correlation between sequentially exchanged BMs between neighbor peers and thus can greatly remove the redundancy in them. We accordingly design a RW-based distributed compression scheme, which can work alone or co-work well with an existing block-level compression scheme for higher compression efficiency. We prove the correctness of our scheme and derive tight upper bound on average length of compressed bitmaps by our scheme via mathematical modeling. Numerical results demonstrate that our scheme alone can achieve compression efficiency of 96.6%, which can be further increased to up to 97.1% when jointly working with a block-level compression scheme.

In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.

The correct evaluation of P2P streaming system models needs the validation in real world system. However, there is lack of systematic and integrated measurement method for real world P2P streaming system. In this paper, we propose a P2P streaming network measurement method based on a peer's buffer occupancy probability. Our method is based on the fixed duration buffer property of commercial P2P streaming systems. We prove the measured buffer occupancy probability reflects the chunk propagation process in the P2P network. We then propose a P2P streaming chunk propagation model and verify it in the commercial P2P streaming network using our measurement method. Our measurement method is useful for measuring and analyzing miscellaneous P2P streaming systems. And our model and parameter estimation are useful for existing P2P simulators to choose correct parameters and are meaningful for researchers to understand the real meaning behind the parameters.