Low Transmission Delay Research Articles

Statistical-based detection of pilot contamination attack for NOMA in 5G networks

Fifth-generation (5G) communication technologies, such as millimeter wave communication, massive multiple-input-multiple-output and non-orthogonal-multiple-access (NOMA) are playing a pivotal role in promoting the modern applications of the Internet-of-Things. Using non-orthogonal resource allocation, NOMA can increase spectrum efficiency and achieve wide connectivity with low transmission delay and signaling cost. Despite the high potential of NOMA in 5G communications, NOMA is susceptible to a pilot contamination attack (PCA), in which an attacker resents the same pilot signals as authorized users. Currently, using the available detection methods in NOMA gives high false positive probability since the time-division-duplex or orthogonal resource block can be allocated by many authorized user. Since the pilot contamination attack changes the signal reception at the legitimate receiver, this work introduces a novel detection scheme for identifying Pilot Contamination attack (PCA) that statistically investigates the asymmetry in received signal power levels. The main idea of the proposed detection scheme is to use various statistical measurements for normal traffic attributes (CSI) as a reference profile. Then, compute the Mahalanobis distance between the reference profile and CSI for the incoming connection and use the probability of the uniform distribution to make the final detection decision. The performance of the proposed detection technique in terms of its detection rate and false positive probabilities has been evaluated through extensive simulation. The simulation results show that the proposed scheme succeeded in detecting the pilot contamination attack with a detection rate of up to 98% and a precision reached 97.88%.

Research on 3D reconstruction and digital protection of woodcarving works based on the combination of U-Net model and 6G network

In this study, we introduce an advanced method for the digital preservation of wood carvings, a significant component of our cultural heritage. By merging the U-Net model with 6G network technology, we’ve developed a precise and efficient 3D reconstruction process. The U-Net model achieved outstanding semantic segmentation with an average IoU of 0.97 and a Dice coefficient of 0.98, ensuring the detailed capture of wood carving features. The integration with 6G networks resulted in remarkably low data transmission delays—0.98 ms for transmission, 3.04 ms for processing, and 0.91 ms for queuing—highlighting the potential of 6G in supporting real-time, high-fidelity 3D reconstruction. This innovative approach not only meets the demands of digital preservation but also opens new avenues for protecting and promoting our cultural legacy, as affirmed by the high satisfaction scores from expert evaluations.

A Mobility Handover Decision Method Based on Multi-Topology

With the emergence of new applications in mobile networks, users demand higher network stability and lower data transmission delays. When the network address of a mobile user changes, the data transmission path in the wired network may need to be switched to maintain service continuity. Traditional mobility support methods typically rely on a single switching path for all mobile data flows. However, if this path cannot meet the requirements of all the flows, it may lead to network congestion or a decline in user experience. To overcome this limitation, this paper proposes a mobility handover decision method based on multi-topology. It enables the dynamic allocation of mobile data flows across different switching paths within multiple logical topologies. The method models a multi-topology selection problem aimed at minimizing average packet transmission delay and packet loss rate, while considering network conditions and the Quality of Service (QoS) requirements for each flow. By solving the dual problem of the original optimization, a near-optimal solution is achieved. The proposed scheme and algorithm were implemented and tested using the Mininet network simulator. Results show that the proposed approach achieves low average packet transmission delay, low average packet loss rate, and high throughput, compared to traditional single-path switching methods and existing multipath routing methods.

Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET.

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.

Effect of raw material pretreatment and ionic radius on the preparation and microwave dielectric properties of Re2TiO5 ceramics

The present study focuses on the synthesis and characterization of rare-earth titanates, specifically Re2TiO5 (ReLa, Nd, Sm), with a particular emphasis on their microwave dielectric properties. The factors influencing the synthesis of La2TiO5 compound through the traditional solid-state method were thoroughly investigated, with a significant finding being the notable impact of moisture absorption in La2O3. The impact of variations in rare-earth cation radii on the structure and dielectric properties was systematically analyzed. Re2TiO5 (ReLa, Nd, Sm) ceramics crystallized into an orthorhombic structure with space group Pnam, which was confirmed through Rietveld refinement. The resulting materials demonstrated exceptional microwave dielectric properties characterized by a low permittivity range of 13.72–17.39, high-quality factors ranging from 8331 to 17,795 GHz, and a significantly reduced temperature coefficient of resonance frequency (−33.2∼-12.6 ppm/°C). The correlation between microwave dielectric properties and structural characteristics (including ionic polarizability, packing fraction, and bond valence) was investigated. The exceptional potential of these rare-earth titanates lies in their ability to meet the stringent requirements for low signal transmission delay in wireless communication systems, owing to their advantageous low permittivity within the microwave frequency spectrum.

LAAKA: Lightweight Anonymous Authentication and Key Agreement Scheme for Secure Fog-Driven IoT Systems

Fog computing is a new computing model with limited resources located near the Internet of Things (IoT) devices to provide low transmission delay, mobility, and location awareness services. However, fog technology not only extends the cloud resources but also inherits its properties and security problems, such as device capture, man-in-the-middle, secret key compromise, identity impersonation, and message replay attacks. These vulnerabilities pose significant risks to the integrity of IoT-fog communications. Nevertheless, the existing mutual authentication approaches either suffer from high computational overheads or fail to provide secure mutual authentication. To address this gap, this paper introduces LAAKA, a Lightweight Anonymous Authentication and Key Agreement scheme. Considering the constrained resources of IoT and fog devices, LAAKA utilizes lightweight operations such as hash function and bitwise XOR. Its main objective is to facilitate mutual authentication and establish secure session keys between IoT devices and fog servers, making it useful for various IoT applications. We validate the robustness of LAAKA against various security threats by conducting comprehensive formal security analysis, including Burrows-Abadi-Needham (BAN) logic and Random Oracle Model (ROM), as well as informal analysis. The efficacy of our scheme is further demonstrated through evaluation with the Scyther tool. Compared to other proposed authentication approaches, the results illustrate the superior performance and efficiency of our approach in enhancing the security features, minimizing the computational cost, and optimizing storage utilization.

Interactive low delay music and speech communication via network connections (OVBOX)

The “OVBOX” is a tool for low-delay network audio communication and generic data transmission between multiple clients. Acoustic end-to-end delays of about 30 ms can be achieved, assuming a good internet connection and a distance between clients of less than about 1500 km. For optimal spatial perception, e.g., when using multiple instruments of similar timbre, an individual 3-dimensional room acoustic simulation based on physical modelling is applied in each client. The system is optimised for headless operation using a dedicated single-board computer (Raspberry Pi 4B), but desktop clients are also available for better integration with other audio software. A client-server system allows remote configuration and automatic traversal of network address translation routers and firewalls. With the low latency that can be achieved, the “OVBOX” is used for music applications such as distributed rehearsals or concerts. Other applications include hearing research to achieve interactive speech communication with low delay transmission of head movements for real-time control of virtual reality, and transmission of other biophysical data for online analysis or central data logging. The tool is fully open source.

Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks

In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.

Re2TiO5: Special dielectric titanates exhibiting abnormally low permittivity and a positive coefficient of resonance frequency

AbstractIn this study, a series of rare‐earth titanates with the general formula Re2TiO5 (Re = Er, Tm, Yb, and Lu) were synthesized and their microwave dielectric properties were reported. The impact of differences in rare‐earth cation radii on the structure and dielectric properties was systematically analyzed. The findings indicated that ceramics of Re2TiO5 (Re = Er, Tm, Yb, and Lu) were crystallized into a cubic structure with space group Fd‐3m, as confirmed by Rietveld refinement and selected‐area electron diffraction patterns. The rare‐earth elements were analyzed for their impact on microwave dielectric properties, considering factors such as ionic polarizability, packing fraction, and bond valence theory. The resulting materials exhibited excellent microwave dielectric properties with a low permittivity (6.95–7.21), high‐quality factors (19 451–20 115 GHz), and low positive temperature coefficient of resonance frequency (19.34–20.90 ppm/°C). The origin of low permittivity was elucidated through the analysis of bond length, X‐ray photoelectron spectroscopy, and impedance spectroscopy. This series of titanates exhibited unusually low permittivity characteristics in the microwave frequency range, which could meet the low signal transmission delay requirements in wireless communication.

Low Delay Transmission Scheme of Power Communication Information Based on 5G Network

Power information data transmission is prone to packet loss rate, and communication protocol is required to improve the transmission capacity of power information data. Therefore, a low delay transmission scheme of power communication information based on 5G networking is proposed and constructed, the power communication information data through 5G networking technology is processed and is sent to the corresponding data application module for data classification and mining, and then the information data is converted in different formats to the same data transmission format. TCP communication protocol as the communication protocol is selected for data online transmission to describe the low delay transmission of power communication information. The chaotic sequence is set based on 5G networking, and the connection weight matrix is calculated. Then the power communication information sequence is encrypted in real time according to the row column substitution rule. On this basis, the data encryption module, dynamic key generation module and shared key update module are combined to realize the low delay transmission of power communication information. The experimental results show that with the increase of the network area and the number of nodes, the data transmission volume can be gradually increased. The change range of this method is relatively small, which can effectively improve the feasibility of the low delay transmission scheme of power communication information.

D-BLAC: A dual blockchain-based decentralized architecture for authentication and communication in VANET

With all the advancements in the last decade in Intelligent Transportation systems (ITS), there has been an exponential increase in the number of applications provided to the Vehicular Ad Hoc Networks (VANETs). These applications provide unmatched security and safety services. As a result, there is a huge surge in the amount of data being generated and transmitted within the VANETs. In this paper, we have proposed a dual blockchain based decentralized architecture for authentication of the vehicles and secure & efficient communication within the network. The use of individual blockchains for authentication and message sharing makes the network more efficient and fast while also helping to increase the network security. Both the blockchains use a different consensus algorithm which adds another layer of security. The security and performance evaluation of the proposed architecture proves that it is resistant to a number of security attacks and has a lower computational cost, higher throughput, lower transmission delay and higher vehicle verification rate by a significant amount when compared to existing schemes.

DA-DMPF: Delay-aware differential multi-path forwarding of industrial time-triggered flows in deterministic network

The time-sensitive network (TSN) utilizes gating technology to achieve mixed traffic transmission across the common network, replacing dedicated networks with time division multiplexing, simplifying the interconnection architecture. TSN fundamentally changes the uncertainty of traditional Ethernet through clock synchronization, traffic scheduling, and network configuration. TSN can meet the requirements of industrial communication for low delay, low jitter, and high-reliable data transmission. It is also fully compatible with the existing Ethernet system and is an important communication platform supporting the development of the industrial Internet. Time-triggered flow is the key flow responsible for transmitting real-time data in time-sensitive networks. Its transmission should strictly follow the gating schedule. Selecting different transmission paths will affect the success rate of scheduling. To solve the delay optimization problem of industrial time-triggered (TT) flow path selection, this paper proposes a novel forwarding mechanism for industrial TT flow, namely delay-aware differential multi-path forwarding (DA-DMPF). This mechanism first designs a multipath forwarding index based on sending delay and processing delay by analyzing the end-to-end delay, then converts the multipath forwarding problem into a 0-1 integer linear planning problem, and finally solves the forwarding path set of the stream through differential multipath forwarding algorithm to achieve low delay parallel transmission of the stream. The simulation results show that compared with load-balanced multi-path forwarding and Hop-Load forwarding mechanisms, this scheme reduces the end-to-end average delay of industrial TT flows by 22.55% and 17.32% respectively, meeting the low delay transmission requirements of TT flows.

A Task-Oriented Hybrid Routing Approach based on Deep Deterministic Policy Gradient

With the development of communication and transmission technologies, the applications of Internet of Things (IoT) and telemedicine are increasingly sensitive to network latency. To meet this urgent demand, learning-based routing approaches emerge, with better performance and higher flexibility. These routing approaches can be divided into single-path and multipath, which have lower transmission delay and better load balancing respectively. However, the traffic scheduling policy is not only affected by the network states but also needs to consider the requirements for traffic tasks. We propose a Task-Oriented Hybrid Routing Approach (TOHRA) based on Deep Deterministic Policy Gradient (DDPG) in Software-Defined Networking (SDN). First, the hybrid routing optimization algorithm comprehensively considers the network states and task requirements, and outputs a task-oriented hybrid routing that combines the advantages of single-path and multipath. Moreover, we design a hop-by-hop traffic segmentation model based on DDPG to output the traffic segmentation ratio on hybrid routing to adapt to the changing transmission path. Experimental results show that TOHRA achieves the best performance of load balancing and network throughput. Especially in the case of Type C tasks in Germany topology, the average network throughput of THORA is increased by 32.86%, and the average variance of link load rate is reduced by 46.62%.

An Enhanced Load Balancing Algorithm for Cloud Enterprise Resource Planning (ERP) Data in a Multi-Cloud Environment

Businesses and individuals have seen the need to adopt the cloud and mulit-cloud environment for their businesses and storage of data. The load balancing concerns especially in the multi-cloud environment was investigated and a new algorithm proposed. In this research, a proposed new load balancing algorithm is presented and compared with the Round Robin (RR) and Weighted Round Robin (WRR) algorithms. The proposed scheduling algorithm considered several Cloud ERP Data chunks to analyse the data transmission rate or throughput, the transmission delay, data loss and the Cloud ERP Data drop ratio. The proposed algorithm performed better compared to the Round Robin (RR) and Weighted Round Robin (WRR) in a multi cloud environment with data chunks above 150 in terms of throughput. The proposed algorithm again outperformed the RR and WRR with a recorded lower transmission delays and lower data loss.

High Reliable Uplink Transmission Methods in GEO–LEO Heterogeneous Satellite Network

As a significant component of non-terrestrial networks, satellite communication has gradually evolved into a heterogeneous topology where the network nodes are classified as low Earth orbit (LEO) and geostationary Earth orbit (GEO) nodes. Considering the advantage of low transmission delay of LEO satellites in a heterogeneous network, ground users are arranged to access LEO satellites in the first priority. By fully understanding the equivalent channel gain difference in the beam-edge and beam-center users, we develop non-orthogonal multiple access (NOMA) in an LEO network where different users are allocated in the same resource block. As a complementary strategy, the beam-edge user has the ability to connect the GEO satellite if the LEO links are not available. That is, the two users are served cooperatively by LEO and GEO satellites in an orthogonal manner. In this paper, we provide deep insights for the uplink transmission performance of NOMA and cooperative orthogonal multiple access methods. As a general metric, the analytical expressions of outage probability are derived and the diversity orders are also provided. The simulation results show that NOMA is capable of providing remarkable performance in low signal-to-noise ratio regions and is more promising when the channels are assumed to be ordered.

DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks

Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network.

A Multi-Agent Deep Reinforcement Learning-Based Popular Content Distribution Scheme in Vehicular Networks.

The Internet of Vehicles (IoV) enables vehicular data services and applications through vehicle-to-everything (V2X) communications. One of the key services provided by IoV is popular content distribution (PCD), which aims to quickly deliver popular content that most vehicles request. However, it is challenging for vehicles to receive the complete popular content from roadside units (RSUs) due to their mobility and the RSUs' constrained coverage. The collaboration of vehicles via vehicle-to-vehicle (V2V) communications is an effective solution to assist more vehicles to obtain the entire popular content at a lower time cost. To this end, we propose a multi-agent deep reinforcement learning (MADRL)-based popular content distribution scheme in vehicular networks, where each vehicle deploys an MADRL agent that learns to choose the appropriate data transmission policy. To reduce the complexity of the MADRL-based algorithm, a vehicle clustering algorithm based on spectral clustering is provided to divide all vehicles in the V2V phase into groups, so that only vehicles within the same group exchange data. Then the multi-agent proximal policy optimization (MAPPO) algorithm is used to train the agent. We introduce the self-attention mechanism when constructing the neural network for the MADRL to help the agent accurately represent the environment and make decisions. Furthermore, the invalid action masking technique is utilized to prevent the agent from taking invalid actions, accelerating the training process of the agent. Finally, experimental results are shown and a comprehensive comparison is provided, which demonstrates that our MADRL-PCD scheme outperforms both the coalition game-based scheme and the greedy strategy-based scheme, achieving a higher PCD efficiency and a lower transmission delay.

IMF-PR: An Improved Morton-Filter-Based Pseudonym-Revocation Scheme in VANETs.

Vehicle ad hoc networks (VANETs) are special wireless networks which help vehicles to obtain continuous and stable communication. Pseudonym revocation, as a vital security mechanism, is able to protect legal vehicles in VANETs. However, existing pseudonym-revocation schemes suffer from the issues of low certificate revocation list (CRL) generation and update efficiency, along with high CRL storage and transmission costs. In order to solve the above issues, this paper proposes an improved Morton-filter-based pseudonym-revocation scheme for VANETs (IMF-PR). IMF-PR establishes a new distributed CRL management mechanism to maintain a low CRL distribution transmission delay. In addition, IMF-PR improves the Morton filter to optimize the CRL management mechanism so as to improve CRL generation and update efficiency and reduce the CRL storage overhead. Moreover, CRLs in IMF-PR store illegal vehicle information based on an improved Morton filter data structure to improve the compress ratio and the query efficiency. Performance analysis and simulation experiments showed that IMF-PR can effectively reduce storage by increasing the compression gain and reducing transmission delay. In addition, IMF-PR can also greatly improve the lookup and update throughput on CRLs.

Research on cloud-edge AC Power Monitoring Systems based on MODBUS

With the development of the Internet of Things and automation technology in the industry, the types of equipment in the industrial field are increasing, and the data parameters of the industrial process are gradually becoming complex. It puts forward higher requirements for the system perception layer’s real-time performance, reliability, and maintainability. The original data acquisition architecture is not easy to ensure the reliable transmission and real-time processing of massive industrial data. In order to solve these problems, this paper designs an AC Power Monitoring System. It proposes a cloud edge computing framework based on MODBUS: First, the data is collected and sent to the edge server by MODBUS through the RS485 bus. Second, combined with the advantages of the powerful service capability of cloud computing resources and the low transmission delay of edge computing, the edge side and cloud side will work together. Finally, the functions of data reading, uploading and sending, correction, configuration, and fault alarm are realized. The field operation shows that using a cloud edge computing framework can reduce the processing pressure of cloud computing, reduce network latency and lower requirements for network bandwidth and make the system efficient and safe for collecting and analyzing big data. The cooperation of MODBUS protocol enhances the reliability and practicability of communication. The cloud edge computing system based on MODBUS is easy to design and maintain and is suitable for many monitoring systems, which have high promotion value.

Large interfacial polarization in metal–organic framework hollow ZIF-8/polyimide low-k nanocomposite films

Polymer films with tailored dielectric properties and large electrical displacement are required for applications in microelectronics due to the need for ultrahigh speed and low transmission delays. In this study, we developed a low-k polyimide (PI) nanocomposite incorporated with metal–organic framework hollow ZIF-8 (HZIF-8) nanocrystals to achieve a high energy storage capacity. The PI nanocomposite film was prepared by solution casting a mixture of HZIF-8/polyamide acid with thermal imidization. A dielectric constant of 2.26 was obtained at 100 Hz for the 5 wt% nanocomposite with a multi-stage pore architecture. Due to improved interfacial polarization, the 1 wt% nanocomposite had a high energy density of 12.2 J/cm3 and a charge–discharge efficiency of 70% at 600 MV/m. The PI nanocomposite film exhibited dielectric reliability with inherent flexible performance, and it is expected to be a primary candidate for polymer dielectric applications in microelectronics.