Communication Latency Research Articles

Aplicación de Ciberseguridad cuántica en la seguridad de puertos de comunicación de la IoT

Quantum cryptography, grounded in principles of quantum mechanics such as superposition and quantum entanglement, represents a significant advancement in enhancing communications security. Methods like Quantum Key Distribution (QKD) offer encryption that is theoretically unbreakable, providing robust protection against cyber threats. However, the advent of quantum computing introduces challenges for conventional cryptographic algorithms, such as RSA and demands the development of new encryption strategies, including post-quantum methods. Integrating quantum encryption into the Internet of Things (IoT) promises to significantly enhance security levels. However, it is crucial to adapt these methods to the limitations of devices with restricted resources. As quantum computing advances, its role in data and communication protection will be crucial, though implementing these systems will face challenges related to cost and complexity. In the realm of industrial communications, selecting the appropriate protocol is essential for the efficient integration and operation of automated systems. Common industrial protocols, such as AMQP, CoAP, DDS, HTTP, MQTT, OPC, and XMPP, exhibit significant variations in aspects such as communication types, security, latency, resource usage, and reliability. Each protocol presents specific challenges, including security vulnerabilities and issues related to latency or resource usage, affecting its suitability for real-time and critical applications.

Disaster-Resilient Telecommunication with Optical Technologies

Telecommunication services enable transmission of data, voice, video, and other forms of communication over long distances through various technologies such as telephone lines, satellites, fiber optics, and wireless systems. These services are fundamental in enabling communication across different sectors, including healthcare. In disaster scenarios, the rapid and reliable delivery of healthcare services is critical for saving lives and ensuring the well-being of affected populations. Telemedicine, enabled by advanced telecommunication systems, plays a pivotal role in providing remote healthcare services when traditional medical facilities are inaccessible. This paper explores the integration of optical communication technologies, such Free Space Optics (FSO), into telemedicine systems to enhance their effectiveness in disaster response. Optical wireless communication (OWC) technologies offer high-speed, secure, and resilient communication channels, making them ideal for transmitting large volumes of medical data, real-time video consultations, and remote patient monitoring in challenging environments. By leveraging the bandwidth, low latency, and reliability of optical communication, telemedicine can support critical care operations, including remote diagnostics, emergency triage, and real-time collaboration between healthcare providers. This paper examines the potential of optical communication technologies to strengthen telemedicine services in disaster scenarios, ensuring that healthcare remains accessible, efficient, and secure, even under the most adverse conditions

Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm

UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This study’s principal objective is to further develop UAV network routing decisions by using CNNs’ ability for design recognition and the Modified SBO’s optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAV’s, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms.

Evolution and Challenges in 5G Telecommunications: A Technical Overview of Network Architecture and Performance Optimization

Abstract-The transition from 4G to 5G telecommunications networks represents a significant leap in wireless technology, marked by substantial improvements in speed, capacity, and latency. This paper explores the technical aspects of 5G network architecture, its key components, and the challenges faced in the deployment of 5G networks. Through an in-depth examination of technologies such as millimeter-wave (mmWave) communication, Massive MIMO (Multiple Input, Multiple Output), and network slicing, the paper provides a comprehensive overview of how these innovations contribute to the enhanced performance characteristics of 5G. Additionally, the paper discusses the challenges associated with 5G rollouts, including spectrum management, interference mitigation, and the integration of legacy networks. Finally, the paper compares LTE and 5G technologies, highlighting the performance improvements and new capabilities that 5G brings to telecommunications. Keywords- 5G Network Architecture, Latency Reduction, Network Slicing, Ultra-Reliable Low- Latency Communication (URLLC), IoT, Network management

Compensation of Communication Latency in Remote Monitoring Systems by Video Prediction

Compensation of Communication Latency in Remote Monitoring Systems by Video Prediction

Enhancing Distributed Neural Network Training Through Node-Based Communications.

The amount of data needed to effectively train modern deep neural architectures has grown significantly, leading to increased computational requirements. These intensive computations are tackled by the combination of last generation computing resources, such as accelerators, or classic processing units. Nevertheless, gradient communication remains as the major bottleneck, hindering the efficiency notwithstanding the improvements in runtimes obtained through data parallelism strategies. Data parallelism involves all processes in a global exchange of potentially high amount of data, which may impede the achievement of the desired speedup and the elimination of noticeable delays or bottlenecks. As a result, communication latency issues pose a significant challenge that profoundly impacts the performance on distributed platforms. This research presents node-based optimization steps to significantly reduce the gradient exchange between model replicas whilst ensuring model convergence. The proposal serves as a versatile communication scheme, suitable for integration into a wide range of general-purpose deep neural network (DNN) algorithms. The optimization takes into consideration the specific location of each replica within the platform. To demonstrate the effectiveness, different neural network approaches and datasets with disjoint properties are used. In addition, multiple types of applications are considered to demonstrate the robustness and versatility of our proposal. The experimental results show a global training time reduction whilst slightly improving accuracy. Code: https://github.com/mhaut/eDNNcomm.

Analysis of potential risks during the control of an unmanned underwater vehicle (UUV)

This article explores the analysis of potential risks associated with controlling unmanned underwater vehicles (UUVs), focusing on operational, environmental, and technological challenges. UUVs play a critical role in marine research, defense, and offshore industries but are subject to unique risks due to limited visibility, communication latency, and unpredictable underwater conditions. While UUVs have revolutionized underwater exploration and navigation, their reliance on advanced technologies introduces unique challenges. The study identifies the primary risk domains of UUV operations, including operational risks, human-machine interaction vulnerabilities, and environmental unpredictability. Using comprehensive scenario analysis and data from practical implementations, the article evaluates key risks such as navigation errors, system malfunctions, and communication disruptions. It further examines how artificial intelligence (AI) can be leveraged to mitigate these risks through real-time trajectory optimization, adaptive learning, and enhanced situational awareness. This research incorporates scenario-based analyses for UUVs to evaluate the most critical risk factors affecting mission safety. Furthermore, it explores how modern technologies can mitigate these risks by enhancing trajectory optimization and monitoring of UUVs during underwater navigation. By providing a holistic understanding of UUV risks, the findings underscore the need for robust control systems and operator training protocols. The research contributes actionable insights for improving UUV safety, efficiency, and reliability, laying the groundwork for further advancements in maritime robotics

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

Optimization of D2D emergency communication resources for UAV relay based on DA-STD3

The allocation of communication resources is extremely important for the device-to-device (D2D) communication network with unmanned aerial vehicle (UAV) relays, but this field currently mainly focuses on the communication between UAVs and ground devices. While neglecting the comprehensive optimization of air-to-ground communication and internal ground communication. This paper studies the optimization problem of communication for ground D2D devices based on UAV relays in urban environment. We study the comprehensive optimization considering the air-to-ground communication and the mobile ground communication devices. We construct the D2D communication neighbor table, and the terrestrial D2D devices will sort and select the best D2D link for pairing based on the Euclidean distance and gain set around them. We construct a communication model for this scenario and propose an improved Dual-Actor Network Softmax Twin Delayed Deep Deterministic Policy Gradient algorithm based on deep reinforcement learning. We perform transmit power optimization to address the interference caused by D2D communication using frequency division multiplexing technology. By designing the reward function and training mechanism, we can achieve good power control and reduce communication interference. The results of the simulation analysis performed in this scenario show an effective reduction in communication latency and an increase in D2I (device-to-infrastructure) channel capacity, D2D connectivity success, and energy efficiency. The feasibility and effectiveness of the algorithm is verified by comparing the baseline algorithm.

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-Synchronous Federated Learning

Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, its convergence speed and model accuracy can be compromised. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the paper introduces a semi-synchronous FL framework that uses client tiering based on computing and communication latencies. Clients in different tiers upload their local models at distinct frequencies, striking a balance between straggler mitigation and communication costs. Building on this, the paper proposes the Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning (DecantFed) algorithm to dynamically optimize client clustering, bandwidth allocation, and local training workloads in order to maximize data sample processing rates in FL. DecantFed dynamically optimizes client clustering, bandwidth allocation, and local training workloads for maximizing data processing rates in FL. It also adapts client learning rates according to their tiers, thus addressing the model staleness issue. Extensive simulations using benchmark datasets like MNIST and CIFAR-10, under both IID and non-IID scenarios, demonstrate DecantFed’s superior performance. It outperforms FedAvg and FedProx in convergence speed and delivers at least a 28% improvement in model accuracy, compared to FedProx.

Modern Kubernetes Ingress Solutions: an In-depth Comparison of Contour and Istio Architectures

This article presents a comprehensive comparative analysis of Kubernetes ingress controllers Contour and Istio, addressing the critical challenge of traffic management in modern cloud-native architectures. With Kubernetes adoption reaching 83% among enterprises in 2023, the selection of appropriate ingress controllers significantly impacts application performance, security, and operational efficiency. Through quantitative analysis of performance metrics across varying load conditions (100-10,000 RPS) and real-world case studies, this article evaluates both solutions across multiple dimensions. Results demonstrate that Contour exhibits superior performance in simple to medium-scale deployments with 30% better latency characteristics (0.5-1.5ms per request) and a 45% smaller memory footprint, while Istio proves more effective for complex deployments, handling up to 75,000 requests per second and reducing service-to-service communication latency by up to 60%. Implementation case studies of an e-commerce platform and a financial services system reveal that Contour achieves 99.99% availability with 47% infrastructure cost reduction, while Istio provides comprehensive security features including automated mTLS implementation and advanced observability tools. The article concludes with a detailed decision framework and implementation guidelines, enabling organizations to select and optimize the most appropriate ingress controller based on their specific requirements, scale of operations, and security needs. The findings demonstrate that while both solutions excel in their targeted use cases, the choice between them should be guided by application complexity, performance requirements, and operational constraints.

Optimasi Jaringan Sensor Nirkabel untuk Monitoring Suhu dan Kelembaban

The development of Internet of Things (IoT) technology has provided new opportunities to improve monitoring through the integration of temperature and humidity sensors in wireless networks. This study aims to optimize a wireless sensor network for temperature and humidity monitoring. This study combines an experimental approach with network performance analysis and optimization methods. First, the selection of temperature and humidity sensors is carried out. Next, an efficient and reliable wireless sensor network architecture is designed to collect temperature and humidity data from various locations. The process of sending data from sensors to base stations and integration with IoT platforms are also studied. Furthermore, a series of experiments are conducted to test the performance of the wireless sensor network under real conditions. Analysis of the collected data is used to identify potential improvements in network performance and improve system reliability and responsiveness. The performance parameters evaluated include connection stability, data throughput, energy consumption, and communication latency. The results of this study contribute to the development of a monitoring system that is reliable, efficient, and easy to implement.

Causality-Sensitive Scheduling to Reduce Latency in Vehicle-to-Vehicle Interactions.

This paper shows through real-life measurement that bi-directional vehicle-to-vehicle (V2V) communication latency can be dominated by sidelink scheduling delay when causality is not taken into account. Moreover, the large delay persists for a few seconds at a time once it occurs. In applications like maneuver coordination between autonomous vehicles or in platoon, such delay can be highly detrimental to safety and efficiency. We investigate the source of the problem and propose a solution that factors in causality in interactive communication. Specifically, we develop a constraint under which the resource positions are automatically aligned between the communicating vehicles, and the delay spikes are provably eliminated. Through the measurements on commercial V2X devices, we confirm that enforcing the constraint can remove latency spikes so that 5G sidelink can be more easily applied to time-sensitive interactions between vehicles.

A Secure and Decentralized Using Blockchain and Ring‐Based Cryptosystem

ABSTRACTThe advancement of Intelligent Transport Systems (ITSs) and the Internet of Vehicles (IoVs) introduces significant security challenges, particularly in ensuring secure communication between vehicles and vehicles (V2V), vehicles and Road Side Units (V2RSU). These vulnerabilities could potentially compromise the integrity of vehicular networks, making robust security measures essential. This paper aims to develop a secure communication framework for IoV environments that addresses these security concerns by enhancing authentication and encryption processes. The proposed approach combines the Ring‐Based Degree Truncated Polynomial Cryptosystem (RNTPC) with a Blockchain framework incorporating an enhanced proof‐of‐authentication (BEPoAuth) consensus algorithm. The approach is initiated from the registration phase and managed by a Trusted Authority (TA), ensuring secure vehicle and RSU registration. Authentication is carried out in two ways: V2V authentication through vehicle signatures and V2RSU authentication via batch verification of cluster members. Group keys are generated using RNTPC for secure communication, and blockchain technology is integrated to secure transactions within the IoV environment. The proposed methodology is evaluated using various performance metrics, demonstrating a 15%–20% improvement in throughput compared to existing techniques such as Practical Byzantine Fault Tolerance (PBFT), Secure and Highly Efficient Blockchain PBFT Consensus Algorithm (SG‐PBFT), credit‐based PBFT consensus algorithm (CPBFT) and Geographic‐PBFT (G‐PBFT). The system also exhibited minimal computational and communication latency while enhancing V2V and V2RSU communication efficiency. The RNTPC‐BEPoAuth framework offers a robust and secure solution for IoV communications, significantly outperforming existing methods in terms of throughput and efficiency. This approach provides a reliable foundation for secure ITSs, addressing critical security concerns in vehicular networks.

Locomotion System-Independent, Portable Software Platform for Autonomous Rovers Navigation

Autonomous navigation is a crucial feature of a rover driving on another planet like Mars due to communication latency at interplanetary distances. It significantly improves the daily traverse and can help rover drivers and engineers free the rover from hazards. Unknown areas can be explored with various locomotion techniques: wheeled rovers are the most common, but legged or crawling robots could represent a valid alternative. However, this might involve a lengthy and laborious design process of the onboard navigation software architecture, as it must be tailored to the chosen locomotion system. This paper presents a modular and adaptive method independent of the robot size and locomotion system, simplifying free field exploration and avoiding hazards along the traverse. Keywords: Abstraction, Adaptive, Autonomous Navigation, Finite State Machine, Locomotion, Navigation, Portability

Implementation of a novel secured authentication protocol for cyber security applications

Robust verification protocols are crucial for maintaining the security and reliability of sensitive information due to the increasing complexity of cyber-attacks. This paper introduces a novel 5G Secure Handover Protocol aimed at addressing security and effectiveness issues encountered in existing systems. The proposed protocol is robust against various attacks, including de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS), and jamming, ensures perfect forward key secrecy, safeguarding communication confidentiality. The proposed protocol utilizes a combination of spiking neural network and fuzzy logic (SNN-FL) techniques that must choose the goal cell as carefully as possible before initiating the transfer process. By combining fuzzy logic and spiking neural networks to reduce handover latency and thwart several types of cyberattacks, the proposed 5G Secure Handover Protocol improves security. Extensive simulations show its efficacy and emphasize its potential for safe communication in large-scale cybersecurity applications. The paper presents a novel secure authentication protocol that significantly reduces handover delays and improves efficiency. Simulations show its resilience against common security threats, protecting sensitive information and maintaining secure communication channels. The protocol, with low communication expenses, complex spatial, and latency for changeover verification, is ideal for large-scale cybersecurity applications, contributing to the development of secure digital authentication mechanisms.

Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Security analysis and provision of authentication protocol, based on peer-to-peer structure in IOT platform

The Internet of Things technology allows you to transfer data to any asset through communication networks such as the Internet or intranet. One of the most important problems is security, which includes confidentiality, authenticity validation, and completeness. Among security problems, the most important ones are ensuring authenticity and protecting privacy. The elliptic curve encryption algorithm is famous protocols for maintaining privacy and verifying authenticity. Despite the capabilities they have and provide good security against network attacks, they face challenges such as response time, delay in the authentication and authentication process, as well as the amount of memory consumed. Two-factor authentication (2FA) is a security process where users provide two different authentication factors to authenticate themselves. It also provides a higher level of security than authentication methods that rely on single-factor authentication (SFA). This article presents two novel methods for 2FA that use encryption techniques, scramble architecture, and chord architecture. These methods allow for the authentication process to be initiated by both parties involved in the communication. Three scenarios and three metrics of reaction time, memory usage, and connection latency were used to assess the efficacy of the suggested approach. The average reaction time is improved and is 0.012 s when the lengths of the finger table in the chord structure are increased to 10, 15, and 20. The findings show that the suggested approach reduces computing complexity, reaction time, and communication latency. It has an average reaction time of 0.016 s, an average memory use of 30,360 kb, and an average connection latency of 0.042 s.

Latency Analysis of Drone-Assisted C-V2X Communications for Basic Safety and Co-Operative Perception Messages

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.