Low Latency Research Articles

FPGA Implementation of 256-Bit Key AES Algorithm

Abstract: The most popular network security technique for secure data transmission between two end users, particularly over a public network, is the AES algorithm, also known as the Rijndael algorithm. It may be found in all kinds of wired and wireless digital communication networks AES, a network security technique, uses 256 bits on an FPGA. An authorized cryptographic technique that can be used to secure electronic data is the Advanced Encryption Standard technique (AES), which is specified by the National Institute of Standards and Technology. Because they provide faster and more flexible solutions, reprogrammable devices like Field Programmable Arrays (FPGA) are a very appealing alternative for hardware implementation of the encryption algorithm AES. The effective FPGA implementation of advanced encryption standards (AES) is proposed in this research. The encryption code is written in the Verilog programming language. The AES Rijndael algorithm performs better than any other when implemented on FPGA. Other works are covered by this implementation to demonstrate its effectiveness. The block and key sizes in the design are 256, and an iterative looping technique is used. Bits, S-box implementation using lookup tables. This results in a low-complexity design that can readily achieve high throughput and low latency

Revolutionizing Communication: How 5G, UAVs, and Cloud Technologies are Shaping the New Telecommunications Landscape

Background: The rapid evolution of telecommunications has been marked by the integration of advanced technologies such as Long-Term Evolution (LTE), Fifth Generation (5G) networks, Unmanned Aerial Vehicles (UAVs or drones), the Internet of Things (IoT), and cloud computing. These technologies have significantly reshaped the landscape of data transmission and communication efficiency. Objective: This review aims to evaluate the collective impact and interconnectivity of LTE, 5G, drones, IoT, and cloud technologies in modern telecommunications, highlighting advancements and identifying future trends. Methods: A comprehensive literature review was conducted, examining recent advancements in each technology and their synergistic effects on telecommunications. The review focused on peer-reviewed articles, white papers, and industry reports published between 2015 and 2023. Results: Findings indicate that 5G technology enhances IoT and drone operations by providing higher bandwidth and lower latency, which are critical for real-time data processing and control. LTE's widespread adoption provides a robust foundation for transitioning to 5G networks. IoT and cloud computing have emerged as pivotal in managing and analyzing vast amounts of data generated by telecommunications networks, improving decision-making and operational efficiency. Conclusion: The convergence of LTE, 5G, drones, IoT, and cloud technologies is pivotal in driving the next wave of telecommunications innovation. Continuous advancements in these areas are expected to further enhance connectivity and scalability, paving the way for more integrated and smart telecommunication solutions. Future research should focus on security challenges and the development of unified regulatory frameworks to support this technological evolution.

Distributed resource optimisation using the Q-learning algorithm, in device-to-device communication: A reinforcement learning paradigm

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6 %–8 %. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

A High Throughput, Low Latency 105 Gbps Four-Pipeline Stage AES

A High Throughput, Low Latency 105 Gbps Four-Pipeline Stage AES

High Spatial Resolution in Total Water Storage Variations Inferred From GPS: Case Study in the Great Lakes Watershed, US

AbstractAssessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill‐conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWSGPS). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine‐scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long‐term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWSGPS on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak‐to‐peak amplitudes of ∼40 km3 with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high‐resolution output at spatiotemporal scales with low latency.

Exploiting OFDM method for quantum communication

Orthogonal frequency-division multiplexing (OFDM) is a crucial modulation method used in contemporary digital communication systems for its significant spectral efficiency, low latency, and robustness in challenging environments. This work examines the novel use of OFDM in quantum communication, an area that offers exceptional security and efficiency in information transfer using quantum mechanics principles. In the rapidly evolving field of quantum computing, understanding, and mitigating quantum bit errors is paramount. This paper presents a rigorous analysis of bit error rates (BER) in quantum circuits, focusing on the impact of the quantum Fourier transform and its inverse, contrasted against quantum circuits employing dynamic gate sequences. Our research methodology encompasses simulations over a diverse set of parameters, including varying qubit counts ranging from 2 to 8 and theta angles (15, 30, 45, and 60°), as well as random theta values, utilizing the advanced capabilities of the Qiskit framework. Our findings indicate that quantum OFDM substantially improves quantum communication, lowering errors and boosting security. The quantum model outperforms the reference model in BER, with further enhancements as qubits increase.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

Ergodic analysis of IRS-NOMA with inter-cell interference and Imperfect-CSI and SIC over Nakagami-m faded channel

Future wireless technology requires higher data rates, low latency, and flexibility to provide services to users. To fulfil these demands, various multiple access techniques are proposed. Among these techniques, the non-orthogonal multiple access technique (NOMA) is providing better features compared to orthogonal multiple access (OMA) techniques, such as more number of radio resources with low latency. In NOMA based communication system, if a user is lying on the cell's edge, their performance is poor. Recently, intelligent reflecting surfaces (IRS)-NOMA system is introduced to enhance its performance. To implement IRS-NOMA in a real-time scenario, the impact of inter-cell interference and imperfect-channel state information (CSI) and successive interference cancellation (SIC) is very much required. In this work, we derived analytical expressions to evaluate the Ergodic capacity of the IRS-NOMA over the Nakagami-m faded channel by considering inter-cell interference, imperfect-CSI, and SIC. By considering these factors, this work offers a comprehensive understanding of the system's capabilities and limitations. Here, two user IRS-NOMA is considered and also evaluated the analytical results by varying fading parameter (m), number of interferers (K), error in CSI and SIC, and IRS elements. These results are also supported by simulated results.

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

A Brief Systematics Review and Visualization of Smart Cities based on Fog and Big Data Analysis Technology

The fog computing is based on IoT, and same as clouds are on the edge of a network between a device and the cloud, which can provide very close service to the device in real time. cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure. the ubiquitous deployment of sensors in smart cities requires a high-performance computing paradigm to support big data analysis with smart technologies and communications in IoT, providing location-awareness and latency-sensitive computing near the data sources (i.e., at the edge of the network). Cloud Computing paradigms widely used in enterprises to address the emerging challenges of big data analysis because of its scalable and distributed data management scheme. However, data centers in the Cloud faces great challenges on the burden of exploding amount of big data and the additional requirements of location awareness and low latency at the edge of network necessary for smart cites. the widely distributed sensor networks generate massive volume of data, which leads to a “Big Data “analysis challenge. The machine-to-machine(M2M) communication among massive numbers of sensors will dominate future communication network traffic, namely Internet of Things, instead of traditional Internet of Contents (IoC) in human-to-human and human-to-machine communication. In this review system where we took the research article around 33 from the different database and also taken 1987 datasets form the PubMed database from 1959 to 2024 (02 June 2024). In addition, depending on the different requirement which is partially processed the data. Then the gathered data is stored at the ledger unit through the smart cities based on the fog computing and big data technology. The hashing identity-based data encryption is a public key encryption which uses a digital signature to encrypt the data to prevent the information from the third party.

Dynamic heatmap pyramid computation for massive high-parallel spatial streaming in urban environments

ABSTRACT It is necessary to integrate data and information effectively in order to build an integrated digital and refined urban system. A multi-granularity and multi-view urban heat map formed by integrating multi-source urban information flow can assist in immediate decision-making. Managing urban big data in the form of streams requires a stable operating environment that can efficiently handle millions of sensors and devices connected to the Internet of Things (IoT) in a highly concurrent environment. Urban management data contain spatiotemporal multi-dimensional information that presents the complexity of spatiotemporal dynamic associations, further increasing the difficulty of data streaming. To overcome these challenges, we have proposed a spatiotemporal-pyramid (ST-pyramid) model that organizes multidimensional and dynamic data streams logically using a data partition strategy based on geographical grid subdivision. Accordingly, we have proposed a load-balanced heatmap pyramid computation framework that can be used to build streaming processing procedures in a distributed environment. In addition, we implemented an urban traffic heatmap prototype system based on an open-source Flink framework. The experimental results show that the real-time heatmap pyramid construction algorithm proposed in this paper has high throughput, low latency, and flexible scalability and can provide large-scale public services in time in digital earth construction.

Design and Verification of Low Latency AMBA AXI4 and ACE Protocol for On-Chip Peripheral Communication

Design and Verification of Low Latency AMBA AXI4 and ACE Protocol for On-Chip Peripheral Communication

BD-TTS: A blockchain and DRL-based framework for trusted task scheduling in edge computing

Edge computing migrates tasks to the edge of the network for execution, which can provide users with lower latency and better quality of service (QoS). However, due to the complexity and dynamism of edge environments, many task scheduling algorithms in edge computing face challenges in achieving real-time scheduling, while there are also trust issues in heterogeneous and dynamic edge environments. To tackle these issues, we introduce a novel framework for trusted task scheduling in edge computing, leveraging blockchain and deep reinforcement learning (DRL) technologies, named BD-TTS. Specifically, we design a blockchain-based trust management scheme tailored for task scheduling in edge computing. The scheme uses blockchain to store, propagate, and update trust information in a decentralized manner to evaluate the trustworthiness of edge servers. In addition, to assign tasks to edge servers with higher trust values for execution, we introduce a DRL-driven task scheduling algorithm. The algorithm dynamically schedules tasks in real-time based on fluctuations in the trust values of edge servers. The experimental results show that compared to other baseline approaches, BD-TTS effectively reduces the number of tasks assigned to malicious edge servers by over 64.4%, reduces the average task response time by at least 13.9%, and improves the success rate by more than 14.7%.

A Compact Linear Microstrip Patch Beamformer Antenna Array for Millimeter-Wave Future Communication.

5/6G is anticipated to address challenges such as low data speed and high latency in current cellular networks, particularly as the number of users overwhelms 4G and LTE capabilities. This paper proposes a microstrip patch antenna array comprising six radiating patches and utilizing a microstrip line feeding technique to facilitate the compact design crucial for 5G implementation. ROGER 3003, chosen for its advanced and environmentally friendly features, serves as the dielectric material, ensuring suitability for 5G and B5G applications. The designed antenna, evaluated at a resonating frequency of 28.8 GHz with a -10 dB impedance bandwidth of 1 GHz, offers a high gain of 9.19 dBi. Its compact array, cost-effectiveness, and broad impedance and radiation coverage position it as a viable candidate for 5G and future communication applications.

Non-intrusive fault detection in shipboard power systems using wavelet graph neural networks

Naval shipboard power systems (SPS) are rapidly embracing electrification, resulting in loads that generate pulsation currents and encounter substantial transients. However, conventional time-based features alone are inadequate for effectively monitoring and safeguarding these loads against faults. This highlights the critical requirement for advanced machine learning based methods to discern and differentiate between the various transient stages within the load profile. In this paper, we propose a Wavelet Graph Neural Network (WGNN) model for non-intrusive fault detection in SPS. The fault detection system leverages the dynamic model of the SPS to train and test performance with varying fault scenarios. The underlying structure and the interdependence among component states in the SPS network are effectively captured using the WGNN model, resulting in accuracies over 99% for intrusive fault detection and 97% for non-intrusive fault detection. The developed WGNN model has also shown to be robust in the presence of pulse loads and noise, achieving an accuracy of over 95%. At the end, a real-time simulation of the proposed method is validated on a hardware-in-the-loop system, guaranteeing the high fidelity and low latency of the proposed approach. These findings validate the effectiveness of the proposed WGNN model for fault detection and real-world applications in SPS.

Self-architectural knowledge distillation for spiking neural networks

Spiking neural networks (SNNs) have attracted attention due to their biological plausibility and the potential for low-energy applications on neuromorphic hardware. Two mainstream approaches are commonly used to obtain SNNs, i.e., ANN-to-SNN conversion methods, and Directly-trained-SNN methods. However, the former achieve excellent performance at the cost of a large number of time steps (i.e., latency), while the latter exhibit lower latency but suffers from suboptimal performance. To tackle the performance-latency trade-off, we propose Self-Architectural Knowledge Distillation (SAKD), an intuitive and effective method for SNNs leveraging Knowledge Distillation (KD). We adopt a bilevel teacher–student training strategy in SAKD, i.e., level-1 involves directly transferring same-architectural pre-trained ANN weights to SNNs, and level-2 encourages the SNNs to mimic ANN’s behavior, considering both final responses and intermediate features aspects. Learning with informative supervision signals fostered by labels and ANNs, our SAKD achieves new state-of-the-art (SOTA) performance with a few time steps on widely-used classification benchmark datasets. On ImageNet-1K, with only 4 time steps, our Spiking-ResNet34 model attains a Top-1 accuracy of 70.04%, outperforming the previous same-architectural SOTA methods. Notably, our SEW-ResNet152 model reaches a Top-1 accuracy of 77.30% on ImageNet-1K, setting a new SOTA benchmark for SNNs. Furthermore, we apply our SAKD to various dense prediction downstream tasks, such as object detection and semantic segmentation, demonstrating strong generalization ability and superior performance. In conclusion, our proposed SAKD framework presents a promising approach for achieving both high performance and low latency in SNNs, potentially paving the way for future advancements in the field.

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

Dialogue-Rewriting Model Based on Transformer Pointer Extraction

In the multi-turn dialogue scenario, users commonly encounter challenges with pronoun referents and information omission, leading to semantically incomplete representations. These issues contribute to textual incoherence, as unclear referents and missing components hinder the semantic understanding of the spoken representations of text by machines. Currently, scholars frequently resort to multiple rounds of dialogue rewriting to address the semantic challenges posed by the machine comprehension of semantically missing texts with pronoun referents and information omissions. However, existing dialogue-rewriting methods often suffer from low precision and high latency in handling such texts. To mitigate these shortcomings, this paper proposes a Transformer-based dialogue-rewriting model that utilizes pointer extraction. The method leverages a Transformer pre-training model to effectively extract the potential semantic features of text and extract the key information of text by a pointer address. By extracting keywords and appropriately replacing or inserting text, the model restores referents and missing information. The experimental findings on an open-source Chinese multi-turn dialogue-rewriting dataset demonstrate the effectiveness of the proposed method in improving both the accuracy and efficiency of rewriting compared with existing methods. Specifically, the ROUGR-1 value increased by 2.9%, while the time consumption decreased by 50% compared with the benchmark method.

Performance Evaluation of mm-Wave Based System in GFDM- 5G and Beyond Channel Model with Dust Storm Scenario

Telecommunication has made tremendous improvements in terms of bandwidth, requiring good frequency location, high data rates, and wideband spectrum availability. One solution to these requirements is the millimeter wave frequency band of 30 GHz. However, communication in this band is facing new challenges due to climate effects such as humidity, dust storms, and temperature. For fifth-generation (5G) mobile networks and beyond, Generalized Frequency Division Multiplexing (GFDM) has been proposed as a compelling candidate to substitute Orthogonal Frequency Division Multiplexing (OFDM). The GFDM's ability to adapt the block size and type of pulse shaping filters enables it to meet various crucial requirements, including low latency, low Out-Of-Band(OOB) radiation, and high data rates. This paper evaluated the overall GFDM performance and investigated the Bit Error Rate (BER) across a Rayleigh channel under various weather conditions. The simulation results show that GFDM outperforms the current OFDM candidate system. Also, GFDM offers better resistance to the Rayleigh channel with moderate and heavy dust storms in terms of BER.

Cross-modal learning for optical flow estimation with events

Benefiting from the low latency and high dynamic range, event cameras have recently been adopted for Optical Flow (OF) prediction under harsh environments with high-speed motion or extreme lighting conditions. However, the emitted events only respond to brightness changes, mostly at edges with high-intensity contrast, leading to the ill-posedness of OF prediction in non-edge or low-contrast regions. To address this problem, we propose a network to learn OF by exploring the complements between the frame and the event domains. In particular, we first extract cross-modal features from frames and events via a fusion-based encoder and then apply a self-attention module of cross-dimension interaction to suppress disturbances in noisy events. Furthermore, a long-term memory recurrent module is built for local and global temporal information combination and cross-modal feature enhancement. Finally, a CNN decoder is designed to upscale cross-modal features and provide pyramidal results for multi-scale loss computation. Extensive experiments demonstrate that the proposed method can achieve leading performance compared with the state-of-the-art OF models.