Low-latency Information Research Articles

UV PHOTODETECTOR MODULE BASED ON THIN FILM ZINC OXIDE

An analysis of the work of the created photodetector module for ultraviolet (UV) radiation based on the zinc oxide/silicon heterostructure was carried out. A wireless online UV photodetector network capable of providing real-time data has been developed and tested. The Wi-Fi protocol is configured to maximize spatial coverage and have high bandwidth to meet radiation monitoring requirements. The availability of low-latency information makes the monitoring system a sufficient tool for a wide range of photovoltaic system control.

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Real-time monitoring of solar energetic particles outside the ISS with the AMS-02 instrument

Space human activities in the International Space Station (ISS) are always ongoing. The atmosphere and geomagnetic field in this environment only offer partial protection from radiation. Considering the whole energetic spectrum of cosmic rays (CR), the highest energy particles are the most dangerous, but the rarest: on the other hand, sporadic solar emissions of plasma, in the low end of the energetic spectrum, inject an intense and dangerous flux of charged particles called Solar Energetic Particles (SEPs) in the Sun-Earth environment. The experiment Alpha Magnetic Spectrometer (AMS-02) operates on the ISS since 2011, performing precision measurements of cosmic ray composition and flux. In this study we aim to verify if the low latency information provided by AMS-02 could be used to perform SEP and solar activity real-time monitoring in the ISS external environment. An algorithm has been developed to identify sudden increases in the AMS-02 trigger rate with respect to the quiet conditions, demonstrating the potency of AMS-02 low-latency data for this real-time application.

Low latency BCH decoder using the affine polynomial over the finite field

The paper proposes a low latency BCH decoder with low complexity using parallel computation and simplifying locating errors by finding the roots of the affine polynomial over finite fields. The proposed design can be implemented on low-cost hardware platforms while applicable in very low latency information systems.

An Edge-AI Based Forecasting Approach for Improving Smart Microgrid Efficiency

Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and distributed systems that require an instantaneous two-way flow of energy information. Edge computing benefits from its proximity to the servers and edge nodes of the smart grid distributed systems, which can provide efficient and low latency information transmission to the smart grid. With the massive number of Internet of Things being used, the amount of real-time power usage information generated by that represents a huge challenge for edge computing. To improve the efficiency of information transmission and processing in power systems, this article combines different deep learning algorithms with edge computing to analyze and process distributed renewable energy generation and consumer power data in smart microgrid. Experiments on two real-world datasets from China and Belgium show that the proposed framework can obtain satisfactory prediction accuracy compared to existing approaches.

Low latency information transfer based on precision time synchronization via wireless interferometry

Low latency information transfer based on precision time synchronization via wireless interferometry

Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data — management and modelling applications

Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal–Wallis and post-hoc Tukey honestly significantly different tests, α = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles–Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north–south gradient (PERMANOVA, α = 0.01). A preliminary test of between-class differences in surface CO2 concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO2 with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources — from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a−1 for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression, α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.

Latency and Reliability of mmWave Multi-Hop V2V Communications Under Relay Selections

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs' instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.

LIDOR: A Lightweight DoS-Resilient Communication Protocol for Safety-Critical IoT Systems

IoT devices penetrate different aspects of our life including critical services, such as health monitoring, public safety, and autonomous driving. Such safety-critical IoT systems often consist of a large number of devices and need to withstand a vast range of known Denial-of-Service (DoS) network attacks to ensure a reliable operation while offering low-latency information dissemination. As the first solution to jointly achieve these goals, we propose LIDOR, a secure and lightweight multihop communication protocol designed to withstand all known variants of packet dropping attacks. Specifically, LIDOR relies on an end-to-end feedback mechanism to detect and react on unreliable links and draws solely on efficient symmetric-key cryptographic mechanisms to protect packets in transit. We show the overhead of LIDOR analytically and provide the proof of convergence for LIDOR which makes LIDOR resilient even to strong and hard-to-detect wormhole-supported grayhole attacks. In addition, we evaluate the performance via testbed experiments. The results indicate that LIDOR improves the reliability under DoS attacks by up to 91% and reduces network overhead by 32% compared to a state-of-the-art benchmark scheme.

Cached Files Updating Revisited: The Distribution of Popularity-Weighted Average File Age

Low-latency information transmission is required in 5G communication networks, where the cache is commonly used to speed up the data access. Meanwhile, a large number of IoT applications need real-time information exchange, resulting in the information freshness more important. In this article, under the discrete-time model, we consider the average age of all files in a cached-files-updating system. To keep the cached files fresh, in each time slot the server refreshes files with certain probabilities. The age of one file or its age of information (AoI) is defined as the time the file stays in cache since it was sent to cache last time. Assume that each file in cache has its own request popularity, we determine the probability distribution of the popularity-weighted average file age. For the random age of single file, both the mean and its probability distribution can be obtained by establishing a simple Markov chain. Following the same line of thinking, we show that an $N$ -dimensional stochastic process can be constituted to characterize the changes of $N$ file ages simultaneously. By solving the steady state of the resulting process, we obtain the explicit expression of stationary probability for an arbitrary state vector. Then, the distribution of the popularity-weighted average file age can be found by mergering a proper set of stationary probabilities, which gives the complete description of the average age. We also provide the numerical results of the distribution for some simple cases. The age-distribution can be utilized to compute certain stochastic performance indices, such as the AoI violation probability of some status updating systems.

Secure Identification Under Passive Eavesdroppers and Active Jamming Attacks

In next-generation connectivity systems, which rely on robust and low-latency information exchange, there exists communication tasks in which the Ahlswede/Dueck identification scheme is much more efficient than Shannon’s transmission scheme. We concentrate on the arbitrarily varying wiretap channel (AVWC) that models jamming attacks. We provide a coding scheme for secure identification and determine the secrecy capacity of the AVWC. Furthermore, we analyze important properties of this capacity function, e.g., continuity and super-additivity. These properties are important for the design of robust secure communication design and for the optimization of the medium access control.

Resilient and Low-Latency Information Acquisition for FiWi Enhanced Smart Grid

As distinct SG applications have different communication requirements in terms of reliability, latency, bandwidth, and so on, it is obviously infeasible to rely on a single communication technology for SG communications. Therefore, a hybrid communication architecture integrating both low latency fiber optic and flexible wireless technologies should be a promising candidate, which gives rise to the so-called FiWi enhanced SG. Note that SG, as a national strategic infrastructure, relies heavily on physical infrastructures, and are vulnerable to various failures. In such cases, the distribution management system of SG should be able to obtain the status of overall SG infrastructure as soon as possible, so as to carry out disaster relief for power restoration and prevention of further chain-style blackouts. Toward this end, we provide in this article an architecture of FiWi enhanced SG and study the problem of resilient and low-latency information acquisition under failures. Furthermore, we propose a heuristic greedy clustering algorithm as our solution for remaining sensors to find their near-optimal clustering paths.