Real-time Services Research Articles

Live demonstration of an SDN-reconfigurable, FPGA-based TxRx for an analog-IFoF/mmWave radio access network in an MNO’s infrastructure

This paper presents the end-to-end integration of a software defined network (SDN) reconfigurable real-time analog-intermediate frequency-over-fiber (A-IFoF)/millimeter wave (mmWave) layout into a real mobile network operator (MNO) infrastructure. The key part of the presented infrastructure is the fully programmable gateway array (FPGA)-based A-IFoF transceiver (TxRx), equipped with active SDN functionalities based on constant monitoring of the transport link. This layout was successfully evaluated in the physical data management and application layer. Regarding the physical layer performance evaluation, error vector magnitude values of 7.3% were achieved for quadrature phase shift keying waveforms, while the uninterrupted operation of real-time services such as augmented reality/virtual reality applications was enabled by the reactive SDN-based capacity reconfiguration capabilities.

Gradient transparency of Semi-IPN PVA/PVME electrospun nanofibrous membrane for estimating the remaining life of face mask

After suffering the COVID-19 viruliferous epidemic outbreak, wearing disposable protective masks has become an effective protective measure in our daily life. However, protection effectiveness of disposable protective masks will be greatly reduced or even lost once overstep their service life span. Thus, developing a strategy that can identify the remaining service life of masks by naked eyes becomes desirable. Here, we prepared a gradient transparent membrane as a view layer of masks by electrospinning the semi-interpenetration PVA/PVME membrane (semi-IPNM) which rapid responded to the respiration moisture. Based on the Darcy law by COMSOL software, with the increasing time of respiration simulation, the transparent area diffused from center to the edge of the membrane, like a progress bar revealing the remaining life of face mask. The resultant semi-IPNM exhibited excellent flexibility, nontoxicity, high tensile strength (34.5 MPa) and reliable filtration efficiency (greater than92%) even through 6 h using. Meanwhile, the pressure drop of semi-IPNM became higher after 6 h using, which was not meet up with human needs that indicated the disposable face mask should be discarded according to WS/T 311–2009 technical specification. Therefore, a view layer of face masks built by this strategy possesses a real-time service life management and an effective respiratory protection.

Positioning performance analysis of real-time BDS-3 PPP-B2b/INS tightly coupled integration in urban environments

Since 2020, the BeiDou-3 global navigation satellite system has provided real-time precision single-point positioning enhancement services (PPP-B2b). The real-time correction provideded by this service can make real-time precision single-point positioning achieve decimeter-level. However, a dense urban environment may cause frequent interruption of GNSS signals, and PPP-B2b alone cannot achieve continuous precision positioning. This study documents a study that attempts to combine PPP-B2b with an inertial navigation system (INS). A real-time tightly coupled integration model of PPP-B2b/INS based on PPP-B2b services was constructed, and a performance analysis of the tightly coupled integration positioning of PPP-B2b/INS in different urban environments was performed. The experiment shows that in an open-sky environment, the decimeter-level positioning accuracy (North: 31.7; East: 21.3; Up: 39.6 cm and North: 34.1; East: 27.6; Up: 41.0 cm) can only be achieved based on PPP-B2b (BDS only and GPS only), PPP-B2b/INS tightly coupled integration positioning accuracy (North: 17.2; East: 10.3; Up: 19.4 cm), and the convergence time is faster; In a dense urban environment, GNSS signals are frequently interrupted, and the positioning accuracy (North: 141.3; East: 95.2; Up: 156.6 cm and North: 147.5; East: 96.4; Up: 159.3 cm) increases significantly only based on PPP-B2b (BDS only and GPS only), and positioning interruption occurs frequently. However, PPP-B2b/INS tightly coupled integration could still achieve continuous decimetre-level positioning (North: 30.0; East: 28.4; Up: 46.3 cm).

Augmented reality participatory platform: A novel digital participatory planning tool to engage under-resourced communities in improving neighborhood walkability

Engaging communities and utilizing local knowledge can alleviate the disparities faced by under-resourced neighborhoods with regard to providing sufficient opportunities for walkability. Digital technologies, and in particular smartphones, have created new opportunities to improve community engagement by expanding the inclusion of diverse stakeholders in decision-making. This paper introduces a novel digital participatory planning tool called the Augmented Reality Participatory Platform (ARPP). The platform uses mobile augmented reality (mobile AR) to engage residents, particularly in under-resourced communities, in identifying the design improvements necessary to enhance neighborhood walkability. ARPP consists of a mobile AR app, cloud database, and real-time mapping service. It provides a platform for two-way communication between residents of these communities and decision-makers, with potential benefits for both groups. ARPP enables residents to interact with street features and walkable design interventions in situ, generate walking-related spatial data, and share them and possible design solutions with decision-makers. The platform also assists decision-makers with presenting walkable street design alternatives developed based on community feedback to residents. To determine if ARPP is useful and user-friendly, experts and a group of residents from an under-resourced community evaluated the usability of the mobile AR app. We found that the app largely met the usability goals, and after we addressed must-solve issues identified by the experts, community participants faced very few issues when interacting with it. With recent technological advances in mobile AR and its potential for planning and design, integrating mobile AR into participatory planning is timely.

Real-time marine PPP-B2b/SINS integrated navigation based on BDS-3

The BeiDou-3 global navigation satellite system was operated successfully in July 2020. It broadcasts precise point positioning (PPP)-B2b signals and provides real-time PPP services free of charge, which greatly meets the navigation needs of real-time users, especially warship navigation. This paper first introduces the PPP-B2b real-time positioning model, and then expounds the two-velocity measurement algorithm of Doppler and carrier phase time difference. Next, it combs the extended Kalman filter (EKF) and factor graph optimization (FGO) integrated navigation algorithm in detail, and finally comprehensively evaluates the real-time integrated navigation performance of PPP-B2b/strapdown inertial navigation system (SINS) through the measured data at sea. The experimental results show that the real-time dynamic positioning accuracy of PPP-B2b can reach the decimeter level, and the positioning accuracy is not significantly improved when integrated with SINS. Furthermore, dynamic alignment experiments results show after adding time-differenced carrier phase and Doppler velocimetry information, the convergence accuracy of yaw misalignment angle has been greatly improved. The accuracy of the EKF algorithm is 16.7% higher than that of the FGO algorithm after the convergence of the yaw angle error.

Load Forecasting with Machine Learning and Deep Learning Methods

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.

A novel secure and distributed architecture for privacy-preserving healthcare system

Patients often visit several hospitals to obtain medication, generating a significant volume of data. Moreover, hospitals use different data analytic techniques to improve healthcare services, leading to patient data privacy. However, no integrated architecture has a trustworthy data repository and secure communication protocols to store and share the data with various parties (e.g., hospitals and data analysts) for different healthcare services. This study proposes an innovative three-tier secure and distributed privacy-preserving healthcare architecture that addresses the aforementioned challenges. The first tier introduces a trustworthy data repository called custodian, where the data owner stores encrypted data and offer real-time privacy-preserving health monitoring service. The second tier provides Elliptic Curve Cryptography-based authentication for secure data exchange between the data owner and the hospital. The third tier utilizes smart contracts and local differential privacy (LDP) for secure machine learning model training. All transactions are recorded on the blockchain and managed by a smart contract. Security analysis shows that the proposed framework ensures privacy, security, and data integrity. Performance analysis is done based on score metrics, scalability metrics, and formal analysis. A transaction takes 0.00121 s in the first tier, and in the second tier, it takes 0.1267 s. The third tier uses ɛ−LDP with a privacy budget of ɛ = 3 on Random Forest and achieves 97%, 83%, and 74% accuracy on breast cancer, heart disease, and diabetic datasets. This accuracy is higher than the previous state-of-the-art methods. Moreover, the proposed healthcare framework ensures privacy, security and outperforms the previous state-of-the-art methods.

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

The 6th generation (6G) mobile communications technology will realize the interconnection of humans, machines, things as well as virtual space. The development of digital twins (DTs) and 6G has accelerated the Internet of Things (IoT) in an unprecedented way. The combination of DTs and edge intelligence (EI) enables powerful digital space synchronized with the real world constructed in the intelligent edge, bringing real-time, and adaptive services delivery of IoT. However, the dynamic features and heterogeneous resources in 6G-enabled IoT make the resource allocation for computation-intensive and delay-sensitive DTs services more challenging. In this paper, we first define the DTs implementation process as a DT service function chain (DTSFC) and address the resource allocation problem of DTs-empowered networks in form of dynamic DTSFCs orchestration. We further propose a novel collective reinforcement learning (CRL) method which is inspired by human collaboration, to realize the effective resource allocation of DTSFCs. Numerical results verify that the proposed CRL algorithm improves the learning efficiency and generalization ability compared with the benchmarks.

Maintaining real-time precise point positioning during outages of the corrections broadcast by BDS-3 global short-message communication

Real-time precise point positioning (RTPPP) is a popular technique for precision applications, which is based on real-time service (RTS) products. However, one serious problem for RTPPP users is the potential communication break for a period from a few minutes to an hour. A significant decline in positioning accuracy may occur since the receiving of the RTS corrections is discontinuous. In this study, the real-time orbit and clock corrections are converted into the equivalent range corrections and broadcast by BeiDou global navigation satellite system (BDS-3) global short-message communication (GSMC). We focus on maintaining global navigation satellite systems (GNSS) RTPPP during outages of the range corrections. Three prediction methods are presented, including “linear function”, “mean value” and “last value” methods. The predicted corrections will be carried out with only at most 5 latest epochs of the registered corrections. The “last value” method shows the optimal prediction performance and the prediction errors of Galileo are minimal compared to GPS and BDS. Both simulated and vehicle kinematic experiments are conducted to evaluate the performance of RTPPP with the predicted corrections. The results show that the three proposed predicted corrections can maintain centimeter-to decimeter-level accuracy in both static and kinematic mode during outages of the BDS-3 GSMC that may be from a few minutes to an hour. Using the last registered equivalent range correction value is the most recommended choice to maintain multi-GNSS RTPPP.

Location and Complex Status Update Strategy Optimization in UAV-Assisted IoT

Complex status updates have attracted widespread attention in real-time monitoring services (e.g., real-time fire gas monitoring and wildfire spread prediction). In complex status updates, the status information needs to be obtained by processing the perceived original data. However, as lightweight terminals, temporarily deployed Internet of things (IoT) devices have no computing modules. Unmanned aerial vehicle (UAV) can act as a edge server to help IoT devices complete computing tasks by mobile edge computing (MEC). To this end, this paper considers a complex status update in UAV-assisted IoT, where an UAV moves in hovering-flight-hovering mode to ensure that it can serve IoT devices in different areas. When the UAV hovers, it obtains the status information based on the original data transmitted by the IoT device and sends it to the control center. During the complex status update, the short packet communication and time-varying channel are considered. To realize the trade-off optimization of the average age of information (AoI) and average power consumption of both IoT device and UAV within a long time, we formulate a location and dynamic status update strategy optimization problem for UAV hovering-flight-hovering mode. In order to solve the problem with Markov properties, we derive the state probability equations and further establish the linear programming problem with fixed UAV location. Then, we propose a probability-based algorithm to obtain UAV location and dynamic status update strategy. To adapt to more urgent scenarios, we propose a AoI threshold based strategy to reduce the complexity of the problem. State probability equations are derived under the strategy and a linear programming problem with a fixed AoI threshold are established. Next, we propose a low-complexity algorithm to obtain the optimal AoI threshold. Simulation results show that the proposed algorithms can optimize the three performance metrics in a balanced way and we need to select the appropriate transmit power of the IoT device and the computing capacity of the UAV to achieve better performance.

Device–Edge Collaborative Differentiated Data Caching Strategy Toward AIoT

Caching AIoT data at the edge can reduce the load on cloud centers while providing real-time services for AIoT users. Existing static caching strategies based on popularity prediction fail to meet users’ demands for time-varying entity data, while dynamic caching strategies focus only on evaluating the time-varying state characteristics of entity data, but ignore the differences in popularity among entities, resulting in poor service experience. To this end, a device-edge collaborative differentiated data caching strategy considering static entity popularity as well as dynamic system state is proposed. First, a hot entity recognition method centered on user interests is designed to achieve individual preference estimation by mining users’ long short-term interests, and then achieve group interest prediction based on social computing. Based on this, a dynamic caching optimization method is designed, which considers the timeliness of entity data and communication cost of system to design the objective function of optimal cache decision and then solve it based on reinforcement learning. Simulation results demonstrate that the proposed caching strategy achieves better performance than other benchmark strategies in terms of cache hit rate and search cost.

SUMO Roundabout Simulation with Human in the Loop

Traffic simulators rely on calibrated driver models in order to reproduce human behavior in different traffic scenarios. Even if quite accurate results can be obtained, the actual interaction between human being and traffic cannot be completely reproduced. In particular, as automated vehicles are being developed, the human in the loop is required to understand whether drivers feel comfortable and safe in mixed traffic conditions. In recent years, dynamic driving simulators have been developed to test vehicles in complex or dangerous situations in safe and controlled environments. However, driving simulators are mostly devoted to the study of vehicle dynamics more than traffic situations.This paper presents an integration of SUMO with a high end dynamic driving simulator with the aim to study human reactions while negotiating a roundabout in mixed traffic conditions. SUMO is in charge of traffic simulation, while a full vehicle model is employed for the simulation of the dynamic of the human driven car. To allow a human to effectively drive the car, both simulation environments have to run in real time while exchanging the required information. Also, scenario graphics, sound and driving simulator feedback motion have to be accurately realized and synchronized with the simulations. A real-time server is employed for the synchronization of the different environments. As SUMO does not consider vehicle dynamics, particular attention is devoted to the a realistic reconstruction of trajectories and vehicle dynamics to be represented in the scenario. Some preliminary tests are shown where a panel of testers has been asked to negotiate the roundabout with different percentages of automated vehicles. The results of the tests show that drivers were able to perceive differences in the behavior of other vehicles and that the proposed approach is effective for understanding the feeling of comfort and safety of the human driver.

The real-time dynamic online feeder service with a maximum headway at mandatory stops

On the one hand, fully flexible demand-responsive feeder services efficiently tailor their service to passengers' needs. Traditional services, on the other hand, offer predictability and easier cost control. This paper considers a semi-flexible feeder service that combines positive characteristics of both traditional and fully flexible services. There are two types of bus stops in this service. Mandatory bus stops have a maximum allowable headway for bus departures. Optional stops are only visited when there is demand for transportation nearby. When new passenger requests arrive, the performance of this feeder service is optimised in real time. A metaheuristic with two phases is developed to optimise the service. The dynamic optimisation method is compared to a model that optimises the service when all requests are known beforehand. The results show that the dynamic method has an average gap of 6.5% with respect to the static model and an average acceptance rate of 95.1%. A case study in the city of Antwerp shows that, when compared to existing transit options in the region, this feeder service can increase the service quality by 31.6% when enough resources are available.

Intelligent Safety Risk Analysis and Decision-Making System for Underground Metal Mines Based on Big Data

In view of the new situation faced by safety risk management in underground metal mines, based on a comprehensive analysis of the current situation of mine safety management business and system construction requirements, the main functional modules, overall architecture, and data interaction mode of the intelligent safety risk analysis and decision-making system were analyzed and designed. On the basis of elaborating the implementation process of the main functional modules of the system, such as multi-source safety information collection and governance, and safety risk intelligence analysis and visualization, a safety risk intelligence analysis and decision-making system was constructed, which provided efficient and real-time intelligent application and analysis services for safety in the production of underground metal mines and realized the whole process information management of collection, aggregation, processing, analysis, and visual display of multi-source mine safety risk information. The application of the system has made an essential change in the mode of mine safety risk management, realizing the active safety management goal of shifting safety risk management from post-analysis to pre-prevention, helping to improve the pertinence and efficiency of safety risk management, and greatly reducing the risk of mine safety accidents.

On the role of lidar measurements in speeding up precise point positioning convergence

Global navigation satellite system (GNSS) and light detection and ranging (lidar) are well known to be complementary for vehicle positioning in urban canyons, where GNSS observations are prone to signal blockage and multi-path. As one of the most common carrier-phase-based precise positioning techniques, precise point positioning (PPP) enables single-receiver positioning as it utilizes state-space representation corrections for satellite orbits and clocks and does not require a nearby reference station. Yet PPP suffers from a long positioning convergence time. In this contribution, we propose to reduce the PPP convergence using an observation-level integration of GNSS and lidar. Lidar measurements, in the form of 3D keypoints, are generated by registering online scans to a pre-built high-definition map through deep learning and are then combined with dual-frequency PPP (DF-PPP) observations in an extended Kalman filter implementing the constant-velocity model that captures the vehicle dynamics. We realize real-time PPP (RT-PPP) in this integration using the IGS real-time service products for vehicle positioning. Comprehensive analyses are provided to evaluate different combinations of measurements and PPP corrections in both static and simulated kinematic modes using data captured by multiple receivers. Experimental results show that the integration achieves cm-level accuracy and instantaneous convergence by using redundant measurements. Accordingly, for classical PPP accuracy of 10 cm and convergence within minutes, respectively, lidar input is only required once every 10 s.

HUBO and QUBO models for prime factorization

The security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers p and q satisfying N=ptimes q. This paper presents a prime factorization method using a D-Wave quantum computer that could threaten the RSA cryptosystem in the future. The starting point for this method is very simple, representing two prime numbers as qubits. Then, we set the difference between the product of the two prime numbers expressed in qubits and N as a cost function, and we find the solution when the cost function is minimized. D-Wave's quantum annealer can find the minimum value of any quadratic problem. However, the cost function must be a higher-order unconstrained optimization (HUBO) model because it contains second- or higher-order terms. We used a hybrid solver accessible via Leap, D-Wave’s real-time quantum cloud service, and the dimod package provided by the D-Wave Ocean software development kit (SDK) to solve the HUBO problem. We also successfully factorized 102,454,763 with 26 logical qubits. In addition, we factorized 1,000,070,001,221 using the range-dependent Hamiltonian algorithm.

Digital Twin Concept, Method and Technical Framework for Smart Meters

Smart meters connect smart grid electricity suppliers and users. Smart meters have become a research hotspot as smart grid applications like demand response, power theft prevention, power quality monitoring, peak valley time of use prices, and peer-to-peer (P2P) energy trading have grown. But, as the carriers of these functions, smart meters have technical problems such as limited computing resources, difficulty in upgrading, and high costs, which to some extent restrict the further development of smart grid applications. To address these issues, this study offers a container-based digital twin (CDT) approach for smart meters, which not only increases the user-facing computing resources of smart meters but also simplifies and lowers the overall cost and technical complexity of meter changes. In order to further validate the effectiveness of this method in real-time applications on the smart grid user side, this article tested and analyzed the communication performance of the digital twin system in three areas: remote application services, peer-to-peer transactions, and real-time user request services. The experimental results show that the CDT method proposed in this paper meets the basic requirements of smart grid user-side applications for real-time communication. The container is deployed in the cloud, and the average time required to complete 100 P2P communications using our smart meter structure is less than 2.4 seconds, while the average time required for existing smart meter structures to complete the same number of P2P communications is 208 seconds. Finally, applications, the future development direction of the digital twin method, and technology architecture are projected.

Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization

This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-based decision tree strategy for smart meters meets the smart grid's real-time communication requirements for user-side applications.

A Fast and Secured Vehicle-to-Vehicle Energy Trading Based on Blockchain Consensus in the Internet of Electric Vehicles

The organization and management of electricity markets worldwide are rapidly evolving, moving towards decentralized, distributed, and renewable energy-based generation with solutions based on real-time data exchange. A Vehicle-to-Vehicle (V2V) energy trading has emerged as one of the most promising alternatives for relieving the load imposed on the traditional grid enabling two individuals to buy and sell energy directly without intermediaries. However, the Internet of Electric Vehicles (IoEV) environment is trustless, and such P2P energy trading is prone to different kinds of cyber attacks. Blockchain technology has lately been proposed to implement V2V energy trading to securely and fairly share energy. The consensus mechanism is one of the most important modules of blockchain applied to the V2V network. It determines the efficiency and security among untrustworthy EVs of the energy trading blockchain (ETB). Nevertheless, most works on ETB have currently adopted traditional consensus mechanisms. Due to high computing power and communication overhead, these consensus algorithms are unsuitable for applications requiring real-time services such as energy trading. The efficient and secured Hashgraph is the revolutionary technology of consensus in blockchain and a promising technology suitable for V2V energy trading with frequent transactions. However, Hashgraph does not support the dynamic addition and deletion of nodes and is completely decentralized and vulnerable to Sybil Attack in a large-scale blockchain. Furthermore, this “complete decentralization” model may result in states losing the ability to macro-control the energy industry and even systemic energy security issues. Therefore, we propose a Block Alliance Consensus (BAC) mechanism to solve these problems. BAC can maintain the throughput of Hashgraph and resist Sybil Attack in a large-scale P2P energy trading network. We design a cryptography-based leader election mechanism and adopt a reputation incentive mechanism to motivate honest and cooperative electric vehicles (EVs). Finally, we implement ETB and the BAC consensus mechanism on the Hyperledger Fabric platform. The high efficiency and security of BAC and the blockchain-based V2V energy trading platform are verified through experiments.

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).