Real-time Services Research Articles

Privacy-Preserving Sports Wearable Data Fusion Framework.

When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.

클라우드 환경에서 블록체인을 이용한 포그 기반 IoT 서비스 상호운용 시스템

사물 클라우드(CoT, Cloud of Things)는 사물인터넷(IoT, Internet of Things) 애플리케이션에 클라우드 서비스가 지원하는 무제한 저장기능과 처리능력을 제공할 수 있다. 그러나, 중앙 집중식 사물 클라우드에서는 병목 문제, 사물 클라우드 네트워크의 중단으로 이어질 수 있는 단일 장애 지점을 발생시킬 수 있다. 본 논문에서는 중앙 집중식 사물 클라우드의 문제를 해결하고 서로 다른 서비스 도메인 간 상호운용을 위하여, 분산 포그 컴퓨팅과 블록체인 기술을 적용한 IoT 서비스 상호운용 시스템을 제안한다. 분산 포그를 사용하여 IoT 장치와 지역적으로 가까운 거리에 위치한 포그 시스템에서 실시간 데이터 처리 및 서비스를 제공하고, 블록체인의 스마트 컨트렉트와 분산 원장을 이용하여 각 포그간에 서비스 상호운용이 가능하도록 한다. 제안 시스템은 클라우드로부터 서비스를 위임받은 분산 포그에서 가까운 지역 내 서비스를 제공하며, 포그 간에도 클라우드를 거치지 않고, 다른 포그의 서비스를 접근할 수 있다. 또한, 블록체인 네트워크상에서 클라우드와 포그 노드들은 서비스 권한 토큰 발행 정보를 공유함으로써 토큰에 대한 무결성을 보장하고 포그 노드들 간 신뢰할 수 있는 서비스 상호운용이 수행될 수 있다.Cloud of Things (CoT) can provide IoT applications with unlimited storage functions and processing power supported by cloud services. However, in a centralized cloud of things, it can create a single point of failure that can lead to bottleneck problems, outages of the CoT network. In this paper, to solve the problem of centralized cloud of things and interoperate between different service domains, we propose an IoT service interoperability system using distributed fog computing and blockchain technology. Distributed fog is used to provide real-time data processing and services in fog systems located at a geographically close distance to IoT devices, and to enable service interoperability between each fog using smart contracts and distributed ledgers of the blockchain. The proposed system provides services within a region close to the distributed fog entrusted with the service from the cloud, and it is possible to access the services of other fogs without going through the cloud even between fogs. In addition, by sharing a service right token issuance information between the cloud and fog nodes using a blockchain network, the integrity of the token can be guaranteed and reliable service interoperability between fog nodes can be performed.

Monitoring the safety status of service bolts in mining roadways.

To monitor the safety status of the bolts in coal mining roadways in real time, the safety and stability of the bolt support structure were evaluated. Based on the conventional support bolts used in the field, a fiber Bragg grating (FBG) sensor and medium materials were selected. Through theoretical analysis, the bolt tension, and FBG temperature tests, the strain transmission mechanism of the FBG bolt was analyzed, and it was ensured that the developed FBG bolt could accurately measure the strain of the bolt. In the field test, FBG bolts were arranged on the positive and negative sides of the mining roadway to accurately monitor the safety status of the bolts in service in real time, and the force characteristics of the bolts monitored by the FBG sensor were analyzed to obtain the maximum axial force of the positive and negative bolts. Thereafter, the safety status of the roadway bolt was evaluated. The results show that the positive side bolts axial force change is significantly greater than that of the negative side bolt; with the working face advancing to a distance of 60 m from the bolt as the dividing line, the positive side bolts axial force grows slowly before this, after which the axial force increases rapidly. The locations of the roadway where the positive and negative bolts are most affected by mining are determined, and roadway support and prevention measures for this location should be conducted in time. The safety status of the bolts is evaluated and monitored as follows: the positive side No. 2, No. 3, No. 5, and No. 6 bolts have reached the failure state, the positive side No. 4 bolt is in a dangerous state, the positive side No. 1, negative side No. 8 and No. 9 are in an abnormal state, and the negative side No. 7, No. 10, No. 11, and No. 12 are in a normal condition. This research has laid a technical foundation for the real-time monitoring of the bolt support of the mining roadway and the assessment of the safety status of bolts.

Extending MQTT with Real-Time Communication Services Based on SDN.

MQTT is one of the most popular application-layer protocols used in the scope of the Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT), given its suitability for resource-constrained embedded systems. However, MQTT Quality-of-Service policies do not support timeliness requirements, which is common in IIoT. The literature reports several research works that address this limitation, but they are limited in scope (e.g., improvements in the broker’s internal operation, control of the publisher’s data rate, and path optimizations). Conversely, this paper presents a comprehensive architectural approach, proposing a set of extensions to the MQTT protocol that allow applications to explicitly specify real-time requirements and instantiate corresponding network reservations to enforce the desired temporal behavior. Such reservations are enforced via Software Defined Networking, specifically the OpenFlow protocol, but other protocols that allow bandwidth reservations, e.g., TSN, can also be used. This paper presents the proposed system architecture together with extensive emulation and implementation results that validate the feasibility of the approach, showing that time-sensitive MQTT traffic can be effectively segregated and prioritized to meet application-defined real-time requirements. Using several combinations of network topologies and load levels and comparing to the absence of the proposed real-time mechanisms, both average and worst-case latencies of the time-sensitive traffic decreased to approximately half, while for the normal traffic, they increased by approximately 10%.

Uplink Channel Allocation Scheme and QoS Management Mechanism for Cognitive Cellular-Femtocell Networks

Cognitive radio and femtocell are promising technologies which can satisfy the requirements of future mobile communications in terms of dynamic spectrum sharing and high user density areas. Providing quality-of-service (QoS) guaranteed realtime services is challenging issue of future cognitive cellular-femtocell mobile networks. In this paper, we introduce a user’s QoS management mechanism used to protect SINR of macro users from QoS violation caused by femtocell users. We design a novel uplink channel allocation scheme (denoted as “flexible scheme”) for real-time connections. The scheme uses the information of interference level and channel occupancy collected at cognitive femtocell access points and their covering macro base station (MBS) and apply relevant selection criteria to select an appropriate channel which causes the minimums interference to macro users of the covering MBS. Performance results prove that comparing with femtocell-access-point (FAP)-based and MBS-based uplink channel allocation schemes, the novel “flexible scheme” can provide lower unsuccessful probability of new connection requests.

Farklı troposfer modellerinin gerçek zamanlı çoklu-GNSS PPP performansına etkisi

With the initialization of IGS (International GNSS Service) real-time service products, real-time Precise Point Positioning (PPP) applications have been a popular topic within the GNSS (Global Navigation Satellite Systems) users. The impact of the troposphere on GNSS signals is one of the most crucial error sources regarding the real-time PPP solution. In the PPP technique, the dry component of tropospheric delay is usually corrected by means of empirical models, while its wet component is estimated as an unknown parameter in the adjustment process. Hence, the troposphere model employed in the PPP solution has a considerable impact on the performance of the obtained solution. Therefore, the main objective of this study is to investigate the impact of different troposphere models on the performance of real-time multi-GNSS PPP. As a part of this study, four different troposphere models, where Saastamoinen and Hopfield models that are most frequently used in GNSS solutions are used together with GPT (Global Pressure and Temperature) 2 and 3 models separately, were constituted. In this context, the observation dataset acquired from a total of 16 different IGS stations over a ten-day period of December 19-28, 2021, were processed utilizing four different troposphere models. In addition to the positioning performance, PPP solutions were also analyzed in terms of zenith total delay (ZTD) estimation. The results show that the best positioning performance can be obtained when the Saastamoinen model is used in combination with GPT3. The three-dimensional positioning accuracy acquired from the corresponding solution is 2.72 cm, which is better than the closest solution by a ratio of 9.2%. Besides, in terms of the ZTD estimation, the best performance is achieved again in the case when the Saastamoinen model is used in combination with GPT3. For the related solution, the accuracy of ZTD estimation is calculated as 1.24 cm and this value indicates a better performance by a ratio of 10.2% compared with the closest solution.

An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

User’s Activity Classification And Domain Wise Ranking Through Social Interactions

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

Distributed service placement in hierarchical fog environments

The fog computing paradigm emerged as a promising solution to realize deployment of large-scale Internet of Things (IoT) environments and low latency real-time services. It leverages a large number of resource-constrained, heterogeneous compute nodes distributed across vast geographical areas and located closer to users and data sources, as compared to the cloud which is usually located at large data centers, far from users and IoT devices. In an infrastructure environment with the interplay of cloud and fog nodes, there is a need for efficient placement of services to satisfy resource requirements as well as improve various factors such as node utilization, network utilization, computation cost, communication cost, energy consumption, response time, availability, and load balancing. In this paper, we propose a scalable, low-overhead, fully distributed approach to select a cost-efficient fog node, considering both computation and communication costs, from the set of prospective fog nodes to host the given application service. We have implemented the solution in a simulation environment and compared its performance with several approaches along with a centralized approach. © 2022 Elsevier Science. All rights reserved.

Artificial intelligence for edge service optimization in Internet of Vehicles: A survey

The Internet of Vehicles (IoV) plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information. Generally, collected information is transmitted to a centralized resource-intensive cloud platform for service implementation. Edge Computing (EC) that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users. Additionally, many measures are adopted to optimize the performance of EC-enabled IoV, but they hardly help make dynamic decisions according to real-time requests. Artificial Intelligence (AI) is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically. Although extensive research has employed AI to optimize EC performance, summaries with relative concepts or prospects are quite few. To address this gap, we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV. Firstly, we establish the general condition and relative concepts about IoV, EC, and AI. Secondly, we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading. Finally, we discuss a number of open issues in optimizing edge services with AI.

Beyond 5G: Reducing the handover rate for high mobility communications

The fifth-generation (5G) and beyond cellular networks are expected to support a huge number of mobile devices, roaming seamlessly across very small cells. Consequently, the handover rate for these extremely dense networks is expected to be very high. To reduce the burden caused by rapid handover requests, and to support a massive number of highly mobile devices in 5G and beyond networks, this study suggests using proximity-based clusters as nomadic cells integrated with Aerial Access Networks (AANs). These nomadic cells are formed by two-levels hierarchical partitioning of the mobile devices into proximity-based clusters. Previous distributed mobility management schemes are not sufficiently efficient to support the handover rate expected for 5G and beyond networks. Due to their high computational complexity, previous group-based methods are not applicable for real-time services. In contrast to these schemes, the proposed scheme is scalable with the number of devices. Moreover, the creation of a mobility group raises practical as well as security and privacy issues that were overlooked by previous schemes. These issues are addressed in this study.

FedSky: An Efficient and Privacy-Preserving Scheme for Federated Mobile Crowdsensing

Mobile crowdsensing (MCS) is a newly emerged sensing paradigm, where a large group of mobile workers collectively sense and share data for real-time services. However, one major problem that hinders the further development of MCS is the potential leakage of workers’ data privacy. In this article, we integrate federated learning (FL) with MCS and introduce a novel sensing system, called federated MCS (F-MCS). In F-MCS, the workers can optimize the global model while keeping all the sensitive training data locally, thus ensuring their data privacy. Nevertheless, there are still two major issues in F-MCS. The first issue is that in F-MCS services, the workers are heterogeneous in terms of computational capacities and data resources. Hence, qualified workers should be appropriately selected to improve the efficiency of the training process. The second issue is that F-MCS is a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-device</i> FL system, where the platform will finally get the global model after multiple training rounds. However, most privacy-preserving techniques are designed for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross-silo</i> FL platforms, which cannot be applied to real-world F-MCS scenarios. To tackle the above problems, in this article, we propose a privacy-preserving scheme for F-MCS, namely, FedSky. Mainly, by extending the classic FedAvg algorithm, FedSky selects qualified workers based on the constrained group skyline (CG-skyline) and securely aggregates model updates based on the homomorphic encryption technique. Comprehensive security analysis demonstrates the privacy preservation of FedSky. Extensive experiments are conducted on an image classification task, where the comparison results validate the proposed scheme’s efficiency and effectiveness.

A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties

Recently, there has been growing interest in the provision of market services from distributed energy resources (DERs). In pursuing this goal, demand response (DR) aggregators continue to face challenges in retaining privacy and comfort for end-users, mitigating scalability issues while controlling a large cohort of DERs and handling uncertainties which are inevitable in a practical setting. This paper presents an end-user privacy and comfort preserving, scalable, hierarchical control scheme for inverter-type air conditioners to provide real-time market services in the presence of uncertainties. Privacy and scalability are achieved thorough the adoption of the alternating direction method of multipliers (ADMM) framework which ensures minimal reliance on local information whilst ensuring desired reference tracking without compromising the end-user comfort. Benefiting from the proposed non-conservative robust MPC design, the local control is able to account for mismatches in outdoor temperature predictions. The overall scheme is validated using real data obtained from the Australian Energy Market operator. The results demonstrate that the proposed approach can achieve desired tracking of the reference signal while regulating indoor temperature within a narrow range (±1 °C) from the nominal set-point. Besides, the robustness to uncertainties is achieved without compromising computational performance and therefore the approach is scalable.

Study on the Development Path of Smart Elderly Care in Guangxi

Smart elderly care refers to the provision of real-time and efficient elderly care services to the elderly based on Internet technology and intelligent equipment such as big data. In the development of traditional elderly care service, Guangxi witnessed slow development in its foundation and lags behind in infrastructure construction. Smart elderly care provides a new path for the development of Guangxi elderly care industry. This study investigated the demand of the elderly in Guangxi for elderly care services, Based on the current development of elderly care services in Guangxi, three smart elderly care paths that adapt to the development characteristics of the region is proposed: community grid management + community elder security, smart home + home care, Internet + hospital health care + home care.

A study on the real-time code-based GNSS positioning with Android smartphones

This study concentrates on the real-time standalone positioning that depends on single-frequency code observations with three smartphones, Xiaomi Mi 8, Google Pixel 4, and Pixel 4 XL, in the kinematic environment. For real-time positioning, two positioning approaches, which respectively employ ultra-rapid and IGS real-time service (IGS-RTS) products, are adopted in this study. Also, this study provides an advanced processing strategy that utilizes the variance component estimation method in determining the multi-GNSS observation weights. The experimental tests reveal that compared with ultra-rapid products, the use of IGS-RTS products augments the positioning accuracy of multi-GNSS solution by 5.4, 11.0, and 11.2% for Mi 8, Pixel 4, and Pixel 4XL smartphones. Also, Xiaomi Mi 8 can provide positioning accuracy of 7 m with the advanced processing strategy that improves the positioning performance of smartphones by 11.0% on average. Still, the improvement rate of the advanced processing strategy can reach 22.1% in some cases.

Facilitating Smart City Development through Adaption of the Learnings from Enterprise Systems Integration

Cross-sectoral city systems integration is a necessity for Smart City Development (SCD) to provide real-time services to citizens. However, there is scant knowledge about integrating city systems, while the existing literature is replete with learnings on systems integration from private enterprises. Hence, the wisdom of Enterprise Systems Integration (ESI) can be utilised to integrate systems for SCD. Yet, there is limited knowledge on the association between these two contexts. This research aims to develop an innovative model, referred to as ‘AdaptModel’, that enables the adaption of the ESI learnings for SCD based on the differences and similarities between city and enterprise through qualitative research utilising literature analysis, semi-structured interviews, and document analysis. The findings show that the difference between cities and private enterprises from a systems integration viewpoint is in degree rather than in kind. Hence, using ESI learnings for the SCD context is valuable and informative, but it would be more complex, bureaucratic, time-consuming, and expensive. The research offers guidance in addressing the systems integration challenges associated with SCD based on interrelations between SCD and ESI; this is carried out by applying a novel approach of contextualising and modifying the ESI learnings to support cross-sectoral city systems integration.

The Systematic Literature Review of Privacy-Preserving Solutions in Smart Healthcare Environment

The rapid development of the Internet of Medical Things (IoMT) technology has resulted in various advances in the smart healthcare field; it improves healthcare systems to offer more complicated real-time services and provides an efficient patient motioning system. However, despite the brilliant side of IoMT, several concerns continue to undercut its adoption. In fact, collecting, transmitting, storing, and using data in IoMT applications raises issues regarding privacy and data protection, especially with the multitude of stakeholders involved during the whole data life cycle. Motivated from these facts, this article is devoted to perform a Systematic Literature Review (SLR) of privacy-preserving solutions used in the smart healthcare ecosystem. The recent research papers disseminated between 2017 and 2021 are selected from multiple databases and a standardized SLR method is conducted. A total of 100 papers were reviewed and a critical analysis was conducted on the selected papers. Moreover, this review study attempts to highlight the limitation of the current approaches and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers in terms of the privacy techniques they deployed, the data life cycle phase they addressed, the stakeholders needs they met, and the privacy principles they covered according to privacy laws and regulations. Finally, we summarize our results showing privacy-preserving trends and identifying recommendations to involve privacy principles coverage in smart healthcare applications.

To trade or not to trade: Simultaneously optimising battery storage for arbitrage and ancillary services

This work presents a novel methodology for determining the value a battery storage system provides while participating in a competitive frequency response market, considering uncertainties. Battery storage systems are an attractive choice for power services in low-carbon electricity grids and their optimal operation are a commonly studied matter. However, the non-deterministic nature of competitive electricity markets is often overlooked. Here, we consider these market uncertainties for a storage device providing Great Britain’s Firm Frequency Response (FFR) and arbitrage services. We use a machine learning classifier to determine the set of all possible FFR market outcomes and their associated probabilities. These are then propagated through a linear optimisation model to generate a set of possible scenarios, from which the most likely can be ascertained. Several different classifiers and bidding strategies are compared, the most suitable classifier and bidding strategies which maximise revenue whilst minimising the probability of the worse-case scenario are identified. It is found that the mean expected income is overestimated by ∼28% when uncertainties in FFR market outcomes are not considered. Providing arbitrage over a tight band can still provide significant income and does not impede on the storage’s ability to provide FFR services in real time.

A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System.

In the present medical age, the focus on prevention and prediction is achieved using the medical internet of things. With a broad and complete framework, effective behavioral, environmental, and physiological criteria are necessary to govern the major healthcare sectors. Wearables play an essential role in personal health monitoring data measurement and processing. We wish to design a variable and flexible frame for broad parameter monitoring in accordance with the convenient mode of wearability. In this study, an innovative prototype with a handle and a modular IoT portal is designed for environmental surveillance. The prototype examines the most significant parameters of the surroundings. This strategy allows a bidirectional link between end users and medicine via the IoT gateway as an intermediate portal for users with IoT servers in real time. In addition, the doctor may configure the necessary parameters of measurements via the IoT portal and switch the sensors on the wearables as a real-time observer for the patient. Thus, based on goal analysis, patient situation, specifications, and requests, medications may define setup criteria for calculation. With regard to privacy, power use, and computation delays, we established this system's performance link for three common IoT healthcare circumstances. The simulation results show that this technique may minimize processing time by 25.34%, save energy level up to 72.25%, and boost the privacy level of the IoT medical device to 17.25% compared to the benchmark system.

Computer Architectures for Incremental Learning in Water Management

This paper presents an architecture and a platform for processing of water management data in real time. Stakeholders in the domain are faced with the challenge of handling large amounts of incoming sensor data from heterogeneous sources after the digitalization efforts within the sector. Our water management analytical platform (WMAP) is built upon the needs of domain experts (it provides capabilities for offline analysis) and is designed to solve real-world problems (it provides real-time data flow solutions and data-driven predictive analytics) for smart water management. WMAP is expected to contribute significantly to the water management domain, which has not yet acquired the competences to implement extensive data analysis and modeling capabilities in real-world scenarios. The proposed architecture extends existing big data architectures and presents an efficient way of dealing with data-driven modeling in the water management domain. The main improvement is in the speed (online analytics) layer of the architecture, where we introduce heterogeneous data fusion in a set of data streams that provide real-time data-driven modeling and prediction services. Using the proposed architecture, the results illustrate that models built with datasets with richer contextual information and multiple data sources are more accurate and thus more useful.