Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed

UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. This paper provides a guide to the implemented and prospective artificial intelligence (AI) capabilities of UMBRELLA in real-world IoT systems. Four existing UMBRELLA applications are presented in detail: 1) An automated streetlight monitoring for detecting issues and triggering maintenance alerts; 2) A Digital twin of building environments providing enhanced air quality sensing with reduced cost; 3) A large-scale Federated Learning framework for reducing communication overhead; and 4) An intrusion detection for containerised applications identifying malicious activities. Additionally, the potential of UMBRELLA is outlined for future smart city and multi-robot crowdsensing applications enhanced by semantic communications and multi-agent planning. Finally, to realise the above use-cases we discuss the need for a tailored MLOps platform to automate UMBRELLA model pipelines and establish trust.Comment: 6 pgaes, 4 figures. This work has been accepted by PerCom TrustSense workshop 202

A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array. The superposition of multiple single-reflection profiles enables multi-reflection for distributed users. However, the optimization challenges from periodic element arrangements in single-reflection and multi-reflection profiles are understudied. The combination of periodical single-reflection profiles leads to amplitude/phase counteractions, affecting the performance of each reflection beam. This paper focuses on a dual-reflection optimization scenario and investigates the far-field performance deterioration caused by the misalignment of overlapped profiles. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based optimization method. Comparative experiments against random and exhaustive searches demonstrate that our proposed DRL method outperforms both alternatives, achieving the shortest optimization time. Remarkably, our approach achieves a 1.2 dB gain in the reflection peak gain and a broader beam without any hardware modifications.Comment: 6 pages, 6 figures. This paper has been accepted for presentation at the VTC2023-Fal

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems

Federated learning (FL) systems face performance challenges in dealing with heterogeneous devices and non-identically distributed data across clients. We propose a dynamic global model aggregation method within Asynchronous Federated Learning (AFL) deployments to address these issues. Our aggregation method scores and adjusts the weighting of client model updates based on their upload frequency to accommodate differences in device capabilities. Additionally, we also immediately provide an updated global model to clients after they upload their local models to reduce idle time and improve training efficiency. We evaluate our approach within an AFL deployment consisting of 10 simulated clients with heterogeneous compute constraints and non-IID data. The simulation results, using the FashionMNIST dataset, demonstrate over 10% and 19% improvement in global model accuracy compared to state-of-the-art methods PAPAYA and FedAsync, respectively. Our dynamic aggregation method allows reliable global model training despite limiting client resources and statistical data heterogeneity. This improves robustness and scalability for real-world FL deployments.Comment: 6 pages, 5 figures. This work has been accepted by PerCom PerconAI workshop 202

RLOps:Development Life-cycle of Reinforcement Learning Aided Open RAN

Radio access network (RAN) technologies continue to witness massive growth, with Open RAN gaining the most recent momentum. In the O-RAN specifications, the RAN intelligent controller (RIC) serves as an automation host. This article introduces principles for machine learning (ML), in particular, reinforcement learning (RL) relevant for the O-RAN stack. Furthermore, we review state-of-the-art research in wireless networks and cast it onto the RAN framework and the hierarchy of the O-RAN architecture. We provide a taxonomy of the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.). To address the challenges, we integrate a set of existing MLOps principles with unique characteristics when RL agents are considered. This paper discusses a systematic life-cycle model development, testing and validation pipeline, termed: RLOps. We discuss all fundamental parts of RLOps, which include: model specification, development and distillation, production environment serving, operations monitoring, safety/security and data engineering platform. Based on these principles, we propose the best practices for RLOps to achieve an automated and reproducible model development process.Comment: 17 pages, 6 figrue

Distributed Sensing, Computing, Communication, and Control Fabric: A Unified Service-Level Architecture for 6G

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardization and pre-standardization landscape

Mediating effects of attitude on the relationship between knowledge and willingness to organ donation among nursing students

BackgroundThe current rate of organ donation in China falls significantly below the global average and the actual demand. Nursing students play a crucial role in supporting and promoting social and public welfare activities. This study primary aims to analyze the levels of knowledge, attitudes, willingness toward organ donation, and attitudes toward death among nursing students, and investigate the mediating role of attitude in the relationship between knowledge and willingness. The secondary aims to identify factors that may influence the willingness.MethodsA convenience sample of nursing students completed online-administered questionnaires measuring the level of knowledge, attitudes, and willingness toward organ donation before and after clinical internship. Spearman correlation and mediation analyses were used for data analyses.ResultsBefore the clinical internship, there were 435 nursing students who had not yet obtained their degrees and were completing their clinical internships. After the internship, this number decreased to 323. The mean score for knowledge before and after the clinical internship (7.17 before and 7.22 after, with no significant difference), the attitude (4.58 before and 4.36 after, with significant difference), the willingness (12.41% before and 8.67% after, with significant difference), the Death Attitude Profile-Revised (DAP-R) score (94.41 before and 92.56 after, with significant difference). The knowledge indirectly affected nursing students’ willingness to organ donation through attitude. Knowledge had a direct and positive impact on attitudes (β = 1.564). Additionally, nursing students’ attitudes positively affected their willingness (β = 0.023). Attitudes played a mediating role in the relationship between knowledge and willingness (β = 0.035). Additionally, attitude toward death, fear of death, and acceptance of the concept of escape were found to be correlated with their willingness.ConclusionOrgan donation willingness was found to be low among nursing students. Positive attitudes were identified as a mediating factor between knowledge and willingness. Additionally, DAP-R was a related factor. Therefore, it is recommended to focus on improving knowledge and attitude, as well as providing death education to help nursing students establish a positive attitude toward death. These efforts can contribute to the promotion of organ donation

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

BackgroundAlthough studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.MethodsTranscriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.ResultsNineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.ConclusionPyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma

Design and Optimization of a Liquid Cooling Thermal Management System with Flow Distributors and Spiral Channel Cooling Plates for Lithium-Ion Batteries

In this study, a three-dimensional transient simulation model of a liquid cooling thermal management system with flow distributors and spiral channel cooling plates for pouch lithium-ion batteries has been developed. The cooling plates play the role of uniforming temperature distribution and reducing the maximum temperature within each battery, while the flow distributors have the function of reducing the temperature difference between batteries in the battery module. The accuracy of the thermophysical properties and heat generation rate of the battery was verified experimentally. The optimal structure and cooling strategy of the system was determined by single factor analysis as well as orthogonal test and matrix analysis methods. The optimal solution resulted in a maximum battery module temperature of 34.65 °C, a maximum temperature difference of 3.95 °C, and a channel pressure drop of 8.82 Pa. Using the world-harmonized light-duty vehicles test cycle (WLTC) conditions for a battery pack in an electric car, the performance of the optimal battery thermal management system (BTMS) design was tested, and the results indicate that the maximum temperature can be controlled below 25.51 °C and the maximum temperature difference below 0.21 °C, which well meet the requirements of BTMS designs