FAAS/SERVERLESS-BASED LIGHTWEIGHT SOFTWARE DEVELOPMENT KIT FRAMEWORK FOR 6LOWPAN ARCHITECTURES

Effecting application development and/or updates in a wireless mesh networking environment raises a number of challenges. To address such challenges techniques are presented herein that support an extensible serverless framework that provides a function as a service (FaaS) paradigm for the rapid development and the easy operation and maintenance for Internet of things (IoT) customers. Among other things, a customer\u27s application code may be uncoupled from a vendor’s kernel and software development kit (SDK) library (thus freeing a customer from having to consider platform dependencies and allowing them to focus just on their business logic) and then generated as a small, platform-independent script that may be quickly and easily delivered to a massive number of endpoints. Additionally, a customer may add their own application programming interfaces (APIs) into a provided command-line interface (CLI) Commands library. As well, a customer may leverage a virtualized or remote development and simulation environment to speed up development activities

Serverless Vehicular Edge Computing for the Internet of Vehicles

Rapid growth in the popularity of smart vehicles and increasing demand for vehicle autonomy brings new opportunities for vehicular edge computing (VEC). VEC aims at offloading the time-sensitive computational load of connected vehicles to edge devices, e.g., roadside units. However, VEC offloading raises complex resource management challenges and, thus, remains largely inaccessible to automotive companies. Recently, serverless computing emerged as a convenient approach to the execution of functions without the hassle of infrastructure management. In this work, we propose the idea of serverless VEC as the execution paradigm for Internet of Vehicles applications. Further, we analyze its benefits and drawbacks as well as identify technology gaps. We also propose emulation as a design, evaluation, and experimentation methodology for serverless VEC solutions. Using our emulation toolkit, we validate the feasibility of serverless VEC for real-world traffic scenarios.We would like to thank Asama Qureshi for his contribution to the traffic visualizer application. We would also like to acknowledge support through the Australian Research Council's funded projects DP230100081 and FT180100140. This work is also partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation, the European Union-NextGenerationEU through the UNICO 5G IþD SORUS project and by the NWO OffSense, EU Horizon Graph-Massivizer and CLOUDSTARS projects

Unleashing the power of decentralized serverless IoT dataflow architecture for the Cloud-to-Edge Continuum: a performance comparison

The advent of new computing and communication trends that link pervasive data sources and consumers, such as Edge Computing, 5G and IIoT, has led to the development of the Cloud-to-Edge Continuum in order to take advantage of the resources available in massive IoT scenarios and to conduct data analysis to leverage intelligence at all levels. This paper outlines the challenging requirements of this novel IoT context and presents an innovative IoT framework to develop dataflow applications for data-centric environments. The proposed design takes advantage of decentralized Pub/Sub communication and serverless nanoservice architecture, using novel technologies such as Zenoh and WebAssembly, respectively, to implement lightweight services along the Cloud-to-Edge infrastructure. We also describe some use cases to illustrate the benefits and concerns of the coming IoT generation, giving a communication performance comparison of Zenoh over brokered MQTT strategies.Ministerio de Universidades | Ref. FPU19/01284Agencia Estatal de Investigación | Ref. PCI2020-112174Agencia Estatal de Investigación | Ref. PID2020-113795RB-C33Agencia Estatal de Investigación | Ref. PID2020-116329GB-C21Xunta de Galicia | Ref. GRC-ED431C2022/04 T254Universidade de Vigo/CISU

COSACC: Cloud-Based Speed Advisory for Connected Vehicles in a Signalized Corridor

The objective of this study is to assess the feasibility of cloud-based real-time connected vehicle (CV) applications. The author developed a cloud-based speed advisory application for CVs in a signalized corridor (COSACC) to achieve this objective. The contribution of this study is threefold. First, it introduced a serverless cloud computing architecture using Amazon Web Services (AWS) for real-time CV applications. Second, the author developed a real-time optimization-based speed advisory algorithm that is deployable in AWS. Third, this study utilized a cloud-in-the-loop simulation testbed using AWS and Simulation of Urban Mobility (SUMO), which is a microscopic traffic simulator. The author conducted experiments on cloud access at three-hour intervals over 24 hours in one day. These experiments revealed that the total data upload and download time to and from AWS via LTE is on average 92 milliseconds, which meets the allowable delay requirement for real-time CV traffic mobility applications. The author conducted a case study by implementing the COSACC in a cloud-in-the-loop simulation testbed. The analyses revealed that COSACC can reduce vehicle stopped delay at the signalized intersections up to 98% and fuel consumption in the signalized corridor up to 12.7%, compared to the baseline scenario, i.e., no speed advisory on the signalized corridor. Moreover, the authors observed an average end-to-end delay from a CV sending basic safety messages to it receiving a speed advisory from the cloud to be about 443 ms, which is well under the 1000 ms threshold required for any real-time traffic mobility application for connected vehicles

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Modular architecture providing convergent and ubiquitous intelligent connectivity for networks beyond 2030

The transition of the networks to support forthcoming beyond 5G (B5G) and 6G services introduces a number of important architectural challenges that force an evolution of existing operational frameworks. Current networks have introduced technical paradigms such as network virtualization, programmability and slicing, being a trend known as network softwarization. Forthcoming B5G and 6G services imposing stringent requirements will motivate a new radical change, augmenting those paradigms with the idea of smartness, pursuing an overall optimization on the usage of network and compute resources in a zero-trust environment. This paper presents a modular architecture under the concept of Convergent and UBiquitous Intelligent Connectivity (CUBIC), conceived to facilitate the aforementioned transition. CUBIC intends to investigate and innovate on the usage, combination and development of novel technologies to accompany the migration of existing networks towards Convergent and Ubiquitous Intelligent Connectivity (CUBIC) solutions, leveraging Artificial Intelligence (AI) mechanisms and Machine Learning (ML) tools in a totally secure environment

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Implementing a Web-based Application for Analysis and Evaluation of Heart Rate Variability Using Serverless Architecture

This article is devoted to the development of a web-based application for analysis and evaluation of Heart Rate Variability (HRV) using serverless architecture. Advancements in information algorithms and computing technologies have been playing an increasingly important role in cardiology, as continuous monitoring of patients’ health can be vital to their well-being.  One physiological parameter that can be easily measured and that can provide indispensable insight into the state of the human body is the HRV.  HRV analysis can assess not only the physiological state of the body but also provide the capability to monitor its dynamics and predict future diseases. As the research in the sphere of cardiology is constantly growing there is a multitude of new ways to assess the physiological state of patients and provide an early indicator to pathological conditions. Therefore, there is a need to bring these advances to a growing number of end-users (health-care professionals and patients) in the shortest possible time. To address this problem, this study proposes the development of a web-based application for analysis and evaluation of HRV by applying linear and nonlinear mathematical methods. The application is created using a serverless architectural approach, which allows for fast development time, as there is no need to manage server infrastructure, and for automatic scaling to dynamically match the number of requests. The developer can instead focus on implementing the logic for the HRV analysis algorithms and deliver new improvements at a faster rate. The proposed web application can be accessed by any device that is connected to the Internet and is optimized to handle both an intermittent and a consistent volume of requests. The algorithms implemented in the web application have been validated by examining two groups of subjects (young adults and older adults) using linear and non-linear models. The obtained results from the two groups can be compared with a set of reference values (only for the linear methods) and an assessment can be made whether each studied parameter is within the normal range or outside it (its value is too high or too low). To aid the assessment for HRV, the results obtained by the linear and nonlinear analysis are presented using a set of both graphs and tables