Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

SciTech News Volume 71, No. 1 (2017)

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Collaborative Video Analytics on Distributed Edges with Multiagent Deep Reinforcement Learning

Deep Neural Network (DNN) based video analytics empowers many computer vision-based applications to achieve high recognition accuracy. To reduce inference delay and bandwidth cost for video analytics, the DNN models can be deployed on the edge nodes, which are proximal to end users. However, the processing capacity of an edge node is limited, potentially incurring substantial delay if the inference requests on an edge node is overloaded. While efforts have been made to enhance video analytics by optimizing the configurations on a single edge node, we observe that multiple edge nodes can work collaboratively by utilizing the idle resources on each other to improve the overall processing capacity and resource utilization. To this end, we propose a Multiagent Reinforcement Learning (MARL) based approach, named as EdgeVision, for collaborative video analytics on distributed edges. The edge nodes can jointly learn the optimal policies for video preprocessing, model selection, and request dispatching by collaborating with each other to minimize the overall cost. We design an actor-critic-based MARL algorithm with an attention mechanism to learn the optimal policies. We build a multi-edge-node testbed and conduct experiments with real-world datasets to evaluate the performance of our method. The experimental results show our method can improve the overall rewards by 33.6%-86.4% compared with the most competitive baseline methods

Modeling Scalability of Distributed Machine Learning

Present day machine learning is computationally intensive and processes large amounts of data. It is implemented in a distributed fashion in order to address these scalability issues. The work is parallelized across a number of computing nodes. It is usually hard to estimate in advance how many nodes to use for a particular workload. We propose a simple framework for estimating the scalability of distributed machine learning algorithms. We measure the scalability by means of the speedup an algorithm achieves with more nodes. We propose time complexity models for gradient descent and graphical model inference. We validate our models with experiments on deep learning training and belief propagation. This framework was used to study the scalability of machine learning algorithms in Apache Spark.Comment: 6 pages, 4 figures, appears at ICDE 201

Mapping Cloud-Edge-IoT opportunities and challenges in Europe

While current data processing predominantly occurs in centralized facilities, with a minor portion handled by smart objects, a shift is anticipated, with a surge in data originating from smart devices. This evolution necessitates reconfiguring the infrastructure, emphasising computing capabilities at the cloud's "edge" closer to data sources. This change symbolises the merging of cloud, edge, and IoT technologies into a unified network infrastructure - a Computing Continuum - poised to redefine tech interactions, offering novel prospects across diverse sectors. The computing continuum is emerging as a cornerstone of tech advancement in the contemporary digital era. This paper provides an in-depth exploration of the computing continuum, highlighting its potential, practical implications, and the adjustments required to tackle existing challenges. It emphasises the continuum's real-world applications, market trends, and its significance in shaping Europe's tech future

Manufacturing Process Optimization Using Edge Analytics

Most manufacturing plants contain some amount of time series sensor data – streams of values and time stamps. This data, however, isn’t useful with most types of analytics or machine learning for the purpose of process optimization. This thesis presents a novel and innovative solution to the problem using a software stack leveraging the Predix Complex Event Processing Engine (Edge Analytics) to condition the data, combined with RFID for serialization. Each step in the formation of the solution is documented, from connecting equipment to analyzing and ingesting data produced by the edge analytic. This solution was developed and piloted at the GE Grid Solutions plant in Clearwater, FL

A concept for application of integrated digital technologies to enhance future smart agricultural systems

Future agricultural systems should increase productivity and sustainability of food production and supply. For this, integrated and efficient capture, management, sharing, and use of agricultural and environmental data from multiple sources is essential. However, there are challenges to understand and efficiently use different types of agricultural and environmental data from multiple sources, which differ in format and time interval. In this regard, the role of emerging technologies is considered to be significant for integrated data gathering, analyses and efficient use. In this study, a concept was developed to facilitate the full integration of digital technologies to enhance future smart and sustainable agricultural systems. The concept has been developed based on the results of a literature review and diverse experiences and expertise which enabled the identification of stat-of-the-art smart technologies, challenges and knowledge gaps. The features of the proposed solution include: data collection methodologies using smart digital tools; platforms for data handling and sharing; application of Artificial Intelligent for data integration and analysis; edge and cloud computing; application of Blockchain, decision support system; and a governance and data security system. The study identified the potential positive implications i.e. the implementation of the concept could increase data value, farm productivity, effectiveness in monitoring of farm operations and decision making, and provide innovative farm business models. The concept could contribute to an overall increase in the competitiveness, sustainability, and resilience of the agricultural sector as well as digital transformation in agriculture and rural areas. This study also provided future research direction in relation to the proposed concept. The results will benefit researchers, practitioners, developers of smart tools, and policy makers supporting the transition to smarter and more sustainable agriculture systems

Digitising the Industry Internet of Things Connecting the Physical, Digital and VirtualWorlds

This book provides an overview of the current Internet of Things (IoT) landscape, ranging from the research, innovation and development priorities to enabling technologies in a global context. A successful deployment of IoT technologies requires integration on all layers, be it cognitive and semantic aspects, middleware components, services, edge devices/machines and infrastructures. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC - Internet of Things European Research Cluster from research to technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster and the IoT European Platform Initiative (IoT-EPI) and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in the next years. The IoT is bridging the physical world with virtual world and requires sound information processing capabilities for the "digital shadows" of these real things. The research and innovation in nanoelectronics, semiconductor, sensors/actuators, communication, analytics technologies, cyber-physical systems, software, swarm intelligent and deep learning systems are essential for the successful deployment of IoT applications. The emergence of IoT platforms with multiple functionalities enables rapid development and lower costs by offering standardised components that can be shared across multiple solutions in many industry verticals. The IoT applications will gradually move from vertical, single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organisations and people, being one of the essential paradigms of the digital economy. Many of those applications still have to be identified and involvement of end-users including the creative sector in this innovation is crucial. The IoT applications and deployments as integrated building blocks of the new digital economy are part of the accompanying IoT policy framework to address issues of horizontal nature and common interest (i.e. privacy, end-to-end security, user acceptance, societal, ethical aspects and legal issues) for providing trusted IoT solutions in a coordinated and consolidated manner across the IoT activities and pilots. In this, context IoT ecosystems offer solutions beyond a platform and solve important technical challenges in the different verticals and across verticals. These IoT technology ecosystems are instrumental for the deployment of large pilots and can easily be connected to or build upon the core IoT solutions for different applications in order to expand the system of use and allow new and even unanticipated IoT end uses. Technical topics discussed in the book include: • Introduction• Digitising industry and IoT as key enabler in the new era of Digital Economy• IoT Strategic Research and Innovation Agenda• IoT in the digital industrial context: Digital Single Market• Integration of heterogeneous systems and bridging the virtual, digital and physical worlds• Federated IoT platforms and interoperability• Evolution from intelligent devices to connected systems of systems by adding new layers of cognitive behaviour, artificial intelligence and user interfaces.• Innovation through IoT ecosystems• Trust-based IoT end-to-end security, privacy framework• User acceptance, societal, ethical aspects and legal issues• Internet of Things Application