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

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

COMET: Co-simulation of Multi-Energy Systems for Energy Transition

The ongoing energy transition to reduce carbon emissions presents some of the most formidable challenges the energy sector has ever experienced, requiring a paradigm change that involves diverse players and heterogeneous concerns, includ- ing regulations, economic drivers, societal, and environmental aspects. Central to this transition is the adoption of integrated multi-energy systems (MES) to efficiently produce, distribute, store, and convert energy among different vectors. A deep understanding of MES is fundamental to harness the potential for energy savings and foster energy transition towards a low carbon future. Unfortunately, the inherent complexity of MES makes them extremely difficult to analyze, understand, design and optimize. This work proposes a digital twin co-simulation platform that provides a structured basis to design, develop and validate novel solutions and technologies for multi-energy system. The platform will enable the definition of a virtual representation of the real-world (digital twin) as a composition of models (co-simulation) that analyze the environment from multiple viewpoints and at different spatio-temporal scales

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments. This survey focuses on the intelligence and spatio-temporal characteristics of four fundamental system components in ubiquitous semantic Metaverse, i.e., artificial intelligence (AI), spatio-temporal data representation (STDR), semantic Internet of Things (SIoT), and semantic-enhanced digital twin (SDT). We thoroughly survey the representative techniques of the four fundamental system components that enable intelligent, personalized, and context-aware interactions with typical use cases of the ubiquitous semantic Metaverse, such as remote education, work and collaboration, entertainment and socialization, healthcare, and e-commerce marketing. Furthermore, we outline the opportunities for constructing the future ubiquitous semantic Metaverse, including scalability and interoperability, privacy and security, performance measurement and standardization, as well as ethical considerations and responsible AI. Addressing those challenges is important for creating a robust, secure, and ethically sound system environment that offers engaging immersive experiences for the users and AR/VR applications.Comment: 18 pages, 7 figures, 3 table

A compendium of Technologies, Practices, Services and Policies for Scaling Climate Smart Agriculture in Odisha (India)

Stakeholders engaged in agricultural research for development (AR4D) are increasingly tackling risks associated with climate change in smallholder systems. Accordingly, development and scaling of climate-smart agriculture (CSA) are one of the priorities for all the organizations, departments and ministries associated with the farm sector. Having a ‘one-stop-shop’ compiled in the format of a compendium for CSA technologies, practices and services would therefore serve a guide for all the stakeholders for scaling CSA in smallholder systems. Bringing out a Compendium on Climate-Smart Agriculture (CSA) for Odisha, India was therefore thought of during the workshop on ‘Scaling Climate-Smart Agriculture in Odisha’ organized at Bhubaneswar on 18-19 July 2018 by International Rice Research Institute (IRRI) in collaboration with Department of Agriculture (DoA) & Farmers’ Empowerment, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Orissa University of Agriculture and Technology (OUAT) & International Maize and Wheat Improvement Center (CIMMYT) under the aegis of CGIAR Research program on Climate Change, Agriculture and Food Security (CCAFS). The main objectives to bring forth this compendium are: to argue the case for agriculture policies and practices that are climate-smart; to raise awareness of what can be done to make agriculture policies and practices climatesmart; and to provide practical guidance and recommendations that are well referenced and, wherever possible, based on lessons learned from practical action. CSA programmes are unlikely to be effective unless their implementation is supported by sound policies and institutions. It is therefore important to enhance institutional capacities in order to implement and replicate CSA strategies. Institutions are vital to agricultural development as well as the realisation of resilient livelihoods.They are not only a tool for farmers and decision-makers, but are also the main conduit through which CSA practices can be scaled up and sustained. The focus in this compendium is on CSA and it’s relevant aspects, i.e., (i) technologies and practices, (ii) services, (iii) technology targeting, (iv) business models, (v) capacity building, and (vi) policies. The approaches and tools available in the compendium span from face-to-face technicianfarmer dialogues to more structured exchanges of online and offline e-learning. In every scenario it is clear that tailoring to local expectations and needs is key. In particular, the voice of farmers is essential to be captured as they are the key actors to promote sustainable agriculture, and their issues need to be prioritized. CSA practices are expected to sustainably increase productivity and resilience (adaptation), reduce Greenhouse Gases (mitigation), and enhance achievement of national food security along with sustainable development goals. CSA is widely expected to contribute towards achieving these objectives and enhance climate change adaptation. CSA practices have to be included in State’s Climate Policy as a priority intervention as the state steps up efforts to tackle climate change. Furthermore, emphasis shoud be laid on CSA training for a sustainable mode to enhance CSA adoption in the state hence the relevance of developing this document. The adaption of climate related knowledge, technologies and practices to local conditions, promoting joint learning by farmers, researchers, rural advisor and widely disseminating CSA practices, is critical. This compendium brings together a collection of experiences from different stakeholders with background of agricultural extension and rural advisory services in supporting CSA. The contributions are not intended to be state-of-the art academic articles but thought and discussion pieces of work in progress. The compendium itself is a ‘living‘ document which is intended to be revised periodically