Cloud based processing of real time sensor-data streams

The aim of the project is to design an architecture for real time sensor data streaming, management and live visualisation over the web to contribute to existing research in the field of the Web of Things. The project will investigate the infrastructure between streaming hardware and website. Typical hardware would be programmable, equipped with sensors or the ability to connect up sensors and a possibility to communicate over the internet, e.g. an Arduino board, Raspberry Pi or also a smartphone. This project focuses on the universality and manageability of the system to allow many users to deploy the sensing data over a web portal and consume the data on their own websites or allow others to integrate the data into third party websites. The requirements for a universal and manageable streaming service will be developed by investigation of existing systems and analysis of use cases in different application areas. The operational capability of the architecture was investigated by implementing key features and experiments. The project identified the strength and weaknesses of the architecture and investigated the feasibility of the concept. A basic prototype was developed and tested. The feasibility of several parts of the system was proved by implementations and tests. Visualisation possibilities, security and processes were investigated. System requirements for different application areas were defined. System limits were investigated such as the relation between the streamed amount of sensor values and visualisation update rate, the accuracy of the system with high visualisation update rates and the limits of creating streaming instances as a bottleneck. The experiments and tests defined the limits and gave statements about the system performance, security and feasibility

Cloud based processing of real time sensor-data streams

The aim of the project is to design an architecture for real time sensor data streaming, management and live visualisation over the web to contribute to existing research in the field of the Web of Things. The project will investigate the infrastructure between streaming hardware and website. Typical hardware would be programmable, equipped with sensors or the ability to connect up sensors and a possibility to communicate over the internet, e.g. an Arduino board, Raspberry Pi or also a smartphone. This project focuses on the universality and manageability of the system to allow many users to deploy the sensing data over a web portal and consume the data on their own websites or allow others to integrate the data into third party websites. The requirements for a universal and manageable streaming service will be developed by investigation of existing systems and analysis of use cases in different application areas. The operational capability of the architecture was investigated by implementing key features and experiments. The project identified the strength and weaknesses of the architecture and investigated the feasibility of the concept. A basic prototype was developed and tested. The feasibility of several parts of the system was proved by implementations and tests. Visualisation possibilities, security and processes were investigated. System requirements for different application areas were defined. System limits were investigated such as the relation between the streamed amount of sensor values and visualisation update rate, the accuracy of the system with high visualisation update rates and the limits of creating streaming instances as a bottleneck. The experiments and tests defined the limits and gave statements about the system performance, security and feasibility

The Librarian & the Big Data: Bridging the Gap

Arcot Rajasekar, PhD, is Professor in the School of Information and Science, Chief Scientist for the Renaissance Computing Institute, and Co-Director of the Data Intensive Cyber Environments Center, all at the University of North Carolina at Chapel Hill. He spoke on how the library and information science community can meet the challenges of the scientific data explosion

Special Issue on Smart Data and Semantics in a Sensor World

Introduction Since its first inception in 2001, the application of the Semantic Web [1, 2] has carried out an extensive use of ontologies [3–5], reasoning, and semantics in diverse fields, such as Information Integration, Software Engineering, Bioinformatics, eGovernment, eHealth, and social networks. This widespread use of ontologies has led to an incredible advance in the development of techniques to manipulate, share, reuse, and integrate information across heterogeneous data sources. In recent years, the growth of the IoT (Internet of Things) required to face the challenges of “Big Data” [6–10]. The cost of sensors is decreasing, while their use is expanding. Moreover, the use of multiple personal smart devices is an emerging trend and all of them can embed sensors to monitor the surrounding environment. Therefore, the number of available sensors is exploding. On the one hand, the flows of sensor data are massive and continuous, and the data could be obtained in real time or with a delay of just a few seconds. Then, the volume of sensor data is increasing continuously every day. On the other hand, the variety of data being generated is also increasing, due to plenty of different devices and different measures to record. There are many kinds of structured and unstructured sensor data in diverse formats. Moreover, data veracity, which is the degree of accuracy or truthfulness of a data set, is an important aspect to consider. In the context of sensor data, it represents the trustworthiness of the data source and the processing of data. The need for more accurate and reliable data was always declared, but often overlooked for the sake of larger and cheaper..

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

A Privacy-Aware Distributed Storage and Replication Middleware for Heterogeneous Computing Platform

Cloud computing is an emerging research area that has drawn considerable interest in recent years. However, the current infrastructure raises significant concerns about how to protect users\u27 privacy, in part due to that users are storing their data in the cloud vendors\u27 servers. In this paper, we address this challenge by proposing and implementing a novel middleware, called Uno, which separates the storage of physical data and their associated metadata. In our design, users\u27 physical data are stored locally on those devices under a user\u27s full control, while their metadata can be uploaded to the commercial cloud. To ensure the reliability of users\u27 data, we develop a novel fine-grained file replication algorithm that exploits both data access patterns and device state patterns. Based on a quantitative analysis of the data set from Rice University, this algorithm replicates data intelligently in different time slots, so that it can not only significantly improve data availability, but also achieve a satisfactory performance on load balancing and storage diversification. We implement the Uno system on a heterogeneous testbed composed of both host servers and mobile devices, and demonstrate the programmability of Uno through implementation and evaluation of two sample applications, Uno@Home and Uno@Sense

Generic processing of real-time physiological data in the cloud

There is an emerging market in the collection of physiological data for analysis and presentation to end-users via web technologies for applications including health and fitness, telemedicine and self-tracking. As technology has improved, real-time streaming of physiological data, providing end-to-end user feedback has become feasible, allowing for innovative applications to be developed. Currently, there is no standardised method of collecting physiological data over the web for analysis and feedback to an end-user in real-time; existing platforms only support specific devices and application domains. This paper proposes a generic methodology and architecture for the collection, analysis and presentation of physiological data. It defines a standard method of encapsulating data from heterogeneous sensors, performing transformations on it and analysing it. The approach is evaluated through an implementation of the architecture using cloud computing technologies and an appropriate case study