2,636 research outputs found
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
Distributed and Communication-Efficient Continuous Data Processing in Vehicular Cyber-Physical Systems
Processing the data produced by modern connected vehicles is of increasing interest for vehicle manufacturers to gain knowledge and develop novel functions and applications for the future of mobility.Connected vehicles form Vehicular Cyber-Physical Systems (VCPSs) that continuously sense increasingly large data volumes from high-bandwidth sensors such as LiDARs (an array of laser-based distance sensors that create a 3D map of the surroundings).The straightforward attempt of gathering all raw data from a VCPS to a central location for analysis often fails due to limits imposed by the infrastructure on the communication and storage capacities. In this Licentiate thesis, I present the results from my research that investigates techniques aiming at reducing the data volumes that need to be transmitted from vehicles through online compression and adaptive selection of participating vehicles. As explained in this work, the key to reducing the communication volume is in pushing parts of the necessary processing onto the vehicles\u27 on-board computers, thereby favorably leveraging the available distributed processing infrastructure in a VCPS.The findings highlight that existing analysis workflows can be sped up significantly while reducing their data volume footprint and incurring only modest accuracy decreases. At the same time, the adaptive selection of vehicles for analyses proves to provide a sufficiently large subset of vehicles that have compliant data for further analyses, while balancing the time needed for selection and the induced computational load
Data Consistency for Data-Driven Smart Energy Assessment
In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received.
Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid
and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
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