Real-Time Sensor Networks and Systems for the Industrial IoT: What Next?

The Industrial Internet of Things (Industrial IoT-IIoT) is the emerging core backbone construct for the various cyber-physical systems constituting one of the principal dimensions of the 4th Industrial Revolution [...

A Tagless Indoor Localization System Based on Capacitive Sensing Technology.

Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%.

Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project.

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance.

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring.

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application.

Evaluation of TELEDERM for dermatological services in rural and remote areas.

Objective: This paper describes the Web-based decision support system (TELEDERM) that provides medical services to rural and remote general practitioners (GPs), and presents qualitative results on the usefulness and usability of the system obtained from trials in the GoldFields, Western Australia.

Methods: Diagnostic methods and other functions were developed to assist medical practitioners, who may not be familiar with aspects of decision support systems, to diagnose patients with dermatological problems. GPs in rural and remote areas took part in a trial to assess the usefulness of TELEDERM in clinical situations.

Dynamic knowledge validation and verification for CBR teledermatology system.

Objective: Case-based reasoning has been of great importance in the development of many decision support applications. However, relatively little effort has gone into investigating how new knowledge can be validated. Knowledge validation is important in dealing with imperfect data collected over time, because inconsistencies in data do occur and adversely affect the performance of a diagnostic system.

A comparison of algorithms for calculating glaucoma change probability confidence intervals.

Purpose: To evaluate the ability to detect change in standard automated perimetry data using 4 different methods for calculating the glaucoma change probability (GCP).

Methods: A database of stable visual fields, collected within 1 week from 35 glaucoma patients and within 6 months from 15 normal patients, was used to determine confidence intervals for GCP using 4 different methods. The methods classified visual field locations on the basis of either defect or mean threshold, and used test-retest data or baseline-less-follow-up data to determine values for the confidence intervals.