Human-centric sensing

Recent Advances in Wearable Sensing Technologies

Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future.

eFarm: A Tool for Better Observing Agricultural Land Systems

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human-land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.

Toward lightweight biometric signal processing for wearable devices

Wearable devices are becoming a natural and economic means to gather biometric data from end users. The massive amount of information that they will provide, unimaginable until a few years ago, owns an immense potential for applications such as continuous monitoring for personalized healthcare and use within fitness applications. Wearables are however heavily constrained in terms of amount of memory, transmission capability and energy reserve. This calls for dedicated, lightweight but still effective algorithms for data management. This paper is centered around lossy data compression techniques, whose aim is to minimize the amount of information that is to be stored on their onboard memory and subsequently transmitted over wireless interfaces. Specifically, we analyze selected compression techniques for biometric signals, quantifying their complexity (energy consumption) and compression performance. Hence, we propose a new class of codebook-based (CB) compression algorithms, designed to be energy efficient, online and amenable to any type of signal exhibiting recurrent patterns. Finally, the performance of the selected and the new algorithm is assessed, underlining the advantages offered by CB schemes in terms of memory savings and classification algorithms.

A framework for detecting unfolding emergencies using humans as sensors

The advent of online social networks (OSNs) paired with the ubiquitous proliferation of smartphones have enabled social sensing systems. In the last few years, the aptitude of humans to spontaneously collect and timely share context information has been exploited for emergency detection and crisis management. Apart from event-specific features, these systems share technical approaches and architectural solutions to address the issues with capturing, filtering and extracting meaningful information from data posted to OSNs by networks of human sensors. This paper proposes a conceptual and architectural framework for the design of emergency detection systems based on the “human as a sensor” (HaaS) paradigm. An ontology for the HaaS paradigm in the context of emergency detection is defined. Then, a modular architecture, independent of a specific emergency type, is designed. The proposed architecture is demonstrated by an implemented application for detecting earthquakes via Twitter. Validation and experimental results based on messages posted during earthquakes occurred in Italy are reported.

An expression of uncertainty and its application to positioning: a quality-metric and optimal ranges for the identification of cells with RFID

Here we devise an approach to model error and its propagation. Without approximations, we define the uncertainty of a measurement as its maximum possible error (maper). Thus, we propose and solve analytically two optimization problems. The one designed to determine the uncertainty of a measurement, the other specifically designed to optimize the accuracy of a RFID location system. The usefulness of this general approach is shown by applying it to the particular instance of estimating the coordinates of a person in real-time using RFID devices. This way, exact formulae to evaluate the quality of this measurement are mathematically deduced, which is useful, for example, to predict whether an inexpensive RFID location technology can meet a desired quality standard or not. The second optimization problem proposed here defines an optimal range (orange) for the RFID devices employed. Again, analytically, its exact formulae were derived. We propose an approach to distribute RFID tags for a positioning system based solely on RFID technology. In the light of the formulae, its quality is good enough as to locate emergency phone calls in real time. We found that key to an optimal performance is the range used and the distance between consecutive tags.

Sensor network algorithms and applications

A sensor network is a collection of nodes with processing, communication and sensing capabilities deployed in an area of interest to perform a monitoring task. There has now been about a decade of very active research in the area of sensor networks, with significant accomplishments made in terms of both designing novel algorithms and building exciting new sensing applications. This Theme Issue provides a broad sampling of the central challenges and the contributions that have been made towards addressing these challenges in the field, and illustrates the pervasive and central role of sensor networks in monitoring human activities and the environment.

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.