A Flexible and Scalable Architecture for Real-Time ANT+ Sensor Data Acquisition and NoSQL Storage

Wireless Personal or Body Area Networks (WPANs or WBANs) are the main mechanisms to develop healthcare systems for an ageing society. Such systems offer monitoring, security, and caring services by measuring physiological body parameters using wearable devices. Wireless sensor networks allow inexpensive, continuous, and real-time updates of the sensor data, to the data repositories via an Internet. A great deal of research is going on with a focus on technical, managerial, economic, and social health issues. The technical obstacles, which we encounter, in general, are better methodologies, architectures, and context data storage. Sensor communication, data processing and interpretation, data interchange format, data transferal, and context data storage are sensitive phases during the whole process of body parameter acquisition until the storage. ANT+ is a proprietary (but open access) low energy protocol, which supports device interoperability by mutually agreeing upon device profile standards. We have implemented a prototype, based upon ANT+ enabled sensors for a real-time scenario. This paper presents a system architecture, with its software organization, for real-time message interpretation, event-driven based real-time bidirectional communication, and schema flexible storage. A computer user uses it to acquire and to transmit the data using a Windows service to the context server

Smartphone sensing platform for emergency management

The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The SmartRescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.Comment: 11th International Conference on Information Systems for Crisis Response and Management ISCRAM2014 (2014

Shared memory for a fault-tolerant computer

A system is described for sharing a memory in a fault-tolerant computer. The memory is under the direct control and monitoring of error detecting and error diagnostic units in the fault-tolerant computer. This computer verifies that data to and from the memory is legally encoded and verifies that words read from the memory at a desired address are, in fact, actually delivered from that desired address. The means are provided for a second processor, which is independent of the direct control and monitoring of the error checking and diagnostic units of the fault-tolerant computer, and to share the memory of the fault-tolerant computer. Circuitry is included to verify that: (1) the processor has properly accessed a desired memory location in the memory; (2) a data word read-out from the memory is properly coded; and (3) no inactive memory was erroneously outputting data onto the shared memory bus

Bringing health and fitness data together for connected health care: Mobile apps as enablers of interoperability

Background: A transformation is underway regarding how we deal with our health. Mobile devices make it possible to have continuous access to personal health information. Wearable devices, such as Fitbit and Apple's smartwatch, can collect data continuously and provide insights into our health and fitness. However, lack of interoperability and the presence of data silos prevent users and health professionals from getting an integrated view of health and fitness data. To provide better health outcomes, a complete picture is needed which combines informal health and fitness data collected by the user together with official health records collected by health professionals. Mobile apps are well positioned to play an important role in the aggregation since they can tap into these official and informal health and data silos. Objective: The objective of this paper is to demonstrate that a mobile app can be used to aggregate health and fitness data and can enable interoperability. It discusses various technical interoperability challenges encountered while integrating data into one place. Methods: For 8 years, we have worked with third-party partners, including wearable device manufacturers, electronic health record providers, and app developers, to connect an Android app to their (wearable) devices, back-end servers, and systems. Results: The result of this research is a health and fitness app called myFitnessCompanion, which enables users to aggregate their data in one place. Over 6000 users use the app worldwide to aggregate their health and fitness data. It demonstrates that mobile apps can be used to enable interoperability. Challenges encountered in the research process included the different wireless protocols and standards used to communicate with wireless devices, the diversity of security and authorization protocols used to be able to exchange data with servers, and lack of standards usage, such as Health Level Seven, for medical information exchange. Conclusions: By limiting the negative effects of health data silos, mobile apps can offer a better holistic view of health and fitness data. Data can then be analyzed to offer better and more personalized advice and care

Data Collection in Smart Communities Using Sensor Cloud: Recent Advances, Taxonomy, and Future Research Directions

The remarkable miniaturization of sensors has led to the production of massive amounts of data in smart communities. These data cannot be efficiently collected and processed in WSNs due to the weak communication capability of these networks. This drawback can be compensated for by amalgamating WSNs and cloud computing to obtain sensor clouds. In this article, we investigate, highlight, and report recent premier advances in sensor clouds with respect to data collection. We categorize and classify the literature by devising a taxonomy based on important parameters, such as objectives, applications, communication technology, collection types, discovery, data types, and classification. Moreover, a few prominent use cases are presented to highlight the role of sensor clouds in providing high computation capabilities. Furthermore, several open research challenges and issues, such as big data issues, deployment issues, data security, data aggregation, dissemination of control message, and on time delivery are discussed. Future research directions are also provided

Spaceborne memory organization, phase 1 Final report

Application of associative memories to data processing for future space vehicle

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data

Open software and standards in the realm of laser scanning technology

Abstract This review aims at introducing laser scanning technology and providing an overview of the contribution of open source projects for supporting the utilization and analysis of laser scanning data. Lidar technology is pushing to new frontiers in mapping and surveying topographic data. The open source community has supported this by providing libraries, standards, interfaces, modules all the way to full software. Such open solutions provide scientists and end-users valuable tools to access and work with lidar data, fostering new cutting-edge investigation and improvements of existing methods. The first part of this work provides an introduction on laser scanning principles, with references for further reading. It is followed by sections respectively reporting on open standards and formats for lidar data, tools and finally web-based solutions for accessing lidar data. It is not intended to provide a thorough review of state of the art regarding lidar technology itself, but to provide an overview of the open source toolkits available to the community to access, visualize, edit and process point clouds. A range of open source features for lidar data access and analysis is provided, providing an overview of what can be done with alternatives to commercial end-to-end solutions. Data standards and formats are also discussed, showing what are the challenges for storing and accessing massive point clouds. The desiderata are to provide scientists that have not yet worked with lidar data an overview of how this technology works and what open source tools can be a valid solution for their needs in analysing such data. Researchers that are already involved with lidar data will hopefully get ideas on integrating and improving their workflow through open source solutions