National Institute of Standards and Technology における超伝導研究及び生活

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

Many technical communities are vigorously pursuing research topics that contribute to the Internet of Things (IoT). Nowadays, as sensing, actuation, communication, and control become even more sophisticated and ubiquitous, there is a significant overlap in these communities, sometimes from slightly different perspectives. More cooperation between communities is encouraged. To provide a basis for discussing open research problems in IoT, a vision for how IoT could change the world in the distant future is first presented. Then, eight key research topics are enumerated and research problems within these topics are discussed.

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Research Directions for the Internet of Things

Wireless sensor networks for healthcare: A survey

Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.

/pdf/context-aware-wireless-sensor-networks-for-assisted-living-1a24qae7go.pdf

Context-aware wireless sensor networks for assisted living and residential monitoring

This paper demonstrates how to use multiple channels to improve communication performance in Wireless Sensor Networks (WSNs). We first investigate multi-channel realities in WSNs through intensive empirical experiments with Micaz motes. Our study shows that current multi-channel protocols are not suitable for WSNs, because of the small number of available channels and unavoidable time errors found in real networks. With these observations, we propose a novel tree-based multichannel scheme for data collection applications, which allocates channels to disjoint trees and exploits parallel transmissions among trees. In order to minimize interference within trees, we define a new channel assignment problem which is proven NP- complete. Then we propose a greedy channel allocation algorithm which outperforms other schemes in dense networks with a small number of channels.We implement our protocol, called TMCP, in a real testbed. Through both simulation and real experiments, we show that TMCP can significantly improve network throughput and reduce packet losses. More importantly, evaluation results show that TMCP better accommodates multi-channel realities found in WSNs than other multi-channel protocols.

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Realistic and Efficient Multi-Channel Communications in Wireless Sensor Networks

We describe ALARM-NET, a wireless sensor network for assisted-living and residential monitoring. It integrates environmental and physiological sensors in a scalable, heterogeneous architecture. A query protocol allows real-time collection and processing of sensor data by user interfaces and back-end analysis programs. One such program determines circadian activity rhythms of residents, feeding activity information back into the sensor network to aid context-aware power management, dynamic privacy policies, and data association. Communication is secured end-to-end to protect sensitive medical and operational information. The ALARM-NET system has been implemented as a network of MICAz sensors, stargate gateways, iPAQ PDAs, and PCs. Customized infrared motion and dust sensors, and integrated temperature, light, pulse, and blood oxygenation sensors are present. Software components include: TinyOS query processor and security modules for motes; AlarmGate, an embedded Java application for managing power, privacy, security, queries, and client connections; Java resident monitoring and sensor data querying applications for PDAs and PCs; and a circadian activity rhythm analysis program. We show the correctness, robustness, and extensibility of the system architecture through a scenario-based evaluation of the integrated ALARM-NET system, as well as performance data for individual software components.

ALARM-NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring

Environmental wireless sensor network (EWSN) systems are deployed in potentially harsh and remote environments where inevitable node and communication failures must be tolerated. LUSTER---Light Under Shrub Thicket for Environmental Research---is a system that meets the challenges of EWSNs using a hierarchical architecture that includes distributed reliable storage, delay-tolerant networking, and deployment time validation techniques.In LUSTER, a fleet of sensors coordinate communications using LiteTDMA, a low-power cluster-based MAC protocol. They measure the complex light environment in thickets and are open to additional ecological parameters, such as temperature and CO2.LUSTER has been deployed and evaluated in laboratory, forested, and barrier island environments. It includes new sensor hardware designs: (a) "SolarDust," a hybrid multichannel energy harvesting and sensing device; (b) "Medusa," a spatially reconfigurable light sensor; (c) a removable SD card storage node; and, (d) in-situ user interface tool for deployment time validation.

/pdf/luster-wireless-sensor-network-for-environmental-research-1j2lorppes.pdf

LUSTER: wireless sensor network for environmental research

Many applications in wireless sensor networks require communication performance that is both consistent and high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short term estimations in routing algorithm designs. To further improve network performance we have designed a distributed route maintenance framework based on feedback control solutions. In real system evaluations with 48 T-Motes, our overall solution improves end-to-end packet delivery ratio over existing solutions by up to 40%, while reducing energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

https://www.cs.virginia.edu/~stankovic/psfiles/Stability-3.pdf

Yafeng Wu

Papers

Context-aware wireless sensor networks for assisted living and residential monitoring

Realistic and Efficient Multi-Channel Communications in Wireless Sensor Networks

ALARM-NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring

LUSTER: wireless sensor network for environmental research

Towards Stable Network Performance in Wireless Sensor Networks