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LUSTER: Wireless Sensor Network for Environmental Research
"... 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 hierar ..."
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Cited by 77 (8 self)
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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.
Reliable Clinical Monitoring using Wireless Sensor Networks: Experiences in a Step-down Hospital Unit
"... This paper presents the design, deployment, and empirical study of a wireless clinical monitoring system that collects pulse and oxygen saturation readings from patients. The primary contribution of this paper is an in-depth clinical trial that assesses the feasibility of wireless sensor networks fo ..."
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Cited by 73 (7 self)
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This paper presents the design, deployment, and empirical study of a wireless clinical monitoring system that collects pulse and oxygen saturation readings from patients. The primary contribution of this paper is an in-depth clinical trial that assesses the feasibility of wireless sensor networks for patient monitoring in general hospital units. We present a detailed analysis of the system reliability from a long term hospital deployment over seven months involving 41 patients in a step-down cardiology unit. The network achieved high reliability (median 99.68%, range 95.21 % – 100%). The overall reliability of the system was dominated by sensing reliability of the pulse oximeters (median 80.85%, range 0.46 % – 97.69%). Sensing failures usually occurred in short bursts, although longer periods were also present due to sensor disconnections. We show that the sensing reliability could be significantly improved through oversampling and by implementing a disconnection alarm system that incurs minimal intervention cost. A retrospective data analysis indicated that the system provided sufficient temporal resolution to support the detection of clinical deterioration in three patients who suffered from significant clinical events including transfer to Intensive Care Units. These results indicate the feasibility and promise of using wireless sensor networks for continuous patient monitoring and clinical deterioration detection in general hospital units.
Mercury: A Wearable Sensor Network Platform for High-Fidelity Motion Analysis
"... This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson’s Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support lo ..."
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Cited by 61 (3 self)
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This paper describes Mercury, a wearable, wireless sensor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson’s Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and application specific policies. Mercury is designed to overcome the core challenges of long battery lifetime and high data fidelity for long-term studies where patients wear sensors continuously 12 to 18 hours a day. This requires tuning sensor operation and data transfers based on energy consumption of each node and processing data under severe computational constraints. Mercury provides a high-level programming interface that allows a clinical researcher to rapidly build up different policies for driving data collection and tuning sensor lifetime. We present the Mercury architecture and a detailed evaluation of two applications of the system for monitoring patients with Parkinson’s Disease and epilepsy.
Metrosense project: People-centric sensing at scale
- In WSW 2006 at Sensys
, 2006
"... Looking forward 10-20 years we envision Internet scale sensing where the majority of the traffic on the network is sensor data and the majority of applications used every day by the general populace integrates sensing and actuation in some form. Sensing will be people-centric. On the other hand, nea ..."
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Cited by 51 (2 self)
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Looking forward 10-20 years we envision Internet scale sensing where the majority of the traffic on the network is sensor data and the majority of applications used every day by the general populace integrates sensing and actuation in some form. Sensing will be people-centric. On the other hand, nearly all published sensor network research over the last five years has focused on isolated, small scale testbeds designed for specialized applications (e.g., environmental sensing, industrial sensing, etc.) of interest to engineers and scientists. We believe the gap between the state of the art and our future vision can be bridged through the development of a new wireless sensor edge for the Internet. To this end, in the MetroSense Project we are developing a general purpose sensing infrastructure capable of realizing a wealth of sensing applications with mass appeal for producers and consumers of sensed data. In this paper we motivate the need for a new architecture to support people-centric sensing at Internet scale, outline our MetroSense architecture [1], and highlight our progress to date in designing and deploying prototype implementations of the MetroSense architecture via the deployment of our campus area sensor network.
Distributed Recognition of Human Actions Using Wearable Motion Sensor Networks
, 2009
"... We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capabl ..."
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Cited by 23 (3 self)
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We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. We model the distribution of multiple action classes as a mixture subspace model, one subspace for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the sparse representation. Fast linear solvers are provided to compute such representation via ℓ 1-minimization. To validate the accuracy of the framework, a public wearable action recognition database is constructed, called wearable action recognition database (WARD). The database is comprised of 20 human subjects in 13 action categories. Using up to five motion sensors in the WARD database, DSC achieves state-of-the-art performance. We further show that the recognition precision only decreases gracefully using smaller subsets of active sensors. It validates the robustness of the distributed recognition framework on an unreliable wireless network. It also demonstrates the ability of DSC to conserve sensor energy for communication while preserve accurate global classification.
Review Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey
, 2011
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ANDES: an ANalysis-based DEsign tool for wireless Sensor networks
- In Proceedings of the 28th IEEE International Real-Time Systems Symposium (RTSS
, 2007
"... Abstract — We have developed an analysis-based design tool, ANDES, for modeling a wireless sensor network system and analyzing its performance before deployment. ANDES enables designers to systematically develop a model for the system, refine it iteratively by tuning the system parameters based on e ..."
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Cited by 18 (3 self)
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Abstract — We have developed an analysis-based design tool, ANDES, for modeling a wireless sensor network system and analyzing its performance before deployment. ANDES enables designers to systematically develop a model for the system, refine it iteratively by tuning the system parameters based on existing analysis techniques, and resolve key design decisions according to the required system performance. We also present a realtime communication schedulability analysis for sensor networks based on exact characterization which utilizes information regarding network topology and workload characteristics to analyze the schedulability of a set of periodic streams with realtime constraints. We further demonstrate the use of ANDES for the designers through detailed case studies where we design wireless sensor network applications (for target detection and environmental monitoring) using ANDES and validate the results through simulations. Currently, ANDES supports communication schedulability analysis, target tracking analysis and real-time capacity analysis which work on system models with differing levels of detail. ANDES has been developed by extending the AADL/OSATE framework which has been used extensively for real-time and embedded systems. Based on key insights gained from the development of this analysis tool, we address issues in AADL for its use in the field of wireless sensor networks. We have developed a plug-in for ANDES, called ModelGeneration, which bridges the gap between the semantics needed for sensor networks and the syntax supported by AADL. This makes it easy for sensor network designers to build system models that are intuitive to them. Furthermore, ANDES is extensible and new analysis techniques can be easily incorporated into the toolset. I.
SenShare: Transforming Sensor Networks Into Multi-Application Sensing Infrastructures
"... Abstract. Sensor networks are typically purpose-built, designed to support a single running application. As the demand for applications that can harness the capabilities of a sensor-rich environment increases, and the availability of sensing infrastructure put in place to monitor various quantities ..."
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Cited by 18 (1 self)
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Abstract. Sensor networks are typically purpose-built, designed to support a single running application. As the demand for applications that can harness the capabilities of a sensor-rich environment increases, and the availability of sensing infrastructure put in place to monitor various quantities soars, there are clear benefits in a model where infrastructure can be shared amongst multiple applications. This model however introduces many challenges, mainly related to the management of the communication of the same application running on different network nodes, and the isolation of applications within the network. In this work we present SenShare, a platform that attempts to address the technical challenges in transforming sensor networks into open access infrastructures capable of supporting multiple co-running applications. SenShare provides a clear decoupling between the infrastructure and the running application, building on the concept of overlay networks. Each application operates in an isolated environment consisting of an in-node hardware abstraction layer, and a dedicated overlay sensor network. We further report on the deployment of SenShare within our building, which presently supports the operation of multiple sensing applications, including office occupancy monitoring and environmental monitoring. 1
Empath: a Continuous Remote Emotional Health Monitoring System for Depressive Illness
"... Depression is a major health issue affecting over 21 million American adults that often goes untreated, and even when undergoing treatment it is hard to monitor the effectiveness of the treatment. To address these issues, we have created a real-time depression monitoring system for the home. This sy ..."
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Cited by 16 (4 self)
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Depression is a major health issue affecting over 21 million American adults that often goes untreated, and even when undergoing treatment it is hard to monitor the effectiveness of the treatment. To address these issues, we have created a real-time depression monitoring system for the home. This system runs 24/7 and can potentially detect the early signs of a depression episode, as well track progress managing a depressive illness. A cohesive set of integrated wireless sensors, a touch screen station, mobile device, and associated software deliver the above capabilities. The data collected are multi-modal, spanning a number of different behavioral domains including sleep, weight, activities of daily living, and speech prosody. The reports generated by this aggregated data across multiple behavioral domains are aimed to provide caregivers with more accurate and thorough information about the client’s current functioning, thus helping in their diagnostic assessment and therapeutic treatment planning as well for patients in the management and tracking of their symptoms. We present data of a case study showing the value of the system, deployed over a period of two weeks in a home during a depressive episode. Larger scale studies are planned for the future.
Archetype-based design: sensor network programming for application experts, not just programming experts
- In Proc. Int. Conf. Information Processing in Sensor Networks
, 2009
"... ABSTRACT Sensor network application experts such as biologists, geologists, and environmental engineers generally have little experience with, and little patience for, general-purpose and often low-level sensor network programming languages. We believe sensor network languages should be designed fo ..."
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Cited by 13 (4 self)
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ABSTRACT Sensor network application experts such as biologists, geologists, and environmental engineers generally have little experience with, and little patience for, general-purpose and often low-level sensor network programming languages. We believe sensor network languages should be designed for application experts, who may not be expert programmers. To further that goal, we propose the concepts of sensor network application archetypes, archetype-specific languages, and archetype templates. Our work makes the following contributions.
