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109
Evolution and Sustainability of a Wildlife Monitoring Sensor Networks
- In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems (SenSys’10
, 2010
"... Abstract As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in u ..."
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Abstract As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments. In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment. We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.
EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks
"... This paper presents the design, implementation, and evaluation of EnviroMic, a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces m ..."
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Cited by 25 (1 self)
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This paper presents the design, implementation, and evaluation of EnviroMic, a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces motivate distributed algorithms for coordinating recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. Applications of acoustic monitoring with EnviroMic range from the study of mating rituals and social behavior of animals in the wild to audio surveillance of military targets. EnviroMic is designed for disconnected operation, where the luxury of having a basestation cannot be assumed. We implement the system on a TinyOS-based platform and systematically evaluate its performance through both indoor testbed experiments and a preliminary outdoor deployment. Results demonstrate up to a 4-fold improvement in effective storage capacity of the network compared to uncoordinated recording. Index Terms Sensor networks, applications, acoustics, distributed storage, group management
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.
Prediction or not? an energy-efficient framework for clustering-based data collection in wireless sensor net-2093 (a) (b) (c
"... Abstract—For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult—it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the rea ..."
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Cited by 21 (6 self)
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Abstract—For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult—it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques. Index Terms—Sensor networks, algorithm/protocol design, clustering, adaptive prediction. Ç 1
TOSThreads: Thread-safe and Non-Invasive Preemption in TinyOS
"... Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an eventdriven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded progr ..."
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Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an eventdriven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code. We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92 % and that dynamic linking and loading takes as little as 90 ms for a representative sensing application. We compare different programming models built using TOSThreads, including standard C with blocking system calls and a reimplementation of Tenet. Additionally, we demonstrate that TOSThreads is able to run computationally intensive tasks without adversely affecting the timing of critical OS services.
Distributed segmentation and classification of human actions using a wearable sensor network
- in Proceedings of the CVPR Workshop on Human Communicative Behavior Analysis
, 2008
"... We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions tha ..."
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Cited by 15 (3 self)
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We propose a distributed recognition method to classify human actions using a low-bandwidth wearable motion sensor network. Given a set of pre-segmented motion sequences as training examples, the algorithm simultaneously segments and classifies human actions, and it also rejects outlying actions that are not in the training set. The classification is distributedly operated on individual sensor nodes and a base station computer. We show that the distribution of multiple action classes satisfies 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 representation. We further provide fast linear solvers to compute such representation via ℓ 1-minimization. Using up to eight body sensors, the algorithm achieves state-of-the-art 98.8% accuracy on a set of 12 action categories. We further demonstrate that the recognition precision only decreases gracefully using smaller subsets of sensors, which validates the robustness of the distributed framework. 1.
Dynamic Data Compression in Multi-hop Wireless Networks ∗
"... Data compression can save energy and increase network capacity in wireless sensor networks. However, the decision of whether and when to compress data can depend upon platform hardware, topology, wireless channel conditions, and application data rates. Using Lyapunov optimization theory, we design a ..."
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Data compression can save energy and increase network capacity in wireless sensor networks. However, the decision of whether and when to compress data can depend upon platform hardware, topology, wireless channel conditions, and application data rates. Using Lyapunov optimization theory, we design an algorithm called seec that makes joint compression and transmission decisions with the goal of minimizing energy consumption. A practical distributed variant, dseec, is able to achieve more than 30 % energy savings and adapts seamlessly across a wide range of conditions, without explicitly taking topology, application data rates, and link quality changes into account.
Energy-Efficient Image Compression for Resource-Constrained Platforms
"... Abstract—One of the most important goals of current and future sensor networks is energy-efficient communication of images. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG compressi ..."
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Abstract—One of the most important goals of current and future sensor networks is energy-efficient communication of images. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG compression followed by transmission of the compressed image data. JPEG compression computations are mapped onto various resource-constrained platforms using a design environment that allows computation using the minimum integer and fractional bit-widths needed in view of other approximations inherent in the compression process and choice of image quality parameters. Advanced applications of JPEG, such as region of interest coding and successive/progressive transmission, are also examined. Detailed experimental results examining the tradeoffs in processor resources, processing/transmission time, bandwidth utilization, image quality, and overall energy consumption are presented. Index Terms—Fixed-point arithmetic, image coding, image communication. I.
Energy and Performance Evaluation of Lossless File Data Compression on Server Systems
, 2009
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Efficient Sensor Network Reprogramming through Compression of Executable Modules
"... Abstract—Software in deployed sensor networks needs to be updated to introduce new functionality or to fix bugs. Reducing dissemination time is important because the dissemination disturbs the regular operation of the network. We present a method for reducing the dissemination time and energy consum ..."
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Cited by 11 (0 self)
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Abstract—Software in deployed sensor networks needs to be updated to introduce new functionality or to fix bugs. Reducing dissemination time is important because the dissemination disturbs the regular operation of the network. We present a method for reducing the dissemination time and energy consumption based on compression of native code modules. Code compression reduces the size of the software update, but the decompression on the sensor nodes requires processing time and energy. We quantify these trade-offs for seven different compression algorithms. Our results show that GZIP has the most favorable trade-offs, saving on average 67 % of the dissemination time and 69 % of the energy in a multi-hop wireless sensor network. I.
