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检索条件"任意字段=First IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2005"

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A Node's Life: Increasing WSN Lifetime by Dynamic Voltage Scaling

A Node's Life: Increasing WSN Lifetime by Dynamic Voltage Sc...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Kulau, Ulf Buesching, Felix Wolf, Lars Tech Univ Carolo Wilhelmina Braunschweig Inst Operating Syst & Comp Networks D-38106 Braunschweig Germany

ISBN: (纸本)9780769550411

In mobile applications, like wireless sensor networks, it is often inevitable to use a location-independent source of energy. Wherever the batteries capacity or inefficient energy harvesting limits the lifetime, other solutions have to be implemented to increase the energy efficiency. Dynamic Voltage Scaling (DVS) is a well known technique with the ability to adapt the voltage level to the actual system load to save energy. While it has been used in many application areas, DVS has not been studied sufficiently for wireless sensor nodes. In this paper, we present a method to estimate the gain of DVS. We study DVS theoretically as well as in a practical manner, i.e., we using a prototype implementation of a DVS capable wireless sensor node. With the theoretical considerations we were able to optimize DVS for an 8-Bit ULP micro controller by a combination of DVS and dynamic power management (DPM). In addition, we proved the calculated results with an extensive practical evaluation of the concept using a prototypical implementation. Moreover, we analysed the effect of voltage scaling on other components of the sensor node (transceiver, sensors and memory).

关键词： wireless sensor node energy efficiency voltage scaling hardware architecture hardware design dvs

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Reliability-Based Splitting Algorithms for Time-Constrained distributed Detection in WSNs

Reliability-Based Splitting Algorithms for Time-Constrained ...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Laitrakun, Seksan Coyle, Edward J. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9780769550411

We consider distributed detection applications for a fusion center that has a limited time to collect, weight and fuse local decisions to produce a global decision in a wireless sensor network that uses a random access protocol. When this time is not long enough to collect decisions from all nodes in the network, a strategy is needed for collecting those with the highest reliability. This is accomplished by incorporating a reliability-based splitting algorithm into the random access protocol: the collection time is divided into frames and only nodes with a specified range of reliabilities compete for the channel within each frame. Nodes with the most reliable decisions attempt transmission in the first frame;nodes with the next most reliable set of decisions attempt in the next frame;etc. To ensure that the results we derive are lower bounds on the detection performance that would be seen in practice, the local decisions that arrive within a frame are assigned the smallest reliability in the range associated with that frame. Two performance measures are used: Detection Error Probability (DEP) and Asymptotic Relative Efficiency (ARE) of the proposed scheme relative to a TDMA-based scheme. We then show how to minimize the DEP by determining the reliability intervals that define which nodes attempt to transmit in each frame. Intervals that maximize the channel throughput will often, but not always, minimize the DEP of the proposed scheme. Necessary conditions for the optimality of the maximum throughput intervals are thus derived. Numerical results indicate that these conditions are often sufficient when the observation noise is Gaussian. From the ARE, we determine when the proposed scheme asymptotically outperforms a TDMA-based scheme.

关键词： Gaussian noise probability radio access networks radio transmitters random processes sensor fusion telecommunication network reliability time division multiple access wireless sensor networks

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Trade-offs of Forecasting Algorithm for Extending WSN Lifetime in a Real-world Deployment

Trade-offs of Forecasting Algorithm for Extending WSN Lifeti...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Aderohunmu, Femi A. Paci, Giacomo Brunelli, Davide Deng, Jeremiah D. Benini, Luca Purvis, Martin Univ Otago Dept Informat Sci Dunedin New Zealand

ISBN: (纸本)9780769550411

Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96% transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5 degrees C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.

关键词： autoregressive moving average processes data reduction least mean squares methods prediction theory radio transmitters sensor placement telecommunication network reliability wireless sensor networks

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A Hybrid sensor System for Indoor Air Quality Monitoring

A Hybrid Sensor System for Indoor Air Quality Monitoring

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Xiang, Yun Piedrahita, Ricardo Dick, Robert P. Hannigan, Michael Lv, Qin Shang, Li Univ Michigan Dept EECS Ann Arbor MI 48109 USA

ISBN: (纸本)9780769550411

Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error;(2) developed algorithms for hybrid sensor network construction;and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.

关键词： air pollution measurement mobility management (mobile radio) sensor placement wireless sensor networks

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On the Effectiveness of Energy Metering on Every Node

On the Effectiveness of Energy Metering on Every Node

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Li, Qiang Martins, Marcelo Gnawali, Omprakash Fonseca, Rodrigo Univ Houston Dept Comp Sci Houston TX 77004 USA

ISBN: (纸本)9780769550411

Making wireless sensor node platforms energy-efficient is one of the major research thrusts in the sensor network community. Energy metering lies at the foundation of this research, either by providing direct measurements for profiling, or by serving as the base for the formulation and fitting of energy-usage models. Most of the literature and tools, however, make their measurements on a very small subset of the node population, and usually at a single point in time, before deployment. In this paper we set out to evaluate the cost, in loss of precision, of not having constant and ubiquitous measurement. Through experiments on a 240-node sensor-network testbed, we find that the variations in energy consumption due to temperature change are small, and we establish a model between environmental temperature changes and power consumption of Quanto testbed motes. We also find that different nodes of the same kind can have up to 15% variation in power draw, suggesting a need to deploy instrumentation on a subset of nodes. We quantify the energy estimation error of different metering techniques and characterize the conditions in which the errors disappear. Overall, we find that a small number of measurements in time and across nodes is adequate for accurate estimation of network-wide energy use.

关键词： energy conservation power consumption power meters wireless sensor networks

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Crowdsensing Maps of On-Street Parking Spaces

Crowdsensing Maps of On-Street Parking Spaces

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Coric, Vladimir Gruteser, Marco Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9780769550411

It has been estimated that traffic congestion costs the world economy hundreds of billions of dollars each year, increases pollution, and has a negative impact on the overall quality of life in metropolitan areas. A significant part of congestion in urban areas is due to vehicles searching for on-street parking. Detailed and accurate on-street parking maps can help drivers easily locate areas with large numbers of legal parking spaces and thus relieve congestion. In this paper, we address the problem of mapping street parking spaces using vehicles' preinstalled parking sensors. In particular, we focus on identifying legal parking spaces from crowdsourced data, whereas earlier work has largely assumed that such maps of legal spaces are given. We demonstrate that crowdsensing data from vehicle parking sensors can be used to classify on-street areas into legal/illegal parking spaces. Based on more than 2 million data points collected in Highland Park, NJ and downtown Brooklyn, NY areas, we show that on-street parking maps can be estimated with an accuracy of similar to 90% using proposed weighted occupancy rate thresholding algorithm.

关键词： road traffic sensor placement traffic control

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Comparison of Accuracy Estimation Approaches for sensor Networks

Comparison of Accuracy Estimation Approaches for Sensor Netw...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Wen, Hongkai Xiao, Zhuoling Symington, Andrew Markham, Andrew Trigoni, Niki Univ Oxford Dept Comp Sci Oxford OX1 3QD England

ISBN: (纸本)9780769550411

With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. first, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user's knowledge of the application scenario.

关键词： estimation theory probability state estimation wireless sensor networks

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Model-Driven Accuracy Bounds for Noisy sensor Readings

Model-Driven Accuracy Bounds for Noisy Sensor Readings

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Hasenfratz, David Saukh, Olga Thiele, Lothar Swiss Fed Inst Technol Comp Engn & Networks Lab Zurich Switzerland

ISBN: (纸本)9780769550411

Wireless sensor networks are increasingly used in application scenarios where a high data quality is inevitable, e.g., the control of industrial production areas. Nevertheless, many deployments must live with strict constraints regarding the sensing hardware and may not employ newest sensing technologies, e.g., due to limited energy budget, size, and bandwidth. Additionally, many applications would benefit from not only gathering absolute sensor readings but also knowing the quality of their low-cost sensor measurements. In this paper, we introduce a model-driven approach that (i) provides reliable accuracy bounds for individual noisy sensor readings and (ii) detects systematic and transient sensor errors. We apply our method to static and mobile real-world deployments of noisy and unstable low-cost sensors by analyzing large sets of urban temperature and ozone measurements. We find that the proposed algorithm successfully calculates precise accuracy bounds. We compare them to measurements of high-quality instruments and show that up to 96% of the reference measurements are inside the computed accuracy bounds in the static scenario and up to 94% in the mobile scenario. This is surprisingly high for the used low-cost sensors. By analyzing data from our static long-term deployment, we reveal that the ozone sensor's reliability is dependent on seasonal weather conditions.

关键词： wireless sensor networks

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Group Communication in Constrained Environments using CoAP-based Entities

Group Communication in Constrained Environments using CoAP-b...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Ishaq, Isam Hoebeke, Jeroen Van den Abeele, Floris Moerman, Ingrid Demeester, Piet Ghent Univ iMinds Dept Informat Technol INTEC Ghent Belgium

ISBN: (纸本)9780769550411

The Constrained Application Protocol (CoAP) is a new Internet protocol that is currently being standardized. CoAP allows access to the drastically increasing number of smart objects and their sensing resources from virtually anywhere. It is a light-weight protocol designed to cope with the restrictions imposed by the limited resources (CPU, memory, power,.) of many smart objects. Depending on the application, information from individual objects might not be sufficient, reliable, or useful. An application may need to aggregate and/or compare data from a group of objects in order to obtain accurate results. Although multicast may be used to transmit the same request to several objects, multicast communication with smart objects has some disadvantages. Programming individual requests is another solution but lacks flexibility and opportunities for reusability. In this paper we propose a novel CoAP-based approach for communication with a group of resources across multiple smart objects. This approach organizes the group of resources that should be accessed into a new CoAP resource, called an entity, and nicely integrates several important aspects of entity management: creation, validation, usage and manipulation. In order to demonstrate the feasibility of this approach we present an implementation and experimental validation.

关键词： Internet of Things CoAP sensors wireless sensor networks group communication entities

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Automatic sensor drift detection and correction using Spatial Kriging and Kalman filtering

Automatic Sensor drift detection and correction using Spatia...

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9th ieee international conference on distributed computing in sensor systems (dcoss)

作者： Kumar, Dheeraj Rajasegarar, Sutharshan Palaniswami, Marimuthu Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

ISBN: (纸本)9780769550411

Internet-of-Things (IoT) is a concept referring to interconnected people and objects and smart city is one of the many applications of IoT. Wireless sensor Network (WSN) is a specific technology that helps to create "Smart Cities". It aims at creating a distributed network of intelligent sensor nodes which can measure various parameters for efficient management of the city. The data thus collected through a range of sensors is processed and is delivered wirelessly in real-time to the citizens or the appropriate authorities. Since the application framework for smart city application is huge, it would require a large number of different types of sensors for its implementation and the project could be viable only if we use low resolution, low precision but inexpensive sensors. The sensors in sensor network can suffer from random or systematic errors. Most common problem with inexpensive sensors used in WSNs for smart city applications is of drift and bias. They can be calibrated at the time of deployment, but they develop drift, which is the slow change in the reading of sensor from actual value as time progresses. In this paper we have proposed a framework to automatically detect and correct the drift of the sensor nodes to keep the WSN usable. Kriging based interpolation of the sensor readings of neighboring sensors is used to predict actual value at the sensor node and the measured drift is then kalman filtered to get correct drift estimates. We have demonstrated the results of this algorithm on real sensor data obtained from Intel Research Berkeley Laboratory deployment and shown that our system is able to detect and correct smooth drift and bias generated in the sensors. We have also shown that our system is robust with respect to the number of sensor nodes drifting and significantly outperforms the traditional averaging based interpolation methods.

关键词： Internet of Things Kalman filters geophysical techniques interpolation statistical analysis wireless sensor networks

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