Distributed detection and tracking in sensor networks

McErlean, D.; Narayanan, Sridhar

doi:10.1109/acssc.2002.1196968

Cited by 34 publications

(20 citation statements)

References 7 publications

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“…The central idea is to utilize decentralized processing within the network to build a better picture of what is going on (locally), before reporting the results (centrally). This notion, which relies on collaboration among sensors, reduces false alarms and avoids sending all the sensor measurements back to a centralized processing unit [62]. In [34], accurate personnel and vehicle tracking has been achieved using wireless MSTAR sensors.…”

Section: New Perspectivesmentioning

confidence: 99%

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Fang

Antsaklis

Montestruque³

et al. 2005

IEEE Trans. Instrum. Meas.

228

144

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Abstract-In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.Index Terms-Dead reckoning, pedestrian navigation system, wireless sensor network.

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Section: New Perspectivesmentioning

confidence: 99%

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Fang

Antsaklis

Montestruque³

et al. 2005

IEEE Trans. Instrum. Meas.

228

144

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“…Most of this work is based on collaborative signal and information processing, sequential Bayesian filtering, and extended Kalman filtering [10][11][12]. Other solutions for tracking in a sensor network are based on a or Kalman filter based approach [5,13,14]. These approaches attempt to estimate the future position of a target given its past and present positions.…”

Section: Related Workmentioning

confidence: 99%

A line in the sand: a wireless sensor network for target detection, classification, and tracking

et al. 2004

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Abstract. Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensors nodes at MacDill Air Force Base in Tampa, Florida, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.

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“…Although there has been much effort to track entities using sensor networks [3][4][5], another important application is the use of such networks for fast and accurate inverse tracking of the location of bombs upon explosion [6]. Speed and accuracy of detection/tracking of the sources is obviously of critical interest.…”

Section: Example: Sensor Network For Plume Detectionmentioning

confidence: 99%

Integrated Analysis of Environment-driven Operational Effects in Sensor Networks

Park¹,

Perumalla²

2007

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There is a rapidly growing need to evaluate sensor network functionality and performance in the context of the larger environment of infrastructure and applications in which the sensor network is organically embedded. This need, which is motivated by complex applications related to national security operations, leads to a paradigm fundamentally different from that of traditional data networks. In the sensor networks of interest to us, the network dynamics depend strongly on sensor activity, which in turn is triggered by events in the environment. Because the behavior of sensor networks is sensitive to these driving phenomena, the integrity of the sensed observations, measurements and resource usage by the network can widely vary. It is therefore imperative to accurately capture the environmental phenomena, and drive the simulation of the sensor network operation by accounting fully for the environment effects. In this paper, we illustrate the strong, intimate coupling between the sensor network operation and the driving phenomena in their applications with an example sensor network designed to detect and track gaseous plumes.

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Distributed detection and tracking in sensor networks

Cited by 34 publications

References 7 publications

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience

A line in the sand: a wireless sensor network for target detection, classification, and tracking

Integrated Analysis of Environment-driven Operational Effects in Sensor Networks

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