Energy Efficient Multi-Object Tracking in Sensor Networks

Fuemmeler, J.A.; Veeravalli, Venugopal V.

doi:10.1109/tsp.2010.2046896

Cited by 31 publications

(20 citation statements)

References 12 publications

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“…A computable lower bound on the optimal trade-off between energy consumption and tracking performance is presented, and strategies that come close to this bound are presented. Furthermore, it is shown by Fuemmeler & Veeravalli [133] that the nearly optimal strategies result in considerable savings in energy over [134], and sleeping policies for under general models for object movement, object sensing and tracking cost are presented by Fuemmeler et al [135].…”

Section: (I) Reduced Complexity Metrics For Trackingmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

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110

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Section: (I) Reduced Complexity Metrics For Trackingmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

Self Cite

110

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“…A kernel P is defined such that P(x,y) is the probability that the next state is y.Here the current state is termed to be x.Then "t" time steps towards the future is predicted by the equation by defining P1=P inductively as in [10] In order to provide the means of centralized control, extra node called as centralized controller is assumed [10]. This controller keeps track of the state of the network and it is responsible for assigning sleep times for the sensors that are awake [10]. For a time period of one unit, each sensor is assumed to remain awake.…”

Section: Pomdp Formulationmentioning

confidence: 99%

“…As the Battery life of the sensor decreases corresponding variations also reflect in the energy cost. M athematical representation of the energy cost is represented as [10] Tracking error: The tracking error is also addressed as the cost which can further be subdivided into two components. They are observation error and data association error.…”

mentioning

confidence: 99%

A Comprehensive Review on Energy Conserved Multiobject Tracking over Wireless Sensor Networks

S.P.Ponsudha¹,

Priya²,

Edward³

2011

IJCA

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Sensor networks are used in wide variety of real-world applications including detecting, tracking and monitoring. One of the main issues in the deployment of the sensor nodes in the field of target tracking is power optimization. Various methods of target tracking are available such as making improvement in hardware, processor design etc. But still, an optimized solution to this problem is not available. To rectify this problem here we formulate the mechanism that the sensors may be put into a sleep mode with a timer that determines the sleep duration. The assumption made is that a sensor that is asleep cannot be with or waken up, and hence the sleep duration depends on the timely information it receives during the awaken period. The objective is to track the location of the objects to within the accuracy of the range of the sensor. The result is in the form of a trade-off between the energy savings and the tracking errors that result from the sleeping policies of the sensors. Sub-optimal sleeping policies that optimize this trade-off are designed, and their performance in a different tracking architecture is examined. General TermsSub optimal sleeping policies, partially observable state based formulations.

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“…Due to difficulties of a centralized processing to handle multi-target problems, the authors in [4] have studied the tracking problem by a distributed data processing approach. In [5] a special scenario is considered in which sensor nodes can be put into a sleep mode with a timer, that determines the sleep duration. By optimizing the sleep duration they show an improvement of the tracking performance in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

“…By optimizing the sleep duration they show an improvement of the tracking performance in sensor networks. In contrast to [4] and [5], we investigate the centralized scenario and determine the least reliable sensor nodes to keep them asleep for a uniform time duration, respectively. It is to mention, that our approach is more general and it can be used not only for tracking but also for detection and classification of targets, cf.…”

Section: Introductionmentioning

confidence: 99%

Power allocation for orthogonally-observed multi-target sensor networks

Zandi

Vieting

Taghizadeh

et al. 2017

2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

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Abstract-In this paper we study the performance bound of a wireless sensor network which is capable of estimating the true values of several active targets. We assume that sensors can observe each target separately, assuming either targets are orthogonal in frequency or physically distinguishable. The aforementioned assumption is of practical relevance for targets with different physical nature, e.g., heat, humidity, pressure etc. Another interesting example of separable observation is power grids where the voltage of each node can be exclusively estimated at any of its neighboring nodes by measuring the flowing current and applying the Ohm's law. Even though such a sensor network is not wireless, the current work provides a suitable framework for state estimation and planning of the smart meters in smart grids. We then propose a novel unbiased estimator. Moreover, as an additional design variable, we perform power allocation and optimal fusion of data to further improve the performance of the proposed estimator. The optimal fusion rules are provided in closed-form, while power allocation is optimally done by means of convex optimization.

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Energy Efficient Multi-Object Tracking in Sensor Networks

Cited by 31 publications

References 12 publications

Distributed inference in wireless sensor networks

Distributed inference in wireless sensor networks

A Comprehensive Review on Energy Conserved Multiobject Tracking over Wireless Sensor Networks

Power allocation for orthogonally-observed multi-target sensor networks

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