See-Through Walls: Motion Tracking Using Variance-Based Radio Tomography Networks

Wilson, Joey; Patwari, Neal

doi:10.1109/tmc.2010.175

Cited by 455 publications

(438 citation statements)

References 19 publications

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“…In variance-based RTI [9], the change in RSS is quantified as the short-term unbiased sample variance of the RSS measurements:…”

Section: Image Estimationmentioning

confidence: 99%

“…of link l. The spatial model used in RTI [9,11,12,16,30,31] is an ellipse having the foci located at the transmitter and receiver. According to this model, the voxels that are located outside of the ellipse have their weight set to zero, while the voxels that are located within the ellipse have their weight set to a constant which is inversely proportional to the area Γ l of the ellipse:…”

Section: Image Estimationmentioning

confidence: 99%

“…In [9,15,31], the estimated trajectory followed by the person is smoothed by recursively applying a Kalman filter [33] on the position estimates. In this chapter, we present results related only to the localization of one person.…”

Section: Image Estimationmentioning

confidence: 99%

“…Without recalibration, the RTI system would not be able to achieve accurate localization over an extended period of time and would need to be stopped, recalibrated and restarted frequently. In [9,30,36,37], different methods to adapt to the dynamic environment are presented. In [30], the reference RSSr l,c (k) of link l on channel c at time k is calculated using a moving average:…”

Section: On-line Calibration For Aal Applicationsmentioning

confidence: 99%

“…Each device broadcasts packets and stores the RSS of the packets received from all the other devices forming the network. When people are located or move in the environment, they modify the way the radio signals transmitted by the nodes propagate [1,2], by shadowing [3], reflecting [4], diffracting [5], or scattering [6] a subset of their multipath components [1][2][3][4][5][6][7][8][9]. The effect of people on the wireless links is reflected in the RSS measurements collected by the nodes [10].…”

Section: Introductionmentioning

confidence: 99%

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Radio Tomographic Imaging for Ambient Assisted Living

Bocca

Kaltiokallio

Patwari

2013

Communications in Computer and Information Science

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Abstract. Accurate localization of people in indoor and domestic environments is one of the key requirements for ambient assisted living (AAL) systems. This chapter describes how the received signal strength (RSS) measurements collected by a network of static radio transceivers can be used to localize people without requiring them to wear or carry any radio device. We describe a technique named radio tomographic imaging (RTI), which produces real-time images of the change in the radio propagation field of the monitored area caused by the presence of people. People's locations are inferred from the estimated RTI images. We show results from a long-term deployment in a typical single floor, one bedroom apartment. In order to deal with the dynamic nature of the domestic environment, we introduce methods to make the RTI system self-calibrating. Experimental results show that the average localization error of the system is 0.23 m. Moreover, the system is capable of adapting to the changes in the indoor environment, achieving high localization accuracy over an extended period of time.

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“…In variance-based RTI [9], the change in RSS is quantified as the short-term unbiased sample variance of the RSS measurements:…”

Section: Image Estimationmentioning

confidence: 99%

Section: Image Estimationmentioning

confidence: 99%

Section: Image Estimationmentioning

confidence: 99%

Section: On-line Calibration For Aal Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Radio Tomographic Imaging for Ambient Assisted Living

Bocca

Kaltiokallio

Patwari

2013

Communications in Computer and Information Science

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A context‐aware framework for modelling and verification of smart parking systems in urban cities

Haque

Zulfiqar

Ahmed

et al. 2019

Concurrency and Computation

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Parking spaces have been considered as vital resources in urban areas. Finding parking spaces in jam-packed areas is often challenging, stressful, and uncertain for the drivers that causes traffic congestion with a consequent of wastage of time, fuel, and increase of pollution. In recent years, context-aware computing paradigm has been considered to be the most effective approach to address these kinds of issues. Context-aware systems acquire and understand contextual information according to the current situation, perform reasoning, and then act intelligently on behalf of the user. These applications often run on tiny resource-bounded smart devices with the incorporation of embedded or attached sensors on these devices and they often exhibit complex and adaptive behaviour. In this paper, we propose a context-aware parking application framework to assist drivers in finding parking slots dynamically while moving and/or arriving at the destination. We optimize the context-aware parking framework with bounds on computational resources for the decision support dynamically in a highly decentralized environment. To illustrate the use of the proposed system, we model the context-aware parking system using UPPAAL model checker for formal analysis and verify the correctness properties of the system.

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