Huseyin Yigitler

Non-invasive respiration rate monitoring using a single COTS TX-RX pair

Respiratory rate is an important vital sign that can indicate progression of illness but to also predict rapid decline in health. For the purpose, non-contact monitoring systems are becoming more popular due to the self-evident increase in patient comfort. As a cost effective solution for non-invasive breathing monitoring, utilizing the received signal strength measurements of inexpensive transceivers has been proposed. However, the applicability of the available solutions is limited since they rely on numerous sensors. In this work, considerable improvement is made, and a respiratory rate monitoring system based on a single commercial off-the-shelf transmitter-receiver pair is presented. Methods that enable estimation and enhance the accuracy are presented and their effects are evaluated. Moreover, it is empirically demonstrated that the performance of the system is comparable to the accuracy of a high-end device for 3-4 orders of magnitude less price; achieving mean absolute error of 0.12 breaths per minute in the most realistic scenario of the experiments.

Localization Services for Online Common Operational Picture and Situation Awareness

Many operations, be they military, police, rescue, or other field operations, require localization services and online situation awareness to make them effective. Questions such as how many people are inside a building and their locations are essential. In this paper, an online localization and situation awareness system is presented, called Mobile Urban Situation Awareness System (MUSAS), for gathering and maintaining localization information, to form a common operational picture. The MUSAS provides multiple localization services, as well as visualization of other sensor data, in a common frame of reference. The information and common operational picture of the system is conveyed to all parties involved in the operation, the field team, and people in the command post. In this paper, a general system architecture for enabling localization based situation awareness is designed and the MUSAS system solution is presented. The developed subsystem components and forming of the common operational picture are summarized, and the future potential of the system for various scenarios is discussed. In the demonstration, the MUSAS is deployed to an unknown building, in an ad hoc fashion, to provide situation awareness in an urban indoor military operation.

A Three-State Received Signal Strength Model for Device-Free Localization

The indoor radio propagation channel is typically modeled as a two-state time-variant process, where one of the states represents the channel when the environment is static, whereas the other state characterizes the medium when it is altered by people. In this paper, the aforementioned process is augmented with an additional state. It is shown that the changes in received signal strength are dictated by: 1) electronic noise, when a person is not present in the monitored area; 2) reflection, when a person is moving in the close vicinity of line-of-sight; and 3) shadowing, when a person is obstructing the line-of-sight component of the transmitter–receiver pair. Statistical and spatial models for the three states are derived, and the models are empirically validated. Based on the models, a link line monitoring system is designed, which aims to, first, estimate the temporal state of the channel using a hidden Markov model, and, second, track a person using a particle filter. The results suggest that the presented system outperforms other state-of-the-art systems in terms of localization accuracy while increasing size of the links sensing region.

On Log-Normality of RSSI in Narrowband Receivers under Static Conditions

A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.

Localizing Multiple Objects Using Radio Tomographic Imaging Technology

Low-data-rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio-frequency magnetic field and can be detected by observing the change in radio attenuation. This gives the basis for the radio tomographic imaging technology, which has recently been developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multiobject intrusion detection and localization is a challenging issue. This paper proposes an extended variational Bayesian Gaussian mixture model (VB-GMM) algorithm in treating this problem. The extended VB-GMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects and uses a modified version of the variational Bayesian approach for model estimation. Real-world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multiobject localization algorithm.

pRoot: An Adaptable Wireless Sensor-Actuator Hardware Platform

The relative significance of available processing/memory resources and cost/power constraints for wireless sensor-actuator nodes depends on the application and use-case scenario. As an example, wireless automation applications usually have diverse processing/memory resource demands and energy constraints, and they bring forth development feasibility as one of the important decision criteria. For similar applications, adaptable platforms, supporting scaling between processing-memory resources and power consumption, and providing flexibility in use-case dependent hardware modifications are preferable. To the best of our knowledge, the available node platforms cannot be easily modified to support resource scaling and hardware flexibility simultaneously. In this paper, an adaptable node hardware architecture is introduced. The proposed platform can be utilized in several of roles for applications with diverse requirements, lowering the development-effort compared to alternative implementations. The practical problems of the architecture are elaborated and the details of their solutions are given. The feasibility of the introduced architecture is shown through an example implementation using off-the-shelf components, the pRoot node.

A management framework for device-free localization

Received signal strength based device-free localization (RSS-based DFL) is recently gaining momentum as an indoor localization technology, since it enables locating people that are not cooperating with the system by carrying a device. The technology is based on monitoring the signal strength measurements of the many wireless transceivers that are deployed in the monitored area. The measurement modality can be used to accurately localize people and recent works have shown that it can be used e.g. in residential monitoring. Despite the recent advances in enhancing the accuracy of RSS-based DFL, real-world requirements such as energy efficiency and adaptation to the changing communication conditions are often neglected in the related literature. In this paper we present a management framework for RSS-based DFL which enables not only monitoring the environment and network, but to also interact with the dynamic environment and varying wireless channel. With the proposed framework, it is possible to make a considerable step forward so that RSS-based DFL can be used in long-term and real-world deployments.

Experimental accuracy assessment of radio tomographic imaging methods

The received signal strength based radio tomographic imaging recently gained momentum as an unobtrusive localization method. The localization accuracy performance evaluation of such systems require complex experiment campaigns. In particular, performance dependence on the test subject parameters should be considered. In this work, we introduce a radio tomographic imaging experimentation system based on an autonomously navigating robot. The robot is equipped with a cylindrical container filled with a special liquid simulating human tissue at 2.4 GHz. We use this system to assess localization accuracy of different imaging methods. The results suggest that Network Shadowing model based method performs as good as more complex methods.

Recursive clock skew estimation for wireless sensor networks using reference broadcasts

Reference broadcast-based time synchronisation protocols are appreciated by the wireless sensor network community for their low-power demands. The underlying time relation characteristics of the broadcast-based time synchronisation schemes are prone to the effects resulting from the time record correlations. The recursive equivalents of the existing time synchronisation methods have large clock skew estimation error variance since these methods ignore the effect of correlation. In this study, the authors propose a novel recursive clock synchronisation algorithm based on a time relation model that reflects the effect of correlation. The authors utilise the maximum likelihood estimator to reach an asymptotically consistent and efficient clock skew estimator. The authors theoretically evaluate the performance of the developed estimator and compare it with the existing ones. Of the methods studied, the proposed estimator achieved the smallest estimation error variance. Experimental validation suggests an accuracy of less than one tick at synchronisation instants for a 6 h experiment.

Detector Based Radio Tomographic Imaging

Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each links measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.