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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 d...
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The wireless cooperative localization plays a key role in location-aware service. However, its objective function, e.g., the posteriori probability function, is commonly nonconvex due to nonlinear measurement function and/or non-Gaussian system disturbance. Moreover, due to the unavoidable reference node location error, the associated objective fun...
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... Recently, RF-sensing technique has been proposed to detect human motion by analysing its impact on the parameters of surrounding wireless links. RF-sensing exploits the phenomenon that the human body, which consists of approximately 60% water, reflects, scatters and attenuates radio waves [37] and causes signal strength disturbances in a ambient wireless links which can be measured by conventional wireless transceivers. Since the pattern of these disturbances depends on the human position in relation to a mesh of wireless links, the person's location, motion or activity can be detected using machine learning and statistical analysis techniques. ...
Maintaining certain physical activity levels is important to prevent or delay the onset of many medical conditions such as diabetes, or mental health disorders. Traditional calorie estimation methods require wearing devices, such as pedometers, smart watches or smart bracelets, which continuously monitor user activity and estimate the energy expenditure. However, wearable devices may not be suitable for some patients due to the need for periodic maintenance, frequent recharging and having to wear it all the time. In this paper we investigate a feasibility of a device- free human energy expenditure estimation based on RF-sensing, which recognises coarse-grained user activity, such as walking, standing, sitting or resting by monitoring the impact of a person’s activity on ambient wireless links. The calorie estimation is then based on Metabolic Equivalent concept that expresses the energy cost of an activity as a multiple of a person’s basal metabolic rate using Harrison-Benedict model. The experimental evaluation using low cost IEEE 802.15.4 transceivers demonstrated that the approach estimated energy expenditure within an indoor environment within 7.4% to 41.2% range when compared to a FitBit Blaze bracelet.
... The operation of network is managed by the DFL management software running on a general purpose computer, which accesses the network through the sink node. The communication schedule, application software running on the nodes and the sink, and management software are as described in [43]. During the experiments, the network is configured to transmit frames approximately every 5 ms over channels 11, 18 and 26 at 2.4 GHz ISM band of of IEEE 802.15.4 MAC/PHY standard with 0 dBm transmission power. ...
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 solve an associated inverse problem using algebraic or compressed sensing reconstruction algorithms. We propose an alternative imaging method that reconstructs spatial field of occupancy using a back-projection based reconstruction algorithm. The introduced system has the following advantages over the other imaging based methods: i.) significantly lower computational complexity such that no floating point multiplication is required; ii.) each link's measured data is compressed to a single bit, providing improved scalability; iii.) 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.
... Robot and human imitating container. All dimensions are in mm on the nodes and the sink, and management software are as described in[15]. During the experiments, the network is configured to transmit frames approximately every 5 ms over channels11, 18 and 26 at 2.4 GHz ISM band of of IEEE 802.15.4 MAC/PHY standard with 0 dBm transmission power. ...
... First, the computational complexity of the detector can be scaled for embedded implementations in the node to allow local decisions and distributed processing. Second, the power requirements of the DFL system can be decreased by duty-cycling when the environment is not occupied, which is a preliminary requirement of low-power deployments [21]. Third, the required number of nodes can be substantially reduced by detecting human-induced reflections since a TX-RX pair can monitor a larger area around the LoS. ...
... In order to simplify the analysis, an idealized deployment scenario, illustrated in Fig. 1a, is considered. The TX follows a multi-channel transmission schedule and the RX is programmed to listen Fig. 1b, which is the single transmitter version of the schedule discussed in [21]. The narrowband communication system is presumed to fulfill the following assumptions. ...
... For a single TX, it is easy to communicate over multiples of frequency channels and satisfy the aforementioned condition. On the other hand, for multiple transmitters the condition is satisfied by the schedule introduced in [21] because each TX broadcasts sequentially on the different channels before changing the transmitting node. However, with large number of nodes and channels, the coherence time of the propagation channel is likely to be exceeded using the schedule in [7]. ...
Radio frequency sensor networks are becoming increasingly popular as an
indoor localization and monitoring technology for gaining unobtrusive
situational awareness of the surrounding environment. The localization effort
in these networks is built upon the well-established fact that the received
signal strength measurements vary due to a person's presence on the
line-of-sight of a transmitter-receiver pair. To date, modeling this decrease
in received signal strength and utilizing it for localization purposes have
received a considerable amount of attention in the research field. However,
when the person is in the close vicinity of the line-of-sight but not
obstructing it, the signal reflected from the human body is also affecting the
received signal strength and can be used for occupancy assessment purposes. In
this paper, we first model the effect of human-induced reflections as a
function of communication frequency, and then use the model as a basis for
energy based occupancy detection. The derived methods are evaluated numerically
and the detection probability of the proposed detector is validated with
experimental data. The results suggest that when more than eight frequency
channels are utilized, presence of a person can be detected using RSS
measurements of a single transmit-receive pair with detection probability
higher than 0.95 and false alarm probability less than 0.01 in an area of 2 m x
2.5 m. Moreover, the important implications of the studied methods on the
available narrowband radio frequency sensor network applications are discussed
in detail.
... Network management serves two purposes in RSS-based DFL: first, the network can be configured easily, reducing the deployment time; second, it offers the possibility to adapt to changing communication conditions, for instance, the network can change the frequency channel of operation if needed. For these reasons, a network monitoring and management framework is designed and utilized for the purpose of the MUSAS [55]. ...
The evolution of mobile social networks and the availability of online check-in services, such as Foursquare and Gowalla, have initiated a new wave of research in the area of venue recommendation systems. Such systems recommend places to users closely related to their preferences. Although venue recommendation systems have been studied in recent literature, the existing approaches suffer from various issues, such as: (a) data sparseness, (b) cold start, and (c) scalability. Moreover, many existing schemes are limited in functionality, as the generated recommendations do not consider group of "friends" type situations. Furthermore, the traditional systems do not consider the effect of real-time physical factors (e.g., traffic and weather conditions) on recommendations. To address the aforementioned issues, this paper proposes a novel cloud based recommendation framework OmniSuggest that utilizes: (a) Ant colony algorithms, (b) social filtering, and (c) hub and authority scores, to generate optimal venue recommendations. Unlike existing work, our approach suggests venues at a finer granularity for an individual or a "group" of friends with similar interest. Comprehensive experiments are conducted with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than many state of the art schemes.
The advancements in wireless communication technologies have enabled new sensing possibilities where the channel measurements of the radio are used for inferring physical changes in the surrounding environment. Relating the channel measurements to the location and actions of people has been of particular interest due to the wide range of application opportunities enabled by such a sensing capability. As an example, the low-amplitude received signal measurements of low-cost wireless communication systems have been used to detect the presence of a person, to locate and track them, identify gestures and activities of the person, and even monitor their vital signs. This chapter aims to give a deep insight on how people influence radio signals, how these effects are observed at the receiver antenna, and how the measurement system impacts the recorded measurements. These topics are presented to shed light on the relation between the location of people and signal strength measurements of narrowband radios.
Received signal strength based device-free localization has attracted
considerable attention in the research society over the past years to locate
and track people who are not carrying any electronic device. Typically, the
person is localized using a spatial model that relates the time domain signal
strength measurements to the person's position. Alternatively, one could
exploit spectral properties of the received signal strength which reflects the
rate at which the wireless propagation medium is being altered, an opportunity
that has not been exploited in the related literature. In this paper, the power
spectral density of the signal strength measurements are related to the
person's position and velocity to augment the particle filter based tracking
algorithm with an additional measurement. The system performance is evaluated
using simulations and validated using experimental data. Compared to a system
relying solely on time domain measurements, the results suggest that the
robustness to parameter changes is increased while the tracking accuracy is
enhanced by 50% or more when 512 particles are used.
