We present experimental evaluations of human-induced perturbations on received-signal-strength-(RSS-) based ranging measurements for cooperative mobile positioning. To the best of our knowledge, this work is the first attempt to gain insight and understand the impact of both body loss and hand grip on the RSS for enhancing proximity measurements among neighbouring devices in cooperative scenarios. Our main contribution is represented by experimental investigations. Analysis of the errors introduced in the distance estimation using path-loss-based methods has been carried out. Moreover, the exploitation of human-induced perturbations for enhancing the final positioning accuracy through cooperative schemes has been assessed. It has been proved that the effect of cooperation is very limited if human factors are not taken into account when performing experimental activities.
With the advent of positioning systems, especially those relying on received signal strength (RSS), it became understood that the accuracy of location estimations is highly affected by the surrounding environment. A plethora of studies concerning presumed accurate solutions show custom coarse experimental activities, with limited repeatability for performance comparisons.
Additionally, the indoor environment sets great, although interesting, challenges for location-based applications since its intrinsic complexity severely affects the accuracy of measurements, causing huge signal fluctuations [
Alternatively to traditional approaches [
The human body represents an additional source of inaccuracies causing unpredictable fluctuations in the RSS. The human body contains around 70% water, which absorbs part of the 2.4 GHz WLAN radio signal causing a significant decay in the signal amplitude. In particular, the direction of the user body has attracted the research interest and has been identified as source of errors in the location estimation [
In this paper, we demonstrate that human-induced errors cannot be ignored when performing experimental activities. In fact we show that both hand-grip and body-loss effects highly compromise even the undisputed benefits of cooperative schemes, highly corrupting the expected accuracy enhancements. We also experimentally demonstrate that although the effects of hand grip and body loss generate systematic errors, if correctly accounted and cognitively exploited, rather than roughly discarded or mitigated, it is possible to enhance the beneficial effects of the cooperation among devices in terms of positioning accuracy. To the best of our knowledge, this work is the first attempt to gain insight and understand the impact of both body loss and hand grip on the RSS for enhancing ranging measurements among neighbouring devices in cooperative scenarios. Our main contribution is represented by experimental investigations on ranging estimations, analysis of the errors introduced in the distance estimation using path-loss-based methods and exploitation of human-induced perturbations information for enhancing the final positioning accuracy of cooperative schemes.
The paper is organized as follows: Section
In wireless positioning different methods and techniques, based on signal of opportunity (SoO), have been proposed and adopted for experimental activities towards commercial location-based services [
Wireless positioning.
AOA makes use of multiarray antennas to estimate the angle of the line of arrival of the signal. The final position of the target MS is located at the intersection of the lines using triangulation. As antenna arrays are large in size, the positioning estimation is suitable to be performed by the network. AOA is mainly adopted for outdoor positioning [
TOA information can be retrieved by evaluating the time of arrival of the signal from MSs to fixed reference points with known coordinates [
TDOA is based on evaluating the difference in the arrival times of signals from two different transmitters to receivers. TDOA values define hyperbolas between the two receivers on which the MS is potentially located. Positions of the MSs are then estimated at the intersection of the hyperbolas. [
Positioning methods based on RSS estimate the MS location through theoretical, statistical, or empirical models to relate the strength of the received radio signal either to the distance between the BSs/APs and MS or to the MS location directly.
Parameters in the applied models are in principle experimentally determined in order to better adapt to the application environment. RSS-based positioning methods are divided into three main categories: cell identifier-based, fingerprinting, and pathloss-based. For mass market location-based applications, the RSS is considered more easily available than the aforementioned parameters as it can be passively listened from the APs of the infrastructure (e.g., WLAN). Technically the APs periodically broadcast beacon frames containing information for network identification (e.g., SSID, BSSID, RSS, RSSI) [
In the Cell identifier method, MSs perform passive scanning of the available radio channels (e.g., WLAN), and the position estimate is reported as the position of the relative BS (or AP) from which the strongest signal is received. With such method prior information about the locations of BSs/APs and their unique media access control (MAC) addresses or custom unique identifiers are required. The main characteristic of the method is the easy deployment and implementation despite its coarse accuracy level.
Fingerprinting method is based on extensive and time-consuming experimentally built models relating recorded RSS values directly to the measured position. Models are obtained from off-line collected data from different locations sufficiently covering the area targeting positioning service [
Pathloss models are conventionally adopted to convert RSS measurements into actual distances between the MS and BSs/APs. Once the aforementioned distances are estimated, trilateration can be adopted to estimate the position of the MS where at least three fixed reference points are needed. In indoor environments, multipath and attenuation caused by walls, other structures and even people complicate the modeling of signal propagation. Because of environmental impairments, the pathloss-based accuracy is typically worse than fingerprinting [
Cooperative mobile positioning (CMP) [
Cooperative scenario.
Data-fusion algorithms.
The present section illustrates some cross-layer and cross-field topics often neglected in studies which focus on specific narrow areas without following holistic approaches that would result in a better comprehension of the underlying phenomena governing the application under investigation. In fact, there is often the selfish tendency of conducting experiments that do not result in providing a better understanding of the actual phenomena, mainly, because of their lack of generalization potential. A simplistic approach in designing experiments and carrying out measurement campaigns is undoubtedly a widespread practice in most studies in literature. In fact, it is mandatory to take into account the most sensitive biases and properly identify beforehand the potential source of uncertainty [
The performance optimization of cooperative localization algorithms requires dedicated efforts, such as the topics addressed in this section, as mobile terminals will often operate in far-from-ideal wireless channel conditions. When modeling an indoor wireless channel, it is useful to break down the propagation mechanism as a superposition of different effects (expressed in logarithmic scale):
The usual path-loss is simply defined as that portion of the propagation loss which depends uniquely on the distance between the access point and the terminal or between two terminals. However, for indoor-to-indoor links, the path loss is also dependent on the number of walls in between the transmitter and the receiver, having different weights for walls of different materials, such as thick brick walls and thin plasterboard walls. Small-scale variations of the signal power are accounted for in the fading part, which also depends on the speed of the mobile terminal.
Static shadowing is due to signal obstructions related to the shadowing effect of large objects which are time-invariant and it is also due to the emergence of destructive and constructive interference of coherent multipaths. Dynamic shadowing is the propagation portion reflecting the well-described large-scale variations around the pathloss, which typically follow a log-normal distribution and may be correlated between different links.
The operating environment of wireless communications in proximity with the user’s body is quite different from more traditional wireless networks, as one end of the link is affected by the fact that the body is in the near field of the device, dictating the non-stationary behavior of the channel. Proximity of a wireless device to external objects such as user’s body, tinted doors, and metallic doors and strongly affects the propagation environment. Nevertheless, most of the propagation phenomena happen away from the body in the surrounding space, allowing the usage of the aforementioned well-established propagation models. The reliability of a wireless link operating in such an environment is strongly conditioned by the user’s body influence, whose impact on propagation demands a rigorous analysis. There are a series of time-varying conditions, such as user movement, orientation, hand grip, and posture. Using a very simplistic approach, the human body can be modeled electromagnetically as a homogeneous dielectric cylinder, where impinging waves generate reflection and diffraction phenomena. The extent of these effects will depend on several factors such as the operating frequency, the relative dimensions of the body with respect to the wavelength, and the average composition of the human tissues. However, not all the body parts respond in the same way to the exposition to electromagnetic waves, as if one particular body part is not large with respect to the wavelength, it will have a negligible effect on the propagation mechanism and will yield small fluctuations. When we are dealing with wireless devices with low operating frequencies, their ground plane will result to be comparable with respect to the half a wavelength, becoming an integral part of the radiating structure. The presence of the user’ body naturally leads to changes in the purity of the antenna radiation pattern and input impedance, causing a corresponding reduction of the total efficiency. Antennas in common devices are very seldom directional, as they have to cope with a rich multipath environment. In fact there is no sense in designing a directional antenna that would be immediately corrupted by the effect of the user’s head and hand. When a standard wireless device rotates around a blocking object such as the user’s body, its radiation pattern is affected in such a way to emulate the behavior of a directional antenna. Moreover, the presence of a large object in between the wireless links causes a consistent drop in the RSS. Though such a drop is typically the strongest when the receiving device is totally in the shadow area of the user’s body, it might also happen that because of the multipath effect there is a signal drop of different entity. By using the RSS measured at one or multiple locations, it is possible to give a first estimation of the distance between the transmitter and the receiver making basic assumptions on the propagation model. However they end up in being inaccurate in reflecting the user-induced anomalies in a practical environment. In fact, the idealized simple assumption that RSS decreases as the receiver moves away from the transmitter often breaks down in practice and spoils even the beneficial cooperation potential in localization. An important matter is also the choice of the maximum physical radius within which human-induced disturbances may be considered to be an integral part of the antenna. The human body is nothing else but a lossy dielectric load for the antenna system point of view [
There is an undisputable need for simple but representative channel models in such a way of being able to account for the variability of the propagation in a parameterized manner. This can be achieved either by using advanced models or by properly extracting statistical models from ad hoc databases. Even though it is possible to describe antennas in a deterministic way, their performance in the presence of the user has an inherently stochastic nature. In [
The user interaction with the device is condensed in a single propagation aspect, so that the rest of propagation can be seen as a superposition of easily identifiable combinations [
The original sin of many mobile device manufacturers has been the fact of overlooking the importance of the hand grip on the communication performance. Usually usability studies are performed on a limited number of users that might not fully capture the exact complexity of real usage patterns [
Harnessing the influence of the human body on the overall measurements and performances of mobile phones can bring unexpected advantages [
Considering research on localization, the user’s impact on RSS measurements is shown to yield a 67% accuracy degradation [
In this context, heterogeneous technologies and mobile terminals coexist and cooperate with the objective of helping each other for enhancing accuracy of their estimated positions. This can be accomplished by sharing link information with peer nodes connected in ad-hoc mode and exploiting their spatial diversity with advanced positioning algorithms [
In this section we present the effect of the human body and the hand grip on RSS measurements for localization of mass market devices in indoor environments, demonstrating the strong influence they have in the evaluation of the RSS and distance estimations to be used in cooperative schemes. The experiment has been performed at Tampere University of Technology, Department of Computer Systems in an open area, with dimensions of
Theoretically, a free-space propagation model could be used for converting RSS into distance [
However, this model does not take into account any characteristics of the environment and it is not suitable at all for practical applications in real life. At this purpose the pathloss model adopted in this work has been obtained empirically (Figure
Figures
Measurement campaigns with mass market devices are always a tricky task since devices’ specifications are not always detailed and companies do not share technical secrets and performances capabilities. In addition to the traditional environmental factors to take into account [
The human body [
The hand-grip effect experiment has been performed using the simple grips shown in Figures
Hand-grip positions.
Human body impact on positioning estimation [
Cooperative positioning with human body effect.
Empirical pathloss model.
Pathloss model definition.
Short-range versus long-range measurements.
Short-range versus long-range scenario.
Body-loss impact on RSS measurements.
Estimated distances with body-loss effect.
Hand-grip impact on RSS measurements.
Estimated distances with hand-grip effect.
Estimated positions without cooperation.
In this experiment we test the effect that the aforementioned human-induced impairments have on the cooperation and hence in the accuracy of the final position estimation. At this purpose, we have devised a squared testing environment using four APs and placing MSs in the center area (Table
Experimental setup.
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A set of 50 RSSs measurements are recorded from each AP by three MSs. Then the empirical path-loss model is applied and the estimated distances from the four APs are sent to the NLLS algorithm (Figure
Results obtained in Figure
Estimated positions with corrupted cooperation.
Estimated positions with augmented cooperation.
User back and front signal blockage.
RMSE of estimated positions.
Percentage gain.
In this paper we have first introduced the broad field of wireless positioning for mass market devices, trying to give an overview of the current advancements in literature, highlighting both their potentials and limitations. Reviewing the conventional positioning techniques it has been asserted that they are not able to keep their promise in term of accuracy and feasibility. In fact, indoor environments constitute a highly challenging scenario because of their intrinsic complexity and unpredictability. Even though some attempts have been done to overcome the aforementioned limitations by proposing coarse cooperative techniques on top of conventional algorithms, it has been found that a simplistic modeling of the wireless scenario is not able to capture the intrinsic variability of indoor environments. In fact the presence of the user’s body is an inescapable part in the broader picture of indoor channel modeling. In the past, the user-induced anomalies in the wireless signals have been neglected not only within literature, but also by the very mobile devices manufacturers. At this purpose, in this contribution we have highlighted first and foremost the effect of the user’s body on the far-field part of the wireless link, mainly identifying it as a time-varying blocking object. Moreover, the effects of the near-field perturbations caused by the close by environment have been presented. In particular, the influence of the hand-grip on mass market devices has been shown to have a growing importance in mobile positioning algorithms. In this paper, many experimental results have been presented to assess the potential of user’s body cognitivity on the cooperative positioning performances. Surprisingly, it has been found that cooperation is not improving significantly the accuracy of the estimated positions of the users with respect to the noncooperative case. In fact, as stated before, the presence of the user should be correctly accounted for in the data-fusion algorithm. This means that if the hand grip and the blocking is somehow accessible to the algorithm we could see a real boost in the cooperative approach. For all these reasons, the encouraging measurement results suggest that future work will highly benefit from a proper knowledge of what surrounds the mobile devices. Additionally, we would like to point out that these benefits could be achieved in a straightforward manner by using off-the-shelf mobile devices that already embed all the needed sensors to unleash cooperative-based positioning services.
The research leading to these results has received funding from the TISE Doctoral Programme, Nokia Foundation, Ulla Tuomisen säätiö, Tekniikan edistämissäätiön (TES), and Tuula ja Yrjö Neuvo Fund.