Real-time Received Signal Strength Research Articles

Easy Peasy: A New Handy Method for Pairing Multiple COTS IoT Devices

Context-based IoT device paring is a popular solution for devices that lack an interface. However, it takes a proximate distance or a long time for IoT devices to sense highly correlated context with enough entropy. In this work, we present a novel approach for fast and secure multiple commercial off-theshelf (COTS) devices pairing in IoT scenarios. Our approach is based on the key idea that devices co-located within a physicallysecure boundary can perceive qualified context under the help of human-in-the-loop (HITL). Specifically, we leverage receivedsignal- strength (RSS) trajectory data with manually-generated interferences in a certain period as the shared secret to achieve fast and secure device pairing. Moreover, the real-time RSS trajectory data can be utilized to generate random numbers in lieu of pre-shared key (PSK), which makes our scheme more resistant to background attacks. We theoretically prove the security of our pairing scheme and implement it in some real-world environments. Our experimental results demonstrate that our scheme can effectively defend against malicious devices by imposing a threshold on the similarity of RSS trajectory data. The experimental results also show that, compared with the traditional context-based pairing that takes up to 24 hours, the legitimate device in our scheme takes only 10 seconds to pass the similarity check on average, which is efficient and feasible

Complexity reduction of ordinary kriging algorithm for 3D REM design

This work has investigated the feasibility of using cluster ordinary kriging (OK) interpolation to speed up indoor three-dimensional (3D) radio environment map (REM) generation time than the conventional OK-based REM generation time in two-dimensional (2D) and 3D ways mainly. The REM has been generated on the real-time received signal strength (RSS) dataset of an ultra-high frequency active television (TV) channel to explore TV grey space (TVGS) volume for cognitive access inside a six-storied office building. It can support future home networks for high-speed data-based applications. The optimal number of clusters has been found at K=4 on both 3D and 2D ways through distortion % deviation analysis. The maximum distortion % deviation = 88.74%, has been found at K=4. The exponential semivariogram model has been selected as the best-fit model after a thorough cross-validated comparative analysis of the clustered and non-clustered OK algorithms separately on residual squared error (RSE), root mean square error (RMSE) and the ratio of expected variance (EV) and kriging variance (KV) results. The RSE and RMSE are minimum, and the EV: KV ratio is in between 0.95 to 1.05 for the exponential model. Significant observation of the analysis of the results shows that cluster OK algorithms outperform conventional OK algorithms of corresponding domains on time complexity. A comparative study on RMSE, correlation coefficient (CC), and relative recovery error (RRE) show that cluster OK improves the accuracy. Cluster 2D and 3D OK-based detected TVGS volume is 13450 m3, 5.634% of the whole building volume.

Underground Coal Mine Fingerprint Positioning Based on the MA-VAP Method.

The access points (APs) in a coal mine wireless local area network (WLAN) are generally sparsely distributed. It can, with difficulty, satisfy the basic requirements of the fingerprint positioning based on Wi-Fi. Currently, the effectiveness of positioning is ensured by deploying more APs in an underground tunnel, which significantly increases system cost. This problem can be solved by using the Virtual Access Point (VAP) method that introduces virtual access points (VAPs), which can be virtually arranged in any part of the positioning area without installing actual access points. The drawback of the VAP method is that the generated received signal strength (RSS) value of a VAP is calculated based on the mapping of RSS value from only one corresponding access point (AP). This drawback does not consider the correlation between different AP signals and the generated RSS value of a VAP, which makes the modeling of fingerprint samples and real-time RSS collection incomplete. This study proposed a Multi-Association Virtual Access Point (MA-VAP) method takes into account the influence of multi-association. The multi-association coefficient is calculated based on the correlation between the RSS values of a VAP and multiple access points (APs). Then, the RSS value generated by a VAP is calculated using the multi-association function. The real-time collected RSS values from multiple APs related to this VAP are the input of the multi-association function. The influence of the number of VAPs and their arrangement on positioning accuracy is also analyzed. The experimental positioning results show that the proposed MA-VAP method achieves better positioning performance than the VAP method for the same VAP arrangement. Combined with the Weight K-Nearest Neighbors (WKNN) algorithm and Kernel Principal Component Analysis (KPCA) algorithm, the positioning error of the MA-VAP method of the error distance cumulative distribution function (CDF) at 90% is 4.5 m (with WKNN) and 3.5 m (with KPCA) in the environment with non-line-of-sight (NLOS) interference, and the positioning accuracy is improved by 10% (with WKNN) and 22.2% (with KPCA) compared with the VAP method. The MA-VAP method not only effectively solves the fingerprint positioning problem when APs are sparse deployed, but also improves the positioning accuracy.

Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons

It has traditionally been laborious and problematic to construct and maintain fingerprint maps for indoor positioning. In this paper, a multi-phase fingerprint map indoor localization method based on interpolation was proposed. The method addresses two needs: (1) to construct an efficient fingerprint map, and (2) to provide high-accuracy indoor positioning. For the effective collection of the real-time received signal strength indicator (RSSI), we proposed a mobile data-collection cart, equipped with a laser rangefinder and a smartphone, which can rapidly collect environmental data in indoor spaces. In practice, as some regions might be inaccessible, we adopted a kriging-based interpolation method that exploits the spatial autocorrelation of the RSSI to efficiently generate and update the fingerprint database. To overcome the instability of RSSI and improve positioning accuracy, we proposed a time-variant multi-phase fingerprint map, with specific fingerprint databases constructed for different time periods, thereby automatically employing the most appropriate fingerprint map according to the time period. Indoor experiments verified that the proposed method has practical value, with sufficient accuracy for general indoor applications. We believe that the proposed method provides a feasible and low-cost indoor positioning solution.

A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots.

Location-aware services are one of the key elements of modern intelligent applications. Numerous real-world applications such as factory automation, indoor delivery, and even search and rescue scenarios require autonomous robots to have the ability to navigate in an unknown environment and reach mobile targets with minimal or no prior infrastructure deployment. This research investigates and proposes a novel approach of dynamic target localisation using a single RF emitter, which will be used as the basis of allowing autonomous robots to navigate towards and reach a target. Through the use of multiple directional antennae, Received Signal Strength (RSS) is compared to determine the most probable direction of the targeted emitter, which is combined with the distance estimates to improve the localisation performance. The accuracy of the position estimate is further improved using a particle filter to mitigate the fluctuating nature of real-time RSS data. Based on the direction information, a motion control algorithm is proposed, using Simultaneous Localisation and Mapping (SLAM) and A* path planning to enable navigation through unknown complex environments. A number of navigation scenarios were developed in the context of factory automation applications to demonstrate and evaluate the functionality and performance of the proposed system.

Multipath Robust Azimuthal Direction of Arrival Estimation in Dual-Band 2.45–5.2 GHz Networks

This paper presents a technique capable to ameliorating the impairment caused by radio channel multipath, in the estimation of the wireless communications direction of arrival (DoA). This feature is enabled by a space division multiple access approach supported by a DoA technique based on the combination of the estimates achieved at both 2.45 and 5.2 GHz. The DoA estimation relies on a comparison between the real-time received signal strength indicator and the predicted signal level distribution, which can be estimated and stored without the need of lengthy offline measurement. The experimental setup adopted for the technique validation involves an active interferer, by which arbitrary multipath levels can be implemented. An equivalent reflection coefficient signal-to-interference ratio is defined as the ratio of the unperturbed signal and the induced coherent isofrequency interferer signal, compatible with the two-ray model of the multipath impairment. The proposed technique exploits a dual-band frequency planning, and because of the combined use of the data at the two frequencies, a mean error of 2.6° is observed within a domain ranging from 0.5 to 4 m, for the entire 360° angle in the reference anechoic scenario. The 90th percentile of the cumulative error distribution is 5.4°. In the presence of a strong coherent interferer, compatible with a total reflection from the walls, the mean error is 4.9°, while the 90th percentile of the cumulative error distribution is 10.5°. In the presence of the strongest coherent interferer signal available, which is more severe than the worst case of total reflection, the mean error is 7.1°, while the 90th percentile of the cumulative error distribution is 15.1°.

Node-Position-Based Joint Relay Selection and Adaptive Power Control Scheme in Wireless Body Area Networks

Sport and healthcare monitoring have become more and more popular in our everyday life, benefiting from the deploying of Wireless Body Area Networks. In these cases, data transmission reliability should be studied to receive the accurate real-time feedbacks, which contain rich biomedical information, such as heartbeat, and breathing process. However, wireless on-body channels experience significant temporal variation due to body movements and single-hop communication scheme cannot adapt to highly dynamic network topology. In this paper, a joint relay selection and power control scheme (JRP) is proposed to improve transmission reliability. We take running activity for experimental study, the influence of nodes’ positions to radio performance is evaluated. By analyzing the source and destination, JRP can decide the way to finish this communication by either relay selection or power control. In addition, the acceleration of each node is measured by accelerometers. Thus, with the help of real-time received signal strength indication and acceleration, JRP can pick several potential relay nodes for the source node’s transmission request. Finally, the experimental result shows that our scheme can achieve a good tradeoff between reliability and energy consumption.

Fine‐grained azimuthal direction of arrival estimation using received signal strengths

We propose a direction of arrival (DoA) estimation technique, which is enabled by a single printed patch antenna capable of four reconfigurable beams at the operative frequency of 2.45 GHz. The direction of the messages sent by a nomadic wireless node is estimated and stored without the need of lengthy offline measurements, by correlating the real-time received signal strength actual data and the expected antenna beams. The comparison is implemented with a least square technique with an extremely reduced computational cost. Experimental validations demonstrate the DoA estimation capability, with a mean error <2.3° with a variance of 1.9° within a domain of 90°.

Aspect Ratio of Receiver Node Geometry based Indoor WLAN Propagation Model

This paper presents validation of indoor wireless local area network (WLAN) propagation model for varying rectangular receiver node geometry. The rectangular client node configuration is a standard node arrangement in computer laboratories of academic institutes and research organizations. The model assists to install network nodes for the better signal coverage. The proposed model is backed by wide ranging real time received signal strength measurements at 2.4 GHz. The shadow fading component of signal propagation under realistic indoor environment is modelled with the dependency on varying aspect ratio of the client node geometry. The developed new model is useful in predicting indoor path loss for IEEE 802.11b/g WLAN. The new model provides better performance in comparison to well known International Telecommunication Union and free space propagation models. It is shown that the proposed model is simple and can be a useful tool for indoor WLAN node deployment planning and quick method for the best utilisation of the office space.

Reference Device-Assisted Adaptive Location Fingerprinting.

Location fingerprinting suffers in dynamic environments and needs recalibration from time to time to maintain system performance. This paper proposes an adaptive approach for location fingerprinting. Based on real-time received signal strength indicator (RSSI) samples measured by a group of reference devices, the approach applies a modified Universal Kriging (UK) interpolant to estimate adaptive temporal and environmental radio maps. The modified UK can take the spatial distribution characteristics of RSSI into account. In addition, the issue of device heterogeneity caused by multiple reference devices is further addressed. To compensate the measuring differences of heterogeneous reference devices, differential RSSI metric is employed. Extensive experiments were conducted in an indoor field and the results demonstrate that the proposed approach not only adapts to dynamic environments and the situation of changing APs’ positions, but it is also robust toward measuring differences of heterogeneous reference devices.

A Distributed Positioning System Based on a Predictive Fingerprinting Method Enabling Sub-Metric Precision in IEEE 802.11 Networks

In this paper, we present a distributed positioning system for indoor environment based on a mesh of compact independent anchor nodes. Each node is built of low-cost off-the-shelf components and operates as specialized access points capable of standard connectivity at 2.45 GHz. The key technology for the localization strategy is the switched beam antenna (SBA), which enables a space division multiple access (SDMA) paradigm. The coordinated operation of SBA-equipped anchor nodes constitutes a legacy unmodified IEEE 802.11 network which can exploit the multiplexing mechanism. The latter is the driving force of the estimation strategy, with the positional information obtained as the result of a maximum likelihood algorithm driven by the comparison of a real-time received signal strength indicator (RSSI) with the predicted signal level distribution, which can be estimated and stored without the need of lengthy offline measurement. The signal level prediction is based on a simple propagation model which is effective because benefits of both the elementary antenna radiation beams directivity and the circular polarization operation, two strong aids for the mitigation of the multipath impairment. In turn, these feature make the estimation procedure tolerant to noisy power measurements, hence particularly suitable for cost-effective solutions based on RSSI. Experimental validations demonstrate the performance of a network composed of four anchors arranged in a 2.6 $\,\times\, $ 3.8 m $^{2}$ mesh in a 6 $\,\times\, $ 7 m $^{2}$ office room, and dealing with a single target node. The mean error inside the mesh area is 63 cm while the mean error in the entire room is 1.1 m. Focusing on the cumulative distribution of the error, the 90th percentile value is 1 m considering only the mesh and 1.9 m for the entire room.

Pedestrian navigation fusing inertial and RSS/TOF measurements with adaptive movement/measurement models: Experimental evaluation and theoretical limits

A common approach for advanced Indoor Localization systems is the fusion of complementary techniques, such as inertial navigation systems like pedestrian dead-reckoning (PDR) and absolute measurement methods like radio-frequency (RF) beacon-based positioning. Although this fusion approach provides accurate drift-free absolute positioning, the best results are only obtained if the techniques are adapted to each environment and user. This requires a previous campaign of building calibration and movement model estimation that will be specific to the place and person. In this paper, we tackle this problem by presenting a real-time pedestrian navigation system that fuses PDR and RF beacon-based strategies using a flexible particle filter (PF) implementation, with the following innovative aspects: (1) the definition of an adaptive stride movement model valid for different walking styles, which is used in the PF prediction stage; (2) the dynamic estimation of the measurements model from real-time Received Signal Strength (RSS) and Time of Flight (TOF) values, which is used in the PF update stage; (3) the tracking of the person's position without an initial position/heading nor specific calibration. Additionally, we have obtained the Cramér-Rao lower bound (CRLB) for our fusion-based approach, in order to assess rigorously the performance of the positioning accuracy. We have tested the system in a building fusing PDR with TOF and RSS values coming from WiFi access points or ZigBee nodes. For trajectories with a total length of approximately 1000m we obtained an error of less than 1.75m for 90% of the total path length, with both systems. The empirical results match closely the CRLB, showing that our system performs close to the theoretical limit.