Real-time Received Signal Strength Indicator Research Articles

High‐gain horizontally polarised omnidirectional planar antenna using parasitic directors for wireless sensor networks

SummaryWireless sensor networks (WSNs) are used to monitor the real‐time parameters of remote/restricted access areas of automation industries which are tightly sealed. In such scenarios, the main challenge is signal transmission in sealed environments. An omnidirectional radiation pattern is preferred as a control link for receiving packets or periodic updates from its neighbouring nodes. However, the gain and directivity of omnidirectional antenna should be sufficient enough to transfer the data to the collecting node. This paper proposes a high‐gain horizontally polarised omnidirectional radiation pattern planar antenna resonating at the 2.45 GHz. The developed microstrip planar antenna offers a very simple and compact structure printed on either side of FR4 epoxy substrate (ε = 4.4) suitable for WSN nodes. A printed dipole director is arranged orthogonally and offers horizontal polarisation. A pair of parasitic elements is placed parallel to dipole radiators to improve the total gain of the antenna. A parallel pair of parasitic elements is placed on an inclination of 45° in antenna design plane for uniform distribution of surface current to achieve the omnidirectional antenna. The symmetrical structure of the radiating element on either side of the substrate avoids the design of the traditional balun structure to reduce the complexity of feeding network. The developed antenna is simulated, fabricated, measured and tested using WSN. The measured radiation (directivity = 3.9 dBi) is maximum at Ⴔ = 0° and Ⴔ = 90°. The frequency response of the antenna indicates a −10 dB fractional bandwidth of 380 MHz (2.24 GHz–2.62 GHz). The measured far‐field response shows a stable average gain of 3.9 dBi over the ISM band. The real‐time received signal strength indicator (RSSI) of the antenna installed in the sensor node observed a success rate of 79.46% with a transmission error of 10%. The observed response of an antenna makes it well‐suitable for WSN in industrial automation.

Boat Detection and Tracking Using RSSI Signal in A Device-Free Environment

Abstract: This project presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So, it raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receiver sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method. Keywords: RSSI Signal, Detection and tracking

Human Detection through RSSI Processing with Packet Dropout in Wireless Sensor Network

This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.

Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment.

Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods.

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

The reliability of location information still maintains a crucial impact on restricting the development of location based services in indoor environment. However, in wireless local area network, received signal strength indicator (RSSI) is prone to be interfered by indoor complex environment, resulting in low accuracy and instability of real-time positioning. Here, the new self-adaptive dynamic ranging model-based real-time hybrid algorithm was proposed to realize accurate and undisturbed localization in indoor scenes. A self-adaptive dynamic ranging model was initially constructed to update the environmental parameters and correct the ranging values of mobile terminals in real time. Based on this model, a hybrid KNN algorithm and a hybrid Bayesian algorithm were severally presented. Location fingerprint database and real-time RSSI data of test points were then obtained through data acquisition. Finally, the acquired data was further used to verify the two hybrid algorithms proposed, and compared with the results of several conventional algorithms. As a result, the stability and accuracy of dual hybrid algorithms were better than those of the traditional ones. The range of average location error of both hybrid algorithms maintained 1.26-1.38 m, which was significantly lower than the error level of 2-5 m under the current WLAN environment. This newly proposed hybrid algorithm could effectively improve the stability and accuracy of indoor localization with real-time positioning algorithm, providing a promising solution for RSSI-based indoor positioning system.

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.

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.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

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.

Extended Gradient RSSI Predictor and Filter for Signal Prediction and Filtering in Communication Holes

In wireless communication, received signal strength indicator (RSSI) is a measurement of power in the received signal. This parameter is mainly used in radio frequency (RF) based distance estimation, position estimation, and handover mechanisms. In real-time applications such as object tracking, a handover mechanism, distance estimations require continuous reception of RSSI measurements in order to accurately estimate the position of the object. However, there are some constraints such as signal attenuation, signal loss, multi-path effects. These parameters cause variations in RSSI, and hence it becomes weak, which produces communication holes. Communication holes refer to the phenomenon when the signal drops to zero, and there is a communication blockage, which is practically negligible and can be considered as disconnection. This communication hole occurs due to various factors such as multi-path effects, noise, moving out of the coverage zone, device manufacturing faults, and inquiry procedure as well. The main focus of this paper is to address the issue of communication holes in Bluetooth networks. In Bluetooth networks communication holes also occur due to the delay in the inquiry procedure. Therefore, an existing gradient filter is modified based on the most recent measurements received. The proposed extended gradient filter is tested using simulated and real time RSSI measurements and compared with Kalman Filter and Kalman Smoother. The results show better performance in case of communication holes occurrence.

A Novel Algorithm to Improve Indoor Localization Accuracy and Path-Loss Reduction Using Real Time RSSI

Distance estimation is vital for localization and many other applications in wireless sensor networks. Research has revealed that the correlation between distance and RSSI (Received Signal Strength Indication) values is the key of ranging and localization technologies in wireless sensor networks (WSNs). Distance measurement based on RSSI (Received Signal Strength Indication) is a low cost and low complexity of the distance measurement technique, and it is widely applied in the range-based localization of the WSN. In this paper, an Log Normal Shadowing Model (LNSM) that estimates the distance between sensor nodes in WSNs is presented. The performance of this model and proposed method are evaluated and analyzed in an indoor environment with and without considering obstacles by performing an empirical measurement using Crossbow TelosB wireless sensor motes. Our result shows that the proposed method reduced distance error and path-loss effectively than LNSM. The results of these evaluations would contribute towards obtaining accurate locations of wireless sensor nodes.

Performance Measurement and Analysis of WiMAX-LAN Communication Operating at 5.8 GHz

Deployment of a hybrid wireless and local area network (LAN) system is a feasible solution for smart grid communication. In this paper, a combination of fixed worldwide interoperability for microwave access (WiMAX) at 5.8 GHz and Ethernet LAN is proposed for monitoring and control of energy meters in a smart grid. Communication signal quality is an important factor to consider as it directly affects the reliability of the whole monitoring and control system of a smart grid. In this paper, the signal quality in terms of received signal strength indicator (RSSI) of the WiMAX system is investigated with a laboratory-scale smart grid prototype. Real-time RSSI is recorded and analyzed to develop an analytical model for the WiMAX RSSI transmitting at 5.8 GHz. It was found that in order to achieve reliable communication for a coverage area of 2 km, the base station has to transmit at a total power of 28.08 dBm for a receiver height of 1.875 m. Similarly, a receiver height of 2.766 m is required for a transmitted power of 25 dBm. The performance of the proposed WiMAX-LAN communication system for advanced metering infrastructure in smart grids is analyzed. On average, the proposed system completes the data collection of 3705 meter units in less than 997 s.