Experimental analysis of RSSI for distance and position estimation

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

Wireless sensor networks have presented multiple opportunities, but also several challenges to researchers. Tools are needed to overcome these challenges. Received signal strength indication (RSSI) is one possible tool since it could theoretically be used to determine the distance between two communicating nodes. However in reality, in addition to distance, RSSI is affected by quite a few other factors, making distance estimates extremely unreliable. We carried out three different studies with MicaZ smart dust motes to examine in realistic operating conditions the effects of distance, transmission strength, antenna characteristics, environmental and radio interferences on the RSSI value. The studies show that while many of the effects are easily observable, it is difficult to recognize the causes solely based on the RSSI value. The high level of indeterminism in the environment causes RSSI to have little practical value in realistic deployments. Ill. 6, bibl. 6 (in English; summaries in English, Russian and Lithuanian).

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

The investigation of the received signal strength indicator (RSSI) and throughput performance in indoor environments for a 2.4 GHz transparent antenna is presented in this paper. The monopole transparent antenna with bi-directional radiation pattern is used in the measurement as the receiver. The RSSI results are taken from a laptop that equipped with AirMagnet wireless PC Card. The antenna throughput is measured with different RSSI levels of the highest, medium and lowest values. Moreover, the measured performance of direct electrical and solar power sources in an indoor environment is compared. The aim is to test the reliability of WiFi systems with solar panel. The measurement results show that throughput performance is proportional to the RSSI values. The RSSI values are between -60 dBm to -90 dBm with average throughput values between 8 and 7 Mbps. Ultimately, it is discovered that the same performance was obtained by using either one of the two power sources. Therefore solar source can be applied to power up the WiFi transparent antenna in real environment that eventually moving towards the green energy and sustainable technology.

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.

In low complexity networks such as wireless sensor networks (WSNs), localization of nodes becomes essential for many applications. In order to obtain location estimates with minimal cost, the received signal strength indicator (RSSI) based localization has been a preferred approach. For accurate location estimates via RSSI, the knowledge of the network path-loss model is of high importance. Accurate modeling of the channel, prior to system deployment is a laborious task, hence there is a recent trend to estimate the power-distance gradient or pathloss exponent (PLE) alongside the location coordinates. In this study, we put to test our previously proposed linear least squares (LLS) based algorithm using NXP's IEEE 802.15.4 compliant JN5148 nodes. Experiments are carried out in outdoor and indoor environments. Our results indicate that the performance of joint estimation of the PLE and the location coordinates surpasses that of the results obtained by using the distance estimates as provided by the vendor for positioning. (5 pages)

Tracking a moving user in indoor environments using Bluetooth low energy beacons.

This study aims to determine the results of the analysis of Quality of Service (QoS) parameter testing using LoRa SX1262 telemetry with a certain distance on Smart PDAM and determine the effectiveness of using LoRa SX1262 telemetry on data exchange on multipoint communication networks. The data collection technique uses observation and documentation techniques to obtain valid test results data. This study uses an quantitative descriptive analytical approach with a standard assessment of the TIPHON QoS parameters on the throughput, delay, packet loss, and RSSI (Received Signal Strength Indicator) values. Based on the results of the analysis of this study, it was found that the data can be received well when the RSSI value is above -90 dBm, the average throughput and packet loss values resulting from data transmission on a digital water meter system is inversely proportional to the amount of bandwidth, the average delay of sending LoRa SX1262 telemetry data using a 115200 baud rate of 513 milliseconds. The conclusion of this study is that multipont communication on digital meter data transmission is effectively used at a maximum distance of 300 meters with a bandwidth of 500 kHz.

A new approach is developed to localise and navigate Wireless Sensor Network (WSN) mobile node with Received Signal Strength Indicator (RSSI) signal, the approach is economical, convenient and reliable in noisy environment. An efficient Metropolis–Hasting (MH) particle filter algorithm was proposed to process the signal for ensures the monotonic increasing relationship between RSSI value and the distance between nodes. The coordinate space quantised with RSSI value was selected to describe the state and position of robots to avoid model error. The navigation system consists of several beacon nodes, and each of them is a distributed measurement and control unites. The outputs from every beacon nodes are collected by the navigation control centre, and the final outputs for mobile robots are calculated based on data fusion. Therefore, the real–time performance of this navigation system is enhanced. Furthermore, this system could adapt dynamic or unknown scenarios due to the coordinate of beacon node is not required before navigation. The simulation and experimental results show the effectiveness of this navigation algorithm.

‘Wireless sensor networks’ (WSNs) follow layered architecture for the fruitful and reliable working of distributed WSNs. The region of WSN is attaining importance in the research field on account of its wide-ranging scope of applications in different domains. The CLD (cross-layer design) is a rising trend in the network layer architectures. And it normally involves significant connections between disparate layers. This paper manages CLD instead of single layer in order to attain good performance. Collision amid packet transmission is the chief issues that directly influence the performance and lifetime of distributed WSNs. This present model specifically provided a secure solution to avoid collision utilizing cross-layer in WSNs. For the foremost time, the transport layer was chosen as a primary platform for the calculation of RSSI (‘received signal strength indicator’) values to enrich cross-layer interaction. Effectively, this sort of selection in the order of nodes bypasses the collision in packet transmission and hence extends the network lifetime by avoiding transmission failure.

Expansion of wireless body area networks applications, such as health-care, m-banking, and others has lead to vulnerability of privacy and personal data. An effective and unobtrusive natural method of authentication is therefore a necessity in such applications. Accelerometer-based gait recognition has become an attractive solution, however, continuous sampling of accelerometer data reduces the battery life of wearables. This paper investigates the usage of received signal strength indicator (RSSI) as a source of gait recognition. Unlike the accelerometer-based method, the RSSI approach does not require additional sensors (hardware) or sampling of them, but uses the RSSI values already available in all radio devices. Three radio channel features namely, the time series, auto-correlation function, and level crossing rate were extracted from unique signature of the RSSI in relation to the corresponding subject. The extracted features were then used together with four different classification learners, namely decision tree, support vector machine, $k$ -nearest neighbors, and artificial neural network, to evaluate the method. The best performance was achieved utilizing artificial neural network with 95% accuracy when the features were extracted from one on-body radio channel (right wrist to waist), and 98% when the features were extracted from two on-body radio channels (right wrist to waist, and left wrist to waist). The developed RSSI-based gait authentication approach can complement high-level authentication methods for increased privacy and security, without additional hardware, or high energy consumption existing in accelerometer-based solutions.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

Aiming at the large positioning errors of traditional coal mine underground locomotive, an improved received signal strength indication (RSSI) positioning algorithm for coal mine underground locomotive was proposed. The RSSI value fluctuates heavily due to the poor environment of coal mine underground. The nodes with larger RSSI value corrected by Gaussian-weighted model were selected as beacon nodes. In order to reduce the positioning error further, the estimated positions of the locomotives were corrected by the weighted distance correction method. The difference between actual position and estimated position of beacon node was regarded as the positioning error and was given a corresponding weight. The results of simulation show that the positioning accuracy of Gaussian-weighted model is better than statistical average model and Gaussian model and it has a high positioning accuracy after correcting positioning error correction. In the 10 m of communication range, positioning error can be maintained at 0.5 m.