Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

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.

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.

In wireless sensor network (WSN), position awareness (localization) of the sensor nodes is necessary to exploit the communication and to provide the meaningful information about their surroundings. Here Received Signal Strength Indicator (RSSI) technique is used as it requires no extra hardware and can be used for both indoor and outdoor environment. RSSI is having limitations such as high randomness due to fading and shadowing. This makes difficulties in establishing exact relationship between RSSI value and distance. In this paper we propose the method of creating database by using Split Range Technique (SRT) to estimate the approximate distance between two nodes. Using this database, position estimation has been done. For this Adaptive n-Triangle algorithm is implemented. Present results of RSSI shows the approximate positioning of the sensor node with the error estimation of about 5–10%. The experiment can be extended to show the predicted path of a moving object which is being identified by sensors.

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.

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.

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

WSN (wireless sensor networks) is a kind of energy-constrained network, which has been widely used in precision agriculture environment monitoring. However, power balance is not taken into account in traditional routing algorithms. In this paper, a novel Power Balance Ad hoc On-Demand Distance Vector (PB-AODV) routing algorithm is proposed on cross-layer design to solve the problem of power balance. The main idea of our proposed algorithm is that, routing path is established by the received signal strength indication (RSSI) value in the route discovery process of PB-AODV. The optimal transmitting power which can be computed by RSSI value, power threshold and node’s surplus energy is encapsulated into route reply packet (RRP). Thus, the sender node can adaptively adjust its transmission power to save energy with the RRP. Simulation results show that the proposed algorithm is effective for load balancing, and increases the WSN’s lifetime about 14.3%.

Results of a study of the influence of manufacturing environment factors on wireless personal networks are presented in the paper. A classification of such factors is proposed, and the noise immunity of wireless personal networks is determined using a real production as an example. A method for evaluating noise immunity based on a received signal strength indicator (RSSI) is proposed. RSSI values can be obtained natively from almost any receiver and transmitter, which makes this method affordable compared to using network analyzers and other specialized equipment. In the conducted experiment the receiver and transmitter were located at a distance ranging from 0.5 to 25 m from each other. The act of signal transfer was carried out alternately under the influence of each production environment factor. Then measured RSSI values were analyzed and converted into the maximum permissible distance between the receiver and the transmitter in accordance with the proposed method. As a result, data on the influence of a production environment on the noise immunity of wireless personal networks was obtained. Nevertheless, it is concluded that the influence is not significant enough to decide against the use of wireless personal networks, since the influence of many factors can be offset by the use of mesh topology and a dense arrangement of receivers and transmitters. The results are particularly interesting in the context of manufacturing digitalization, where the wireless method of data transmission from field level sensors becomes preferable to the wired due to the requirement for flexibility and mobility of a production process.

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.

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