Data Logger of Temperature, Humidity, Barometric Pressure with LoRa Module 915 MHz Using Two Array Rectangular Microstrip Antenna on Elevation

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.

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.

Due to the limited resources and scalability, the security protocols for the Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for the multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of the secured data transfers. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is $O(1)$ . The energy dissipation is calculated for the IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52–53 mJ for each IoT node and 313.626 mJ for the entire network. The RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance, and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.

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.

In recent years, the Internet of Things concept has gained an immense momentum in today's technological world. Internet of Things connects devices efficiently which improves quality of life from various aspects. One of the most heavily used device for Internet of Things application is ESP8266 WiFi serial transceiver module. It is operating with Received Signal Strength Indicator feature. Characteristic analysis of the Received Signal Strength Indicator readings collected using ESP8266 WiFi serial transceiver module is done in this paper. The aim is to explore the future possibilities of Received Signal Strength Indicator value as a stand-alone and unique parameter to be used in various applications especially in the domain of Internet of Things. In addition, the potential of the cheap yet sophisticated ESP8266 WiFi serial transceiver module is also highlighted. The findings have shown an insight into the characteristics of Received Signal Strength Indicator readings and how it can be utilized for other different purposes. The findings have brought up a few stimulating issues that may arise from some implementation of Received Signal Strength Indicator readings. However, if solutions to the issues are to be introduced, it will thrust the Internet of Things' technological advancements even further.

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.

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).

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

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.

Microstrip antennas or patch antennas are popular for their attractive features such as low profile, low weight, low cost, ease of fabrication and integration with RF devices. Micro strip antennas have been found favorable because they are inexpensive to manufacture and compatible with monolithic microwave integrated circuit designs (MMIC). They are usually employed at UHF and higher frequencies because the size of the antenna is directly tied to the wavelength at the resonance frequency. A Microstrip or patch antenna is a narrowband, wide-beam antenna fabricated by etching the antenna element pattern in metal trace bonded to an insulating dielectric substrate with a continuous metal layer bonded to the opposite side of the substrate which forms a ground plane. The most commonly employed microstrip antenna is a rectangular patch. The major disadvantages of Microstrip antennas are lower gain and very narrow bandwidth. Microstrip patch antennas have some drawbacks of low efficiency, narrow bandwidth (3-6%) of the central frequency. Millimeter wave technology being an emerging area is still much undeveloped. As micro strip antennas have found wide variety of application areas, a number of techniques are evolved to improve its limited bandwidth. A good approach to improve the bandwidth is increasing the thickness of substrate supporting the micro strip patch. However problems exist on the ability to effectively feed the patch on a thick substrate and the radiation efficiency can degrade with increasing substrate thickness. A substantial research needs to be done in this area as its applications are numerous. The radiation patterns and S11 performance are used for the analysis of the different configurations. In the present endeavor a rectangular patch antenna is designed on thick substrate and simulated using MATLAB software and configuration on different dielectric susbstrates was used .

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.

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.

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.