Use of a Simplified Maximum Likelihood Function in a WLAN-Based Location Estimation

This paper describes feasibility of RSSI (Received Signal Strength Indicator) based access network detection for multi-band WLAN (Wireless LAN) using 2.4/5GHz and 60GHz aiming at power-saving of multi-band WLAN device and higher spectrum efficiency. The proposed scheme detects 60GHz coverage by using 2.4/5GHz WLAN signals without using 60GHz WLAN signals for on/off control of 60GHz part of multi-band WLAN device. This makes it possible to turn off 60GHz part whenever a multi-band WLAN device is out of 60GHz coverage. Ray-tracing simulation results indicate that the proposed RSSI based access network detection is feasible.

D-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation and experiment that it is able to decide the propagation parameter of an extent that doesnpsilat influence the location estimation accuracy without investigating the propagation environment of the sensor field.

The Global Positioning System (GPS) has eventually become a common positioning technology. While GPS enabled many applications, satellite signals have yet to overcome many obstacles to enable indoor positioning. Meanwhile, due to the wide deployment of Wireless Local Area Networks (WLAN) in recent years, WLAN positioning algorithms have become popular for mobile device positioning in indoor environments. The most accurate WLAN positioning algorithms exploit the so-called fingerprinting concept which consists of two stages. In the offline stage (training), the Received Signal Strength Indicator (RSSI) from a set of available Access Points (AP) is measured for a number of reference locations and stored in a database. Due to the availability of many APs and the complex structure of indoor environments, this information is distinctive for each reference location and thus is called a position fingerprint. In the online stage (testing), a mobile device receives RSSIs from the APs and their fingerprint is compared to the fingerprints that are stored in the database for a best possible match. WLAN fingerprinting can provide high accuracy in indoor environments when there are many APs which ensure fingerprint uniqueness. This paper provides a novel approach to use WLAN multipath signals as possible fingerprints for positioning algorithms for applications with lower number of access points. The paper provides an initial study.

Position location estimation in sensor networks is a valuable supplement since it supports the deployment of location-based services. Sensor networks have changing conditions in the environment due to propagation issues, noise and placement of sensors, which represent challenges that position location algorithms must deal with. Accuracy of the location estimation technique is relevant since it allows minimizing positioning error. In indoor environments, propagation issues such as multipath signals, affect adversely the precision of the positioning algorithm. Also, the use of parameters such as time of arrival has a trade-off between the small distances that the signals traverse and the precision of the hardware used to capture such measurements. In this paper, we use received signal strength indicator (RSSI) to estimate the coordinates of individual sensors in an area of study. The RSSI parameter is measured and processed by a set of reference nodes installed in the area. We show that performance of the location estimation algorithm needs additional techniques to obtain improved accuracy rate. We develop additional techniques based on the use of polynomial interpolation and spline functions to balance propagation issues. These techniques help us to implement correcting factors that are used in the propagation model to compensate the RSSI measurements. We use these techniques to show how the positioning error is reduced in the area of study with simulations and measurements using sensors.

This paper presents a BLUE-based (Best Linear Unbiased Estimator) real-time localization estimation approach using indoor Received Signal Strength Indicator (RSSI) measurements, its Android based mobile application and real experimental results. Location estimation and monitoring have recently become very popular research topics using WiFi networks for indoor environments. Differing from its counterparts in use, this presented approach does not require any calibration or hardware integration, discarding additional system overload and cost as an important contribution. Compared to its classical counterpart Least Square method, it achieves up to two times better location estimation especially for measurement points with none or very small signal received from some of the access points.

In wireless sensor networks, location estimation of sensor nodes is important. Therefore some location estimation methods have been proposed. Especially, in wireless sensor networks based on IEEE802.15.4, a method using received signal strength indicator (RSSI) which can be measured by each sensor node is one of the most practicable methods in the way that new hardware is not needed and it is suitable also for the miniaturization. However this technique has a weak point that it needs to investigate the propagation environment of the sensor field to estimate the location of sensor nodes in advance. This paper proposes the method that the decision of the parameter of the propagation environment using only measurements obtained during localization without investigating prior. Evaluation of this method is shown by simulation that it is able to decide the propagation parameter of an extent that doesn't influence the location estimation accuracy without investigating the propagation environment of the sensor field.

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.

Chaotic deployment of Wireless Local Area Networks (WLANs) in dense urban areas is one of the common issues of many Internet Service Providers (ISPs) and Wi-Fi users. It results in a substantial reduction of the throughput and impedes the balanced distribution of bandwidth among the users. Most of these networks are managed independently and there is no cooperation among them. Moreover, the conventional association mechanism that selects the Access Points (APs) with the strongest Received Signal Strength Indicator (RSSI) aggravates this situation. In this paper, we present a versatile near-optimal solution for the fair bandwidth distribution over virtualized WLANs through dynamic association control. The proposed scheme is called ACO-PF, which is developed on top of Ant Colony Optimization (ACO) as a meta-heuristic technique to provide Proportional Fairness (PF) among the greedy clients. In fact, it presents a generic and centralized solution for ISPs that are using a common, virtualized or overlapped WLAN infrastructure for serving their customers. We have evaluated the efficacy of ACO-PF through numerical analysis versus popular existing schemes for both downlink and uplink scenarios. Our proposed technique has less complexity in terms of the implementation and running time for largescale WLANs and it can be easily developed and customized for different objective functions. In addition, it is implemented in a testbed environment to investigate the key challenges of real deployment scenarios.

This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of the measurements in this study were performed using a test bench operating under the IEEE 802.11ax standard in the 2.4 GHz band. RSSI is widely used due to its simplicity and availability; however, its accuracy is limited by signal attenuation, electromagnetic interference, and environmental variability. To mitigate these limitations, the present work proposes the implementation of advanced techniques, including weighted averages and positioning algorithms such as Min–Max, Maximum Likelihood, and trilateration, aiming to achieve an accuracy of 2 m in controlled conditions. The design also included a specialized test bench to calculate the coordinates and estimate the location of unknown nodes using anchor node positioning. This approach combines the simplicity of RSSI with optimized algorithms, providing a robust and practical solution for indoor localization. The results validate the system’s effectiveness and highlight its potential for future applications in real-world environments, opening new possibilities for optimizing wireless sensor networks and addressing the current challenges in localization systems.

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.

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

Localization is the crucial problem for mobile robot navigation. For indoor mobile robot, since a global positioning system (GPS) is incapable, another promising technique to detect the position is the received signal strength indicator (RSSI) from wireless communication. To improve the precision and robustness of mobile unit localization, an inertial measurement unit (IMU) is normally used. In this report, we propose the algorithm for mobile robot localization based on sensor fusion between RSSI from wireless local area network (WLAN) and an IMU. The proposed fusion scheme is based on the extended Kalman filter (EKF). The experiment is conducted by using mobile unit equipped with low-cost IMU and a wireless communication module together with access points to evaluate the performance of our algorithm, and the result is promising.

In wireless sensor networks (WSN) location awareness is required to use the communication advantages of adaptive antenna arrays (AAA) and use energy efficiency location based routing protocols. In this work, we introduce a simple and practical location estimation technique which uses AAAs at the central node in WSNs. This localization technique can be used at the setup phase in the location based routing protocol. This technique is based on scanning the desired region in azimuth and radial directions by changing parameters of downlink beam. This sweeping process can activate the nodes in the desired region which is specified by antenna beam width and beam direction of the localization beam and also by minimum and maximum thresholds (Rmin and Rmax) for the received signal strength indicator (RSSI). Active nodes in the desired region transmit their IDs and RSSI levels via multi hop communication to the central node. Unlike GPS-based or beacon based localization techniques, the proposed technique does not require any modification in the sensor nodes. We demonstrate different scenarios for performance analysis of this localization technique. The results show that by carefully adjusting these parameters, desired performance can be achieved. The accuracy of location estimation depends on beam direction, beam width and transmitting power for downlink beams.

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