RSSI-based Localization Research Articles

ORB Visual and WiFi Online RSSI fusion SLAM

Simultaneous Localization and Mapping (SLAM) technologies are indispensable for indoor service robots, enabling them to navigate through and interact with environments. Visual SLAM systems often encounter significant challenges such as dynamic obstacles, variable lighting, feature scarcity, and perceptual aliasing in real-world scenarios. By merging the precise environmental mapping capabilities of visual SLAM with the ubiquity and stability of WiFi signals, our method effectively addresses the limitations typically associated with visual SLAM. Notably, our fusion technique leverages existing WiFi infrastructure, thus providing a cost-effective improvement in spatial awareness without the extensive offline database requirements of WiFi RSSI-based localization. Comparative performance evaluations highlight that our graph optimization-based approach not only surpasses the original ORBSLAM3 method but also significantly outperforms the Extended Kalman Filter (EKF) in terms of accuracy, particularly in environments characterized by poor lighting, feature-less scenes, and significant occlusions. This is evidenced by a reduced Root Mean Square Error (RMSE) in localization: 3.09m for our method versus 4.02m for EKF. This enhancement in precision underscores the potential of our integrated system to advance indoor navigation technologies, making it a crucial development in the field of robotics and automated systems.

RSSI-based localization enhancement by exploiting interference signals

RSSI-based localization enhancement by exploiting interference signals

A cost-effective LoRaWAN-based IoT localization method using fixed reference nodes and dual-slope path-loss modeling

Recently, RSSI-based localization has gained popularity for outdoor localization within the IoT ecosystem due to its cost-effectiveness, low-power and low-cost deployment, and ability to operate loT nodes for years on a single battery. However, this approach typically sacrifices accuracy, resulting in location estimates in the tens or hundreds of meters. For example, LoRaWAN communications have been widely used in IoT applications, being an attractive solution due to their long-range coverage, low implementation cost, and higher autonomy. However, RSSI-based LoRaWAN location methods suffer from multipath and fading interference which results in the near-far problem, impacting ranging accuracy for short and long distances, and thus leading to an overall decrease in the localization accuracy. In this article, we address the low accuracy of RSSI based localization challenge for outdoor IoT node localization by computing the Path Loss (PL) parameters of LoRaWAN, for short and long distances separately, thus providing a dual-slope PL model. In addition, some IoT nodes in specific locations are adopted as Reference Nodes (RNs), whose main task is to estimate the interference effect on the transmitted signals dynamically. The proposed model has been evaluated using a publicly available LoRaWAN dataset collected in urban areas in the city of Antwerp, Belgium, which serves as a benchmark for the evaluation of the results. Its effectiveness is assessed by simulation and comparison to the state-of-the-art. In addition, results are compared with the derived Cramer–Rao Lower Bound (CRLB). The localization error achieves a median error of 117 m and a mean error of 236 m.

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively.

Efficient Localization Method Based on RSSI for AP Clusters.

The localization accuracy is susceptible to the received signal strength indication (RSSI) fluctuations for RSSI-based wireless localization methods. Moreover, the maximum likelihood estimation (MLE) of the target location is nonconvex, and locating target presents a significant computational complexity. In this paper, an RSSI-based access point cluster localization (APCL) method is proposed for locating a moving target. Multiple location-constrained access points (APs) are used in the APCL method to form an AP cluster as an anchor node (AN) in the wireless sensor network (WSN), and the RSSI of the target is estimated with several RSSI samples obtained by the AN. With the estimated RSSI for each AN, the solution for the target location can be obtained quickly and accurately due to the fact that the MLE localization problem is transformed into an eigenvalue problem by constructing an eigenvalue equation. Simulation and experimental results show that the APCL method can meet the requirement of high-precision real-time localization of moving targets in WSN with higher localization accuracy and lower computational effort compared to the existing classical RSSI-based localization methods.

Federated Kalman Filter for Secure IoT-Based Device Monitoring Services

Device monitoring services have increased in popularity with the evolution of recent technology and the continuously increased number of Internet of Things (IoT) devices. Among the popular services are the ones that use device location information. However, these services run into privacy issues due to the nature of data collection and transmission. In this work, we introduce a platform incorporating Federated Kalman Filter (FKF) with a federated learning approach and private blockchain technology for privacy preservation. We analyze the accuracy of the proposed design against a standard Kalman Filter (KF) implementation of localization based on the Received Signal Strength Indicator (RSSI). The experimental results reveal significant potential for improved data estimation for RSSI-based localization in device monitoring.

Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

RF Source Localization Using Multiple UAVs through a Novel Geometrical RSSI Approach

In this paper, a novel geometrical localization scheme based on the Received Signal Strength Indicator (RSSI) is developed for a group of unmanned aerial vehicles (UAVs). Since RSSI-based localization does not require complicated hardware, it is the correct choice for RF target localization. In this promising work, unlike the other techniques given in the literature, transmit power or path loss exponent information is not needed. The procedure depends on the received power difference of each receiver in UAVs. In the developed scheme, four UAVs forming two groups fly in perpendicular planes. Each UAV in the group moves in a circle, keeping its distance from the plane’s center until it gets equal power with the other members of its group. Using this movement rate, lines passing through the source position are calculated. The intersection of these lines gives the position of the RF target. However, in a noisy environment, the lines do not intersect at one point. Therefore, the algorithm given in the manuscript finds a point that has a minimum distance to all lines and is also developed. Simulation results are provided at the end of the manuscript to verify our theoretical claims.

3-D Indoor Localization and Identification Through RSSI-Based Angle of Arrival Estimation With Real Wi-Fi Signals

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.

Improvement of triangle centroid localization algorithm based on PIT criterion (ITCL-PIT) for WSNs

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error.

A Simplified and High Accuracy Algorithm of RSSI-Based Localization Zoning for Children Tracking In-Out the School Buses Using Bluetooth Low Energy Beacon

To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver.

An Adaptive Energy Saving Algorithm for an RSSI-Based Localization System in Mobile Radio Sensors.

In localization systems based on the emission of reference radio signals, an important issue related to the reliability of sensor operation is the problem of operating time and power of the emitted reference radio signal. There are many localization methods that have proven useful in practice and that use a reference radio signal for this purpose. In the issue of determining the location of radio emitters, various radio signal propagation models are used to determine the effective range and distance of the sensor-receiver from the radio emitter. This paper presents an adaptive power control algorithm for a transmitter, as a reference emitter, operating in power-saving mode. An important advantage of the presented solution is the adjustment of the localization system accuracy at the assumed level of energy radiated by radio emitters based on the RSSI signal received power estimation.

Accurate Guidance Method and App Development for Assigning Parking Spaces Based on Indoor Wi-Fi

Existing parking guidance systems only provide road guidance outside the parking lot but do not provide accurate guidance to specific parking spaces inside the parking lot. By using a Kalman filter, the Grubbs test, and a neural network algorithm to improve the RSSI-based location fingerprint identification technology, an accurate location method based on indoor Wi-Fi is obtained, which implements precise route guidance and a reverse search function for parking spaces. We utilize Beidou positioning to develop a Gaode map for outdoor navigation and use an integrated system of ultrasonic detector/indicators and ground locks to manage parking spaces. Through the secondary development of an Android system and the application of a MySql database, an app for precise parking guidance was developed. The system makes full use of the Internet and parking information, eliminates information asymmetry, improves the utilization ratio of the urban static traffic resources, allocates parking spaces in real-time, breaks information islands, provides parking search and recommendation functions for users, achieves parking information-sharing, and effectively improves parking efficiency and the parking utilization ratio.

Practical approximate indoor nearest neighbour locating with crowdsourced RSSIs

In the indoor space, finding the nearest neighbour is of great importance in location-based services. Received Signal Strength Indication (RSSI) has received much attention due to its simplicity and compatibility with existing hardware, which has been widely used for indoor localization. Existing indoor nearest neighbour search methods are based on the real walking distance, which need ground survey and much labor work to measure many real distances. Crowdsourcing is a low-cost and efficient way to collect the RSSI of indoor space without expert surveyors and designated coordinates for RSSI collection points. The crowdsourced RSSIs can reflect the location of indoor objects and RSSI-based localization method is the simplistic method as it needs low hardware requirements, low deployment cost and no survey indoor distance. So we study how to search the nearest neighbour of indoor objects with crowdsourced RSSIs. To address this problem, we propose a graph with interval weights, called I-graph, which can connect the RSSIs and represent the topology of indoor space. We also construct a search tree index D-tree, which can index the graph with interval weights and search the nearest neighbour objects efficiently. We also propose a novel distance metric for RSSI and study the relationship between the RSSI distance and the indoor distance. To locate nearest neighbour of indoor objects with crowdsourced RSSIs, we devise efficient search algorithms and pruning strategies for computing the nearest neighbour query. We demonstrate the efficiency and effectiveness of the proposed solution through extensive experiments with two real data sets.

Renewable Energy Sources Efficient Detection in Triangulation for Wireless Sensor Networks

It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

RSSI-Based Localization Schemes for Wireless Sensor Networks Using Outlier Detection

The received signal strength indicator (RSSI) of RF signals is a cost-effective solution for distance estimation, which makes it a practical choice for localization schemes in wireless sensor networks (WSN). However, RF propagation channels in most WSN deployment environments, including dense cities and natural habitats, are commonly affected by shadowing due to obstructions caused by natural and man-made obstacles. RF signal attenuation from shadowing introduces uncharacteristically high errors in RSSI-based distance estimates, which result in large errors in RSSI-based localization schemes. This paper proposes the use of outlier detection methods for removing the effect of such disproportionately erroneous distance estimates in location estimation using RSSI. Three different localization schemes are proposed that apply outlier detection to effectively reduce localization errors in shadowed environments. Performance results of the proposed schemes are obtained using computer simulations and experimental tests.

Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

<p class="0abstract">In this report, we perform the digital filter computation using Matlab for Wi-Fi tracking application. This work motivates to improve the accuracy of filter algorithm in the RSSI-based distance localization system. There are several aspects that we can improve, e.g., in the Filter part and Path-loss model. But, in this work, we focus on filter part; Unscented Kalman Filter (UKF) is implemented to replace linear Kalman Filter (KF), which is used in previous work. Based on the performance comparison, UKF has 90% hit ratio while linear KF has only 81.15 % hit ratio. We found that UKF can handle the noise in RSSI. Further work, the UKF algorithm is then embedded on the server system.</p>

An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System

A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment.

A Novel Trilateration Algorithm for RSSI-Based Indoor Localization

This paper proposed a novel trilateration algorithm for indoor localization based on received signal strength indication (RSSI). Firstly, all the raw measurement data are preprocessed by a Gaussian filter to reducing the influence of measurement noise. Secondly, the transmit power and the path loss exponent are estimated by a novel least-squares curve fitting (LSCF) method in the RSSI-based localization. Thirdly, a novel trilateration algorithm is proposed based on the extreme value theory, which constructs a nonlinear error function depending on distances and anchor nodes position. To minimize the function, a Taylor series approximation can be used for reduce the computational complexity. And, an iteration condition is designed to further improve the positioning accuracy. Afterward, Bayesian filtering is used to smoothing the localization error, and decrease the influence of the process noise. Both the simulation and experimental results demonstrate the effectiveness of the proposed methodology.