Wireless Received Signal Strength Research Articles

Predictive Wireless Received Signal Strength Using Friis Transmission Technique

A good WLAN performance is crucial in determining the quality of experience (QoE) among the campus community. Proper WLAN planning and design should be done beforehand to ensure good WLAN performance. Various studies have discussed different methods of conducting WLAN planning to predict WLAN's best performance, including using artificial intelligence and mathematical approaches. One of the processes involved in performing WLAN planning is measuring performance parameters. Signal strength is one of the vital parameters to be measured in determining the excellent performance of WLAN in a particular area. When deploying a WLAN design in two different environments, the signal strength outcomes can differ due to various factors, including obstacles and path loss propagation issues within the deployment area. Higher Learning Institutions (HLIs) present a unique challenge as their building designs vary to accommodate student needs. As a result, the selection of materials used will also be different, affecting the WLAN performance. A detailed study should investigate the effect of path loss propagation and the type of obstacle that affects WLAN performance in HLI. Thus, this study focuses on predicting received signal strength using Friis Transmission and studying the effect of path loss propagation on WLAN performance. The simulated model significantly affects signal strength when the signal passes through different types of building material (non-LOS) and line-of-sight (NLOS), where concrete walls substantially affect the received signal strength between transmitters. The proposed model can assist network planners in designing robust WLAN infrastructure by improving signal strength, particularly in the HLI WLAN environment.Â

Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization.

Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model's performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

Enhanced RSSI-Based Real-Time Position-Tracking System in Vehicular Networks

The next-generation vehicular tracking applications on the road bring many benefits, including aided travel time estimation, traffic trajectory observation, and quick congestion control. To meet such requirements, adopting the device-free positioning system with wireless received signal strength indicator (RSSI) samples is a cheap, reliable, simple, and robust solution because RSSI indicates the current condition of the signal by geographically overlaying across a region. Considering the usefulness of RSSI, this letter has investigated an enhanced vehicle-tracking scheme incorporating the environmental issues in the driving motions, where the moving vehicle on the road can be tracked by at most two successive base stations (BSs). In addition, due to the use of the minimum number of BSs, the associated data analysis platform with the vehicular network can quickly provide current information about the position of each vehicle.

An optimal location strategy for multiple drone base stations in massive MIMO

The concept of drone base stations (DBSs) has been applied to reduce the distance of the wireless link between a macro base station and its active users under diverse scenarios in military communications, smart industries, and high-density networks, and to provide service in topologies with damaged infrastructure. In this paper, we address the optimal positioning of multiple DBSs in a multiple-input multiple-output wireless network setting. We present a low-complexity machine learning-based algorithm to optimize the location of the DBSs by minimizing the collective wireless received signal strength experienced by the active terminals. The proposed algorithm reduces the propagation loss in the system and provides a lower bit error rate when compared with the Euclidean cost benchmark.

SRS: Spatial-tagged radio-mapping system combining LiDAR and mobile-phone data for indoor location-based services

One of the most challenging issues in radio received signal strength (RSS)-based localization systems is the generation and distribution of a radio map with a coordinate system linked with spatial information in a large indoor space. This study proposes a novel spatial-tagged radio-mapping system (SRS) that effectively combines the heterogeneous properties of LiDAR and mobile phones to simultaneously perform both spatial and radio mappings. The SRS consists of synchronization, localization, and map building processes, and enables real-time spatial and radio mapping. In the synchronization process, the distance range, motion data, and radio signals obtained through the LiDAR and mobile phone are collected in nodal units according to the sensing time. In the localization process, a feature variance filter is used to control the number of features generated from LiDAR and estimate the positions at which the nodes are generated in real time according to the motion data and radio signals. In map building, the estimated positions of the nodes are used to extract spatial and radio maps by using a unified location coordinate system. To ensure mobility, the SRS is manufactured in the form of a backpack supporting LiDAR and a mobile phone; the usefulness of the system is experimentally verified. The experiments are performed in a large indoor shopping mall with a complex structure. The experimental results demonstrated that a common coordinate system could be used to build spatial and radio maps with high accuracy and efficiency in real time. In addition, the field applicability of the SRS to location-based services is experimentally verified by applying the constructed radio map to well-known fingerprinting algorithms using the heterogeneous mobile phones.

Autonomous UAV Trajectory for Localizing Ground Objects: A Reinforcement Learning Approach

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

Low-Cost Indoor Human Tracking by Utilizing Fluctuation of Received Radio Signal Strength

Existing indoor positioning technologies aim at providing location information of extremely high accuracy, but are limited by the requirement for infrastructure with high installation and maintenance costs. Thus, for indoor human tracking applications, a suitable indoor positioning technique should be selected by taking into account practical considerations of adequate accuracy as well as cost-effectiveness. Based on this observation, an indoor, room-level tracking method is presented, which is characterized by an infrastructure that is cost-effective to build and maintain with no need to deal with calibration issues of the fingerprint map caused by hardware heterogeneity. A mobile device tracks variations in the radio signal strength from nearby Bluetooth beacons to determine the entry and exit of the user’s device from a given room or hallway. Therefore, by determining the sequence of users’ movements, their moving path can be recorded for indoor tracking or navigation. Experiments revealed that the proposed method attains an average hit rate, false alarm rate, and miss rate of 94.2%, 3.6%, and 5.8%, respectively.

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Wi-Fi-based indoor positioning for determining accurate wireless indoor location information has become crucial in meeting increasing demands for location-based services by leveraging the Internet of Things (IoT) and ubiquitous connectivity. Most Wi-Fi-based indoor positioning techniques using wireless received signal strength (RSS)-based methods are affected by the indoor environment and depend on the respective signals from at least three reference access points. In this paper, we propose a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation through a Wi-Fi located on a one-hop wireless network. Our cloudlet-based cloud computing system provides the reference point data and real-time interactive response for a self-driving indoor cart. The system was tested in a real environment with the following results: (1) our system autonomously performed actions, such as turning right or left or going straight according to a movement decision algorithm and determined the position within a stable range of Wi-Fi coverage; (2) the cloudlet and core cloud can track navigation for an indoor self-driving cart; (3) the global and local positions designed for reference access points and a specific position can navigate the self-driving cart to a particular position accurately; (4) the moving edge clouds play a role in deciding three action movements (go straight, turn left, and turn right), as well as managing the local position of the items; and (5) a core cloud is deployed to store all information for the items, such as their positions and corresponding Wi-Fi locations. A core cloud manages items that have the same position (i.e., a global position) defined as the corresponding Wi-Fi location. Finally, the practical results have significance in designing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation.

Tracking System and Its Application in Unmanned Automobile Navigation Based on Sparse Photoelectric Sensor Network

Sensor tracking technology has broad prospects of application in the fields of smart home and environmental protection. The passive motion tracking method of sensor networks can realize the perception of location, temperature and other information without carrying sensor nodes. A sparse network tracking system based on infrared sensor nodes is proposed in this study, which can control the running automobiles with unmanned navigation. On the basis of the theory of diffraction, the way of spreading for wireless received signal strength (RSS) can be divided into "scattered waves" and "diffracted waves," which can be regarded as two components of infrared sensing wireless signals so as to further propose the RSS indicators of "long-term testing value" and "short-term test value." Based on these indicators, a measurement model based on diffraction effects and scattering effects is proposed, and an improved particle filter algorithm is used to update the motion tracking. The hardware design of each module in an unmanned vehicle includes the main controller, tracking circuit, serial port circuit, motor control circuit and infrared sensor control circuit of the car. In the experiment, the measurement accuracy of the tracking system based on the sparse infrared photoelectric sensor was first tested. In the simulation experiment, the long-term test value, the short-term test value and the actual measurement value were compared respectively. The test results show that the theoretical RSS value and the actual test result can be matched. Moreover, the infrared photoelectric tracking system is used to design the navigation control system of unmanned cars, helping the car to drive automatically through obstacle avoidance test and tracking obstacle avoidance test.

Improved Distance Metrics for Histogram-Based Device-Free Localization

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Device-Free Localization Systems Utilizing Wireless RSSI: A Comparative Practical Investigation

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric to localize targets with no device attached to them have been reported in the literature for almost a decade. Approaches using RSSI can be split into three main categories. Link-based approaches utilize weighted summation or probabilistic methods to infer location. Location-based approaches create a fingerprint map of an area. Radio Tomographic Imaging treat DFL as an imaging problem solved with a linear inverse. In this paper, we implement and investigate the performance of all three major RSSI approaches in two test environments. We demonstrate how different environments and walking trajectories can have significant effects on the localization accuracy. The experimental results lead us to the conclusion that without implementing and testing within the same environment for the same target trajectories, the performance of various classes of DFL systems cannot be reliably evaluated. Relying on the stated accuracy from the literature for comparison is a flawed premise.

A Novel Vehicle Detection Method Based on the Fusion of Radio Received Signal Strength and Geomagnetism.

A geomagnetic signal blind zone exists between the front and rear axle of high-chassis vehicle such as trucks and buses, which leads to multiple-detection problem in detecting those vehicles running at low speed on roads or error-detection problem in the case of the stopping position of the vehicle is not standard when waiting for the traffic light to change. In order to improve the detection accuracy of any type of vehicle running at any speed, a novel two-sensors data fusion vehicle detection method through combining received signal strength from radio stations with geomagnetism around vehicles is designed and verified in the paper. Experimental results show that the accuracy of our proposed method can reach 95.4% and traditional single magnetism-based detection method was only 83.4% in the detection of high-chassis vehicles.

Smartphone-based Acoustic Indoor Space Mapping

Constructing a map of indoor space has many important applications, such as indoor navigation, VR/AR, construction, safety, facility management, and network condition prediction. Existing indoor space mapping requires special hardware (e.g., indoor LiDAR equipment) and well-trained operators. In this paper, we develop a smartphone-based indoor space mapping system that lets a regular user quickly map an indoor space by simply walking around while holding a phone in his/her hand. Our system accurately measures the distance to nearby reflectors, estimates the user's trajectory, and pairs different reflectors the user encounters during the walk to automatically construct the contour. Using extensive evaluation, we show our contour construction is accurate: the median errors are 1.5 cm for a single wall and 6 cm for multiple walls (due to longer trajectory and the higher number of walls). We show that our system provides a median error of 30 cm and a 90-percentile error of 1 m, which is significantly better than the state-of-the-art smartphone acoustic mapping system BatMapper [64], whose corresponding errors are 60 cm and 2.5 m respectively, even after multiple walks. We further show that the constructed indoor contour can be used to predict wireless received signal strength (RSS).

Multi-Sensor Multi-Floor 3D Localization With Robust Floor Detection

Location has become an essential part of the next-generation Internet of Things systems. This paper proposes a multi-sensor-based 3D indoor localization approach. Compared with the existing 3D localization methods, this paper presents a wireless received signal strength (RSS)-profile-based floor-detection approach to enhance RSS-based floor detection. The profile-based floor detection is further integrated with the barometer data to gain more reliable estimations of the height and the barometer bias. Furthermore, the data from inertial sensors, magnetometers, and a barometer are integrated with the RSS data through an extend Kalman filter. The proposed multi-sensor integration algorithm provided more robust and smoother floor detection and 3D localization solutions than the existing methods.

Exploiting Wireless Received Signal Strength Indicators to Detect Evil-Twin Attacks in Smart Homes

Evil-Twin is becoming a common attack in smart home environments where an attacker can set up a fake AP to compromise the security of the connected devices. To identify the fake APs, The current approaches of detecting Evil-Twin attacks all rely on information such as SSIDs, the MAC address of the genuine AP, or network traffic patterns. However, such information can be faked by the attacker, often leading to low detection rates and weak protection. This paper presents a novel Evil-Twin attack detection method based on the received signal strength indicator (RSSI). Our approach considers the RSSI as a fingerprint of APs and uses the fingerprint of the genuine AP to identify fake ones. We provide two schemes to detect a fake AP in two different scenarios where the genuine AP can be located at either a single or multiple locations in the property, by exploiting the multipath effect of the Wi-Fi signal. As a departure from prior work, our approach does not rely on any professional measurement devices. Experimental results show that our approach can successfully detect 90% of the fake APs, at the cost of a one-off, modest connection delay.

Location Data Analytics in Wireless Lighting Systems

Location data analytics refers to the statistical analysis of data pertaining to user location. Such analytics offers useful insights into the interactions among occupants and with their environment. Based on the insights, building facility management may be improved. We consider location data analytics using wireless sensors, which are part of a wireless lighting system, to collect radio received signal strength indicator data resulting from device transmissions from user mobile devices. Zone positioning of users is first performed. Zone position data are then used for analytics to obtain: 1) occupancy density; 2) environment characterization; 3) crowd detection; and 4) occupant connectivity. The approach is evaluated in a simulated open-plan office lighting environment considering simple occupant motion profiles and wireless device transmission models.

A multi-sensor RSS spatial sensing-based robust stochastic optimization algorithm for enhanced wireless tethering.

The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.

Adaptive Autocorrelation Approach for Fingerprint-based Distance Dependent Positioning Algorithms in WLAN Indoor Areas

This paper addresses the adaptive autocorrelation approach for the fingerprinting-based distance dependent positioning algorithms (DDPAs) in wireless local area network (WLAN) indoor environment. As far as we know, although the DDPAs, like nearest neighbor (NN), K nearest neighbors (KNN) and weighted KNN (WKNN) algorithms, have been widely utilized for the indoor and outdoor location based services (LBS), the guarantee of location accuracy and precision has always been one of the significant compelling problems. Therefore, in response to this challenging task, the expected errors and associated confidence probabilities are mathematically deduced by the assumptions of logarithmic attenuation model and Gaussian distributions of received radio signal strength (RSS) at the receiver. However, because of the non line of sight (NLOS) property, time-varying interference and multipath effect, the measured radio strength varies a lot in the real-world indoor environment. Therefore, in order to fill this gap, a novel adaptive autocorrelation preprocessing approach is utilized to eliminate the singular strength from the original prestored radio map and improve the matching accuracy of DDPAs. Finally, compared with traditional DDPAs without adaptive autocorrelation preprocessing, the feasibility and effectiveness of the adaptive autocorrelation-based DDPAs are verified by approximately decreasing the average errors from 1.13m to 0.75m.

Mobile Positioning System using Signal Strength Measurement for WCDMA System

The precise position of the mobile station is critical for the ever increasing number of applications based on location . In this paper a new approach for mobile positioning in WCDMA (Wideband Code Division Multiple Access ) cellular network is proposed . Radio Received Signal Strength Indication (RSSI) can be measured from a number of nearby base stations . Three proposed approaches (3-BS's , 5-BS's and 7-BS's) are simulated using three propagation models and the rmserror is calculated for each model. As a simulation study case, the University of Mosul area is choosed to find the position of a mobile user in the university campus .

Localization in ambiguous environments using multiple weak cues

This paper presents a probabilistic approach for sensor-based localization with weak sensor data. Wireless received signal strength measurements are used to disambiguate sonar measurements in symmetric environments. Particle filters are used to model the multi-hypothesis estimation problem. Experiments indicate that multiple weak cues can provide robust position estimates and that multiple sensors also aid in solving the kidnapped robot problem.