Radio Signal Strength Indicator Research Articles

Intelligent Localization System for Rescue during the Accident in Construction Work

A significant development in assuring the security and well-being of laborers in the construction sector is the Intelligent Localization System for Construction Accident Rescue employing RSSI for Person Identification and Health Monitoring System. During emergencies or accidents, this system is intended to precisely find and identify individuals within a building site by utilizing the power of Radio Signal Strength Indicator (RSSI) technology. The system precisely locates each worker by evaluating the strength of the RSSI signal, facilitating quick and effective rescue efforts. Additionally, the system has health monitoring features that offer workers real-time insights into their well-being. This entails keeping an eye on critical indicators like body temperature and heart rate in order to recognize crisis signals early and seek medical attention quickly if necessary. The combination of individuals.

Sliding Window, Hierarchical Classification, Regression, and Genetic Algorithm for RFID Indoor Positioning Systems

Several surveys in the Indoor Positioning System (IPS) use the Radio Signal Strength Indicator (RSSI) to locate target objects. However, RSSI suffers significantly from the multipath phenomenon and other environmental factors such as the absence of the field of view, reflective materials, excessive obstacles, and a high density of objects. To overcome such problems, we propose three new IPS approaches using Radio Frequency Identification (RFID) technology and the collaborative use of Sliding Window, Hierarchical Classification, Random Forest Regressor, and Genetic Algorithm. We tested the new approaches on three datasets obtained in different environments and compared them with two state-of-the-art IPS approaches (Landmarc and SVR-Landmarc). One of the main results got was a precision of 4.82 cm in an environment with 7,000 target tags, reducing by up to 92.91% the average error w.r.t. other IPS approaches presented in the literature, including more complex indoor scenarios.

Public Health and Safety on Close Contact Proximity Detection for COVID-19 and Alert via IoT

The social distancing among people is vital in minimizing spread of COVID-19 among community and can be effective in flattening the outbreak. This research work on developing a close contact proximity detection system among smartphone users and particularly of COVID-19 patient using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone user in his/her surrounding and to alert user if the social distancing is breached. The methodology used the Radio Signal Strength Indicator (RSSI) signal to analyze and estimate the proximity distance and duration of the individual’s exposure to other peoples in surrounding area. The overlap zone of 1-meter is used to indicate detection of closed contact proximity between users. Furthermore, the collected data can be used to do contact tracing that enable health official to identify the closed contact of infected patient systematically, faster and can ensure coverage of people that anonymously and not directly known to the COVID-19 patient. An encouraging result is obtained on the closed contact proximity detection which shown within the mobile apps. Furthermore, the performance of system for close contact proximity detection shown that indoor location has a good signal distribution compared to outdoor location.

Sensor fusion for indoor positioning system through improved RSSI and PDR methods

Given the growth of the Internet of Things (IoT) and smart home appliances, the concept of the Indoor Positioning System or IPS has considerably risen. The major application of IPS is in locating people in roofed places. In this type of positioning, accuracy has always been the most important challenge. The main focus of this article is to improve the indoor positioning of people. First, two positioning approaches are proposed, Radio Signal Strength Indicator (RSSI) and Pedestrian Dead Reckoning (PDR). In the RSSI positioning part, the distance between the participating nodes is calculated based on the low-fluctuating values of RSSI. A novel filter called Weight-Based Optimization (WBO) is developed to optimize these raw RSSI values. Moreover, the path loss model parameters, which are location-dependent, are calculated by employing Particle Swarm Optimization (PSO) to convert the resulting RSSI values to distances. In the PDR positioning part, the accelerometer sensor data is used to detect the person’s steps, and the compass sensor data is used to detect the heading direction. Finally, the results of RSSI and PDR methods are combined using a sensor fusion approach. The positioning accuracy resulting from the proposed IPS approach is 68 cm, which is far better than the existing methods.

Spatial Compression of Sensing Information for Exploiting the Vacant Frequency Resource Using Radio Sensors

Recently, broadband wireless communication has been significantly enhanced; thus, frequency spectrum scarcity has become an extremely serious problem. Spatial frequency reuse based on the spectrum database is attracting significant attention. The spectrum database collects the wireless environment information, such as the radio signal strength indicator (RSSI), estimates the propagation coefficient for the propagation loss and shadow effect, and finds a vacant area where the secondary system uses the frequency spectrum without harmful interference to the primary system. To collect RSSI from the radio environ-mental monitor, which is a radio sensor, wireless sensor networks are required. However, a large number of RSSIs should be gathered because numerous sensors are spread over the wireless environment. In this paper, a data compression technique based on spatial features, such as buildings and houses, is proposed. Using a computer simulation and an experimental evaluation, we confirm that the proposed compression successfully reduces the size of the RSSI and restores the original RSSI in the recovery process.

LoRa-Based Traffic Flow Detection for Smart-Road.

This paper presents a wireless traffic flow detection system, mainly focused on conditions in which the traffic flow is slow or stopped, which increases the risk of highway accidents. To achieve this goal, a Low Power Wide Area Network (LPWAN) based on LoRa called Short LoRa has been developed. This LoRa sub-network complies with the European Telecommunications Standards Institute (ETSI) harmonized standard for its compatibility in Europe countries. In addition, the development of the devices has allowed them to also work on a LoRaWAN network. The introduced development has been compared to a reference system mounted with laser barriers that provided a high accurate comparison. Field tests of the system have been carried out and the data obtained in the measurement has been analyzed with two different methods, and both of them were valid for the application. The results can determine vehicle speed with adequate precision at low speeds. The attenuating behavior of the communication signal is also analyzed through the Radio Signal Strength Indicator (RSSI). The relationship between vehicle speed, gate distances and RSSI attenuation has been studied. The system is proven to have efficient results in detecting traffic flow under the conditions for which it has been developed.

Surface Electric Field Variations Due to Different Human Postures for Wireless Endoscopy Applications

Radio signal strength indicator (RSSI)-based localization methods for wireless capsule endoscopy (WCE) application require a patient to wear on-body sensors detecting the RF signals from the capsule. The main advantage of WCE is to allow the patient to live normally and to move freely during the natural transit time, which could take up to 73 hours. Changes in posture can be accompanied by the movement of soft tissues in the abdominal region. This will affect the propagation path length between on-body receiving sensors and the ingested wireless capsule, and consequently the received signal strength (RSS). This article explores potential variations in RSSI measurements created by postural change. Retroreflective markers placed in direct contact with the skin of a participant were used to represent appropriate locations of the on-body slot antennas, which could be used for WCE localization. Optical motion capture technology was used to determine an estimate of the location of the markers and corresponding skin position for different postures. The relative displacement of the markers from their initial location (the standing posture) was a maximum of 24 mm for the lying posture (supine position) for the participant with a BMI of 29 kg/m2. The maximum variation of the surface electric field of 13.77 dB was calculated using the attenuation path loss (PL) model for the human abdominal region propagation environment at 2.45 GHz. If not anticipated, posture-induced variations in RSSI can produce misleading interpretations and will require adjustments of WCE localization algorithms.

When RSSI encounters deep learning: An area localization scheme for pervasive sensing systems

Localization has long been considered as a crucial research problem for pervasive sensing systems, especially with the arrival of big data era. Various techniques have been proposed to improve the localization accuracy by leveraging common wireless signals, such as radio signal strength indication (RSSI), collected from sensors placed in pervasive environments. However, the measured signal value can be easily affected by noise caused by physical obstacles in such sensing environment, which in turn compromises the localization performance. Hence, we present a novel RSSI-based area localization scheme using deep neural network (DNN) to explore the underlying correlation between the RSSI data and the respective sensor placement to achieve a superior localization performance. Moreover, to cope with the sensor data loss issue that commonly occurs during wireless sensor network (WSN) operation, an algorithm is designed to reconstruct the missing data for respective sensors in order to preserve the performance of DNN localization model. The effectiveness of the proposed scheme is verified with a real-world WSN testbed deployed inside an office building. The results demonstrate that the proposed scheme provides satisfactory prediction accuracy in area localization for pervasive sensing systems, regardless of the data loss issue that occurs with the respective sensors.

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

Secure trajectory planning against undetectable spoofing attacks

This paper studies, for the first time, the trajectory planning problem in adversarial environments, where the objective is to design the trajectory of a robot to reach a desired final state despite the unknown and arbitrary action of an attacker. In particular, we consider a robot moving in a two-dimensional space and equipped with two sensors, namely, a Global Navigation Satellite System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor. The attacker can arbitrarily spoof the readings of the GNSS sensor and the robot control input so as to maximally deviate its trajectory from the nominal precomputed path. We derive explicit and constructive conditions for the existence of undetectable attacks, through which the attacker deviates the robot trajectory in a stealthy way. Conversely, we characterize the existence of secure trajectories, which guarantee that the robot either moves along the nominal trajectory or that attacks remain detectable. We show that secure trajectories can only exist between a subset of states, and provide a numerical technique to compute them. We illustrate our findings through several numerical studies, and show that our methods are applicable to different models of robot dynamics, including unicycles. More generally, our results show how control design affects security in systems with nonlinear dynamics.

L ocation Identification Methods f or IOT Devices Using Bluetooth Low Energy (BLE)

Indoor localization is a positioning method that used for closed environment condition. Since Internet of Things or IOT devices come with small size microcontroller units, positioning algorithms are studied and three of them are tested based on three parameters which are accuracy, algorithms response time and programming bit size. In this project, three Radio Signal Strength Indicator or RSSI based positioning algorithms are used to get the results which are trilateration, fingerprinting and proximity. Based on the data collected, proximity algorithm gives the highest accuracy, lowest response time, and lowest programming bit size. This project can be improved by sending the location of IOT devices to cloud so that it can be track by other IOT devices.

Secure Navigation of Robots in Adversarial Environments

We study the problem of navigating a robot in an adversarial environment, where the objective is to perform localization and trajectory planning despite the malicious and unknown action of an attacker. We consider robots with single integrator dynamics, equipped with a Global Navigation Satellite System (GNSS) sensor and a Radio Signal Strength Indicator (RSSI) sensor that provides relative positioning information with respect to a group of radio stations, each with limited communication range. The attacker can simultaneously spoof the sensor readings and send falsified control inputs to the robot, so as to deviate its trajectory from the nominal path. We demonstrate the existence of attacks that escape detectability, and illustrate a method for their systematic design. Conversely, we show that appropriate control design and waypoints selection allow the trajectory planner to ensure attack detectability or secure navigation. More generally, our results show that trajectory planning in nominal and adversarial settings are substantially different, and that careful trajectory design is required to ensure resilience to attacks.

BVIRE improved algorithm for indoor localization based on RFID and a linear regression model

Traditional indoor location technologies such as infrared technology and ultrasonic technology are complex, expensive, or having unsatisfactory location accuracy. Radio frequency identification (RFID) technologies are very popular in many areas since their costs are very low. The tag in such technologies acts as the transmitter, and the radio signal strength indicator (RSSI) information is measured at the reader. However, RSSI information suffers strictly from the multipath circumstance and circumferential elements. Therefore, the localization accuracy of the boundary will be affected severely. In order to solve this problem, we introduce the boundary virtual reference label (BVIRE) algorithm to well utilize RFID techniques for locating the tracking object, which inserts some virtual reference tags on the boundary by establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The positioning accuracy of the boundary tags and stability have been improved significantly, at least 78%, without adding extra reference tags or radio frequency interference. Also, the estimation errors of our improved BVIRE are much smaller compared to the virtual reference label, location identification based on the dynamic active RFID calibration (LANDMARC), ultrawide band, RADAR, and PinPoint algorithms.

Residential wearable RSSI and accelerometer measurements with detailed location annotations

This repository offers smart-home wearable accelerometer and Radio Signal Strength Indicator (RSSI) data acquired : 1) with low-cost hardware; 2) with high-resolution location annotations; 3) from four UK homes. The data are intended to evaluate RSSI-based indoor localisation methods with activity measurements provided from a user-worn wearable device. A wrist worn accelerometer records activity signatures which are relayed to a number of receiving Access Points (AP) placed throughout the building. Upon reception of a packet, each AP measures the RSSI of the received radio signal and timestamps the accelerometer measurements. Location labels are recorded automatically using a small camera which registers fiducial floor tags as the participant carries out their normal routines in a natural way. Approximately 14 h of annotated wearable measurements are provided. A scripted fingerprint measurement is provided along with several unscripted natural living recordings, where the participant carried out a number of daily household activities which are annotated, where possible, throughout. Codes are provided to access the data and to replicate the ground-truthing procedure.

Narrowband Characterization of Near-Ground Radio Channel for Wireless Sensors Networks at 5G-IoT Bands.

In this contribution, a narrowband radio channel model is proposed for rural scenarios in which the radio link operates under near-ground conditions for application in wireless sensor networks dedicated to smart agriculture. The received power attenuation was measured for both transmitter and receiver antennas placed at two different heights above ground: 0.2 and 0.4 m. Three frequency ranges, proposed for future 5G-IoT use case in agriculture, were chosen: 868 MHz, 2.4 GHz and 5.8 GHz. Three ground coverings were tested in a rural scenario: soil, short and tall grass fields. The path loss was then estimated as dependent of the radio link range and a three-slope log-normal path loss model was tailored. Results are explained in terms of the first Fresnel zone obstruction. Commercial Zigbee sensor nodes operating at 2.4 GHz were used in a second experiment to estimate the link quality from the experimental Radio Signal Strength Indicator (RSSI) received values. Two sensor nodes were placed at the same elevation above ground as in the previous experiment, only for short grass field case. The Quality of Service performance was determined in terms of theoretical bit error rate achieved for different digital modulations—BPSK, 8PSK and 16QAM—concluding remarkable results for an obstructed radio link.

Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery.

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage.

Testing the feasibility of wireless sensor networks and the use of radio signal strength indicator to track the movements of wild animals

Context Wireless sensor networks (WSNs) are revolutionising areas of animal behaviour research and are advantageous based on their ability to be deployed remotely and unobtrusively, for long time periods in inaccessible areas. Aims We aimed to determine the feasibility of using a WSN to track detailed movement paths of small animals, e.g. rats (Rattus spp.) 100–400g, too small for current GPS technology, by calibrating active Radio Frequency Identification (RFID) tags and loggers using Radio Frequency Signal Strength Indicator (RSSI) as a proxy for distance. Active RFIDs are also called Wireless Identification (WID) tags. Methods Calibration tests were conducted using a grid of loggers (n=16) spaced at 45-m intervals in clear line-of-sight conditions. WID tags (n=16) were placed between the loggers at 45-m intervals. Eight ‘walks’ were also conducted through the grid using a single WID tag. This involved attaching the tag to a small bottle of water (to simulate the body of an animal), towed around the grid using a 1-m long tow line attached to a volunteer walker. The volunteer also held a GPS device that logged their track. Models were constructed to test the effects of distance, tag movement and individual differences in loggers and tags on the reliability of movement data. Key results Loggers were most successful at detecting tags at distances <50m. However, there was a significant difference in the detection probabilities of individual loggers and also the transmission performance of individual tags. Static tags were less likely to be detected than the mobile tag; and although RSSI was somewhat related to distance, the reliability of this parameter was highly variable. Implications We recommend caution in the future use of current radio frequency ID tags in wireless sensor networks to track the movement of small animals, and in the use of RSSI as an indicator of individual distance values, as extensive in situ calibration is required. ‘Off the shelf’ devices may vary in performance, rendering data unreliable. We emphasise the importance of calibrating all equipment in animal tracking studies to reduce data uncertainty and error.

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.

Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks.

Radio frequency signals are commonly used in the development of indoor localization systems. The infrastructure of these systems includes some beacons placed at known positions that exchange radio packets with users to be located. When the system is implemented using wireless sensor networks, the wireless transceivers integrated in the network motes are usually based on the IEEE 802.15.4 standard. But, the CSMA-CA, which is the basis for the medium access protocols in this category of communication systems, is not suitable when several users want to exchange bursts of radio packets with the same beacon to acquire the radio signal strength indicator (RSSI) values needed in the location process. Therefore, new protocols are necessary to avoid the packet collisions that appear when multiple users try to communicate with the same beacons. On the other hand, the RSSI sampling process should be carried out very quickly because some systems cannot tolerate a large delay in the location process. This is even more important when the RSSI sampling process includes measures with different signal power levels or frequency channels. The principal objective of this work is to speed up the RSSI sampling process in indoor localization systems. To achieve this objective, the main contribution is the proposal of a new MAC protocol that eliminates the medium access contention periods and decreases the number of packet collisions to accelerate the RSSI collection process. Moreover, the protocol increases the overall network throughput taking advantage of the frequency channel diversity. The presented results show the suitability of this protocol for reducing the RSSI gathering delay and increasing the network throughput in simulated and real environments.

Understanding the process of social network evolution: Online-offline integrated analysis of social tie formation.

It is important to consider the interweaving nature of online and offline social networks when we examine social network evolution. However, it is difficult to find any research that examines the process of social tie formation from an integrated perspective. In our study, we quantitatively measure offline interactions and examine the corresponding evolution of online social network in order to understand the significance of interrelationship between online and offline social factors in generating social ties. We analyze the radio signal strength indicator sensor data from a series of social events to understand offline interactions among the participants and measure the structural attributes of their existing online Facebook social networks. By monitoring the changes in their online social networks before and after offline interactions in a series of social events, we verify that the ability to develop an offline interaction into an online friendship is tied to the number of social connections that participants previously had, while the presence of shared mutual friends between a pair of participants disrupts potential new connections within the pre-designed offline social events. Thus, while our integrative approach enables us to confirm the theory of preferential attachment in the process of network formation, the common neighbor theory is not supported. Our dual-dimensional network analysis allows us to observe the actual process of social network evolution rather than to make predictions based on the assumption of self-organizing networks.