Received Signal Strength Index Research Articles

Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach

A wireless sensor network (WSN) consists of distributed autonomous sensors deployed over a large geographical area. Wireless Sensor Networks (WSNs) are composed of large-scale sensors that are specifically assigned to do particular tasks, with a significant portion of these tasks involving reporting and monitoring activities. Nevertheless, because to the potential expansion of the network to several sensor nodes, the likelihood of collision is significantly increased. This research presents an innovative approach for conducting collision detection and mitigation in wireless sensor networks (WSN). The first step involves conducting a simulation of Wireless Sensor Networks (WSN), followed by the utilization of the Fractional Artificial Bee Colony (FABC) algorithm for the selection of cluster heads. In this context, the network-based parameter is derived by considering factors such as the Received Signal Strength Index (RSSI), priority level, delivery rate, and energy consumption. The Deep Recurrent Neural Network (DRNN) has been modified to suit the task of collision detection. The training of the deep recurrent neural network (DRNN) is conducted via the Lion Crow Search optimizer (LCSO). Following the completion of collision detection, the subsequent step involves the implementation of a collision mitigation process utilizing a pre-scheduling technique known as Dolphin Ant Lion Optimizer (Dolphin ALO). In this context, the evaluation of fitness encompasses many factors related to collision mitigation, including energy consumption, Sleep Index (SI), delivery rate, priority level, E-waste management, and E-save measures. The approach presented in this study demonstrated superior performance in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and collision detection rate. Specifically, it achieved the least energy consumption of 0.185, the greatest throughput of 0.815, the highest PDR of 0.815, and the highest collision detection rate of 0.930.

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

SummaryWireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.

IoT-Enabled Vacant Parking Slot Detection System Using Inkjet-Printed RFID Tags

It is challenging for motorists to manually search for a parking slot in a crowded parking area. Conventional methods for locating vacant or occupied space are regarded as inefficient and unreliable. In this work, an Internet-of-Things (IoT)-enabled vacant parking slot detection system is developed and tested using low-cost inkjet-printed passive ultrahigh-frequency (UHF) radio frequency identification (RFID) tags. The working principle of the proposed system is to analyze the backscattered signal strength from the tags at the receiver side for vehicle presence/ absence detection in the parking slots. First of all, we propose a low-cost passive tag optimized to work in UHF RFID-band pasted on plastic material display for parking slots. The data collected by the RFID reader from the tags is sent over message queuing telemetry transport (MQTT) protocol via Scotland 5G-network to the MongoDB database using a Python module Pymongo for decision-making and display of vacant parking slots to the motorists. A Python-based web-app extracts data from the database and shows it on a web-app display using the embedded JavaScript template (EJS) for each slot. Experimental observations conducted at parking slots in the presence of vehicles indicate a drastic reduction in the received-signal-strength-index (RSSI) value at the receiver end, which implies that RSSI signal drop can be utilized for the detection of vehicle presence in the parking slot. The suggested approach aims to facilitate the economical and effective location of available parking slots. The test results for the proposed system are recorded and presented, demonstrating its significant performance and showing great accuracy for the tested scenarios.

PSO-Based Target Localization and Tracking in Wireless Sensor Networks

Research of target localization and tracking is always a remarkable problem in the application of wireless sensor networks (WSNs) technology. There are many kinds of research and applications of target localization and tracking, such as Angle of Arrival (AOA), Time of Arrival (TOA), and Time Difference of Arrival (TDOA). The target localization accuracy for TOA, TDOA, and AOA is better than RSS. However, the required devices in the TOA, TDOA, and AOA are more expensive than RSS. In addition, the computational complexity of TOA, TDOA, and AOA is also more complicated than RSS. This paper uses a particle swarm optimization (PSO) algorithm with the received signal strength index (RSSI) channel model for indoor target localization and tracking. The performance of eight different method combinations of random or regular points, fixed or adaptive weights, and the region segmentation method (RSM) proposed in this paper for target localization and tracking is investigated for the number of particles in the PSO algorithm with 12, 24, 52, 72, and 100. The simulation results show that the proposed RSM method can reduce the number of particles used in the PSO algorithm and improve the speed of positioning and tracking without affecting the accuracy of target localization and tracking. The total average localization time for target localization and tracking with the RSM method can be reduced by 48.95% and 34.14%, respectively, and the average accuracy of target tracking reaches up to 93.09%.

Toward Simultaneous Localization and Speed Measurement of Mobile Vehicles via RF-ELP

Radio-frequency identification (RFID) electronic license plate (RF-ELP) has been widely used to enable various automatic vehicle identification applications. Endowing RF-ELP with mobile vehicle sensing capabilities, such as localization and speed measurement is of practical importance, yet there is no solution on the shelf. Moreover, the position information is essential for accurate speed measurement, while the related RFID-based vehicular localization and indoor mobile localization methods suffer from at least one of the following major limitations: 1) difficult to deploy in practice; 2) requiring moving speed in advance; 3) only working for indoor-speed vehicles; and 4) not well compatible to frequency-hopping mechanism. To overcome the above limitations, this article proposes an RF-ELP-based mobile vehicle sensing (RESensing) system. RESensing conducts a new signal phase collection strategy to ensure the phase coupling in road-speed cases and converts phases of each interrogation to the relative speed to make it immune to frequency hopping and interinterrogation phase fluctuation. Then, the speed measurement and longitudinal localization are simultaneously performed by solving a nonlinear optimization model. Furthermore, the propagation model and antenna radiation pattern are investigated to facilitate the received signal strength index (RSSI)-based accurate lane-level lateral localization. To our knowledge, RESensing is the first RF-ELP-based speed measurement and localization system for mobile vehicles. The performance of RESensing is evaluated by real experiments under specifications of GB/T 37987 and EPC C1G2, which shows that RESensing achieves the mean speed error ratio of 4.34%, the longitudinal localization error of submeter level, and the lane estimation accuracy of nearly 100%.

WPFD: Active User-Side Detection of Evil Twins

The bothersome evil twin problem has an active user-side remedy in the form of the Wireless Packet Forwarding Detector (WPFD). The evil twin issue can lead to further security problems, including man-in-the-middle (MITM) attacks. Open public Wi-Fi connections have provided potential answers to this issue, although they often need more data that people either cannot get or are too pricey for regular users. The solution that we created does not require these standards. It allows users’ notebooks to be used to check for evil twins. We have succeeded in developing a user-side detection system that can successfully identify the presence of an evil twin. The packet forwarding behavior generated by the evil twin and the TCP/IP (Transmission Control Protocol/Internet Protocol) protocol are both used by the WPFD. It can identify evil twins without a hitch when we utilize accessible Wi-Fi settings in public spaces or IoT smart homes with unencrypted WLANs (Wireless Local Area Network). However, neither additional data nor a wireless network administrator’s assistance is needed. We compare our work to various publications on popular Rogue Access Points (APs) or IoT (Internet of Things) smart homes. The WPFD does not require any extra setup to install on the host of any end user. According to experimental findings, the WPFD true positive and true negative rates are 100% even when Received Signal Strength Index (RSSI) is 45%.

Research on Indoor Positioning Algorithm Based on SAGA-BP Neural Network

The Indoor positioning based on the ZigBee received signal strength index has attracted more and more researchers’ attention and, due to its low cost, low hardware power consumption and easy implementation. However, because of multipath effects and shadow effects, traditional indoor positioning algorithms cannot obtain good positioning effects. In order to improve the accuracy of ZigBee indoor positioning, this paper proposes an indoor positioning algorithm of annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (SAGA-BP), and the superiority of this algorithm is proved through simulation and experiment. First, establish the position relationship between the received signal strength indicator(RSSI) and the target position, and arrange the node network structure model to collect signals to establish a fingerprint database. Then use the mechanism of the annealing algorithm combined with the genetic algorithm to optimize the initial weight and initial threshold of the neural network algorithm, so that it can quickly jump out of the local optimal solution and achieve high-precision positioning. Experiments have proved the effectiveness of the positioning algorithm. Compared with BP and GA-BP algorithms, SAGA-BP positioning algorithm has an average error of 0.75m for RSSI signals after acquisition and processing, and an average error of BP positioning algorithm is 1.24m. The average error of GA-BP algorithm is 0.98m. Thus, the SAGA-BP algorithm has higher positioning accuracy.

Elastic Routing Mechanism for Flying Ad Hoc Network

Flying Ad-Hoc Network (FANET) is a hot topic in current research. The design of routing mechanism is challenging because when the scale of Unmanned Aerial Vehicle (UAV) nodes is large, vast amount of routing overhead may lead to network collapse. An elastic routing mechanism is proposed for large-scale small UAVs multitasking scenarios. Firstly, the New-Unifying Connected Dominating Set (N-UCDS) algorithm is proposed to construct a virtual backbone network based on the connected dominating set. The number of neighboring nodes, remaining energy and link duration are considered to influence the UAV network performance when electing backbone nodes. Secondly, by deploying and running the New Better Approach to Mobile Ad-Hoc Network-Advanced (NBATMAN-ADV) routing protocol on the backbone nodes, the link quality can be evaluated by using the received signal strength index and signal-to-noise ratio of the physical layer data. In this way, the change of the link can be quickly sensed while reducing the routing overhead. The simulation results show that the routing protocol proposed in this paper has significantly improved average packet delivery rate, end-to-end delay and received throughput compared with other traditional proactive routing protocols.

A New Filtering Algorithm for Generating Path Loss Model to Improve Accuracy of Trilateration-Based Indoor Positioning System

Global Positioning System (GPS) is used to determine someone’s or something’s position. It is very accurate. But, GPS cannot be used in an indoor environment as its signal is blocked by an object (i.e. roof). To overcome this problem, Indoor Positioning System (IPS) is used in the indoor environment. There are two methods that IPS can use to determine the position. The first method is pattern matching. This method is highly accurate. But, the computation cost is expensive. The second method is the path loss model. Its computation cost is cheap. However, it is fairly accurate. In this paper, a new filter algorithm is proposed to improve path loss model accuracy. The algorithm works by filtering the received signal strength index (RSSI) in the training phase. The algorithm works by replacing the RSSI value at a certain distance by considering the current and the previous RSSI value. The experiment result shows that the proposed method has 3% better accuracy than the existing method.

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.

Long Range Radio Communication for Urban Sensor Networks

Society’s technological landscape is growing rapidly. In the era of smart cities, wireless technologies and their novel applications have begun to permeate almost every aspect of urban life. From controlling traffic signals to monitoring air quality, the Internet of Things (IoT) has emerged as a field with much potential, but some challenges. Being a relatively new field of research, there is much more to discover about IoT. Before developing proposed applications of the technology in smart cities, it is imperative to fully understand how IoT-based technologies work, including Long-Range Radio Communication (LoRa). This study aimed to test the feasibility of LoRa devices in the bustling, urban environment of New York City to identify limitations in the technology. Using a testbed containing two LoRa radio modules and a monopole antenna, we determined the relationship between the distance of the radios and received signal strength to be inversely proportional. We hypothesized that Received Signal Strength Index values from the transmissions could be used to determine the maximum range. Our results supported this hypothesis by determining the maximum range to be 250 meters. In addition, we found that the relationship followed an exponential decay curve. These findings will help future researchers further understand the limitations of LoRa in urban sensor networks and ultimately create stronger IoT smart city applications.

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.

Relative Angle Correction for Distance Estimation Using K-Nearest Neighbors

Indoor position estimation has been cited as not being accurate due to noisy environments. Variations of the k-nearest neighbor (kNN) algorithm have been used to mitigate this noise. In this paper, the concept of using the fingerprinting process with the radiation pattern of an antenna is explored. The contents of the paper focus on finding the distance between an RFID reader and a tag. Using the relative angle of the antenna with respect to a reference point, a model is introduced. Received signal strength index (RSSI) readings from the backscattered signal of the RFID tag were measured in a 7-foot $\times \,\,4$ -foot grid. The resulting data points were placed in the relative angle correction KNN (RAC-KNN) algorithm. Both regression and classification models of the RAC-KNN were implemented. As a result, the classification model yielded an 85% prediction rate of the distance estimation and the regression model resulted in a 0.2-foot average error.

An Optimal Cluster-Head Selection algorithm for Wireless Sensor Networks

The energy utilization is one of the most common challenges in Wireless Sensor Network (WSN), as frequent communication between the sensor nodes (SNs) results in huge energy drain. Moreover, optimization and load balancing within the WSN are the significant concern to grant intellect for the extensive period of network lifetime. As a matter of fact, many WSNs are deployed and operating outdoors is exposed to varying environmental conditions, which may further set grounds for severe performance degradation of such networks. Therefore, it is necessary to take into consideration the factors like radio signal strength in order to reduce the impact and to adapt to varying environmental conditions. Since clustering is a topological control technique to reduce the activity of SNs transceivers, it extensively increases overall system scalability and energy efficiency. It selects CH to manage the entire network to achieve longevity in WSN. In this paper, we present an optimal CH selection (OCHS) algorithm which is also based on environmental conditions to achieve energy efficiency and enhanced network lifetime. The originality of this work is that we have taken into consideration the received signal strength index (RSSI) of SNs from the base-station (BS). The OCHS algorithm mainly focuses on maximizing the network lifetime based on RSSI values and residual energy levels of SNs. TheOCHS algorithm is simulated on Cooja Simulator and its performance is compared with existing LEACH and HEED protocols. Simulation analysis and results proved that our OCHS algorithm can effectively enhance the network lifetime by two times and thus it is an energy-efficient way to choose a CH.

WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks

To achieve device-free person detection, various types of signal features, such as moving statistics and wavelet representations, have been extracted from the Wi-Fi Received Signal Strength Index (RSSI), whose value fluctuates when human subjects move near the Wi-Fi transceivers. However, these features do not work effectively under different deployments of Wi-Fi transceivers because each transceiver has a unique RSSI fluctuation pattern that depends on its specific wireless channel and hardware characteristics. To address this problem, we present WiDet, a system that uses a deep Convolutional Neural Network (CNN) approach for person detection. The CNN takes the 2-dimensional wavelet coefficients as input, and extracts effective and robust detection features automatically. With a large number of internal parameters, the CNN can record and recognize the different RSSI fluctuation patterns from different transceivers. We further apply the data augmentation method to improve the algorithm robustness to wireless interferences and pedestrian speed changes. To take advantage of the wide availability of the existing Wi-Fi devices, we design a collaborative sensing technique that can recognize the subject’s moving directions. To validate the proposed design, we implement a prototype system that consists of three Wi-Fi packet transmitters and one receiver on low-cost off-the-shelf embedded development boards. In a multi-day experiment with a total of 163 walking events, WiDet achieves 95.5% of detection accuracy in detecting pedestrians, which outperforms the moving statistics and the wavelet representation based approaches by 22% and 8%, respectively.

Research on Adaptive SVR Indoor Location Based on GA Optimization

Indoor positioning based on the received signal strength index of ZigBee has received more and more attention due to its low cost, low hardware power consumption and easy implementation. However, due to the existence of multi-path and shadow effects, traditional positioning algorithms often cannot achieve better positioning results. Support vector regression can use these relative errors as the characteristics of the fingerprint database to establish a regression map between the RSSI values and the positioning coordinates. Based on this, this paper proposes a genetic algorithm optimization support vector regression (GA-SVR) method to solve the problem of low ZigBee positioning accuracy. The penalty vector $$C$$ , RBF kernel width $$\sigma$$ , and loss function variable $$\in$$ of support vector regression are optimized by genetic algorithm in the proposed method, so that support vector regression achieves the best position prediction performance. Firstly, the training data is clustered by Gaussian hybrid clustering algorithm to establish a fingerprint database. Then the GA-SVM classifier is used to classify the test points. Finally, the coordinates of the test points are regressed and predicted by the GA-SVR model. The simulation and experiment in the actual scene prove the effectiveness of the proposed method. The experimental results show that compared with PSO-SVR, GS-SVR, PLS-SVR, SVR and WKNN algorithms, the GA-SVR algorithm has higher positioning accuracy.

Front End to Back End Speech Scrambler

As modern mobile devices are often equipped with a WLAN interface and many buildings are equipped with a WLAN infrastructure, WLAN could be used for localization. The aim of this work is to investigate the quality of different indoor positioning methods based on values of WLAN received signal strength index (RSSI) using commercially available devices (smart phones, personal digital assistants). Three positioning algorithms were considered: minimal Euclidian distance, intersections of RSSI-Isolines and a stochastic model based on Bayes' theorem. A measuring test bed within an empty seminar room, four different types of personal digital assistants (Dell, Fujitsu, HP, T-Mobile) and two types of access points (Netgear, Lancom) were used. No modification of hardware and software was conducted. Furthermore this paper shows how WLAN localization can be applied in a museum guide application. This paper also shows the visualization of spatial user position frequency pattern during a field trial within a museum.

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

Signal transmission and node deployment of a 2.4 GHz wireless sensor network: A case study in a persimmon orchard

The change of received signal strength index and packet loss rate under 2.4 GHz radio communication in a persimmon orchard in flowering phase was investigated. Texas CC2530 RF chip and the TinyOS system based WSN nodes were selected as instruments to conduct interwoven tests along a column of persimmon trees and different rows and columns of the orchard, and measure the receiving signal strength and packet loss rate which were in the three different heights from 0.80 m at the bottom of the canopy to 2.80 m at the top. In the tests, distance of each three trees was set as a tested distance, and there were eight tested distances in each row or column totally. Afterwards, all the obtained data underwent regression analysis. The results showed that when the transmitter and receiver were maintained at a fixed height, the received signal strength decreased, while the packet loss rate increased with the augment of tested distance. Furthermore, under the two situations where receiver was at the fringe of persimmon orchard and both transmitter and receiver were in the orchard, the models of signal strength attenuation affected by different factors along a row of persimmon tree were established and evaluated. Additionally, the curved surface graphs of different transceiver heights and different ranks of trees which were drawn by MATLAB software was able to reflect wireless signal transmission characteristics in persimmon orchard straightly.

Multiple Fingerprints-Based Indoor Localization via GBDT: Subspace and RSSI

The demand for accurate indoor localization has become more and more urgent in location-based services (LBS). Since the traditional indoor localization is based on a single fingerprint, which is susceptible to environmental changes, it is hard to obtain high accuracy. In order to fill this gap, we propose a new multiple fingerprints method used in the indoor environment without extra measurement, and it can reduce the uncertainty caused by a single fingerprint. The multiple fingerprints are based on the signal Subspace and the received signal strength index (RSSI) and others, where the signal Subspace represents the characteristic representation of the received array signal and the RSSI represents the signal strength. First, we propose an algorithm named Subspace gradient boost decision tree (Subspace-GBDT) to obtain a strong classifier, which can make a successful prediction. Second, we propose another algorithm named double threshold assisted mode (DTAM) to combine multiple fingerprints. It can achieve accurate prediction by setting two different threshold parameters, the threshold of samples number and classifiers number, and reduce errors of matching. The experiment results show that the proposed method can obtain higher cumulative distribution function (CDF) value and localization accuracy than the traditional methods.