Received Signal Strength Research Articles

Investigating the Influence of Temperature on UAV Signal Quality

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system declines with rising temperatures. Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

NLOS visible light positioning and communication based on LoRa modulation.

With the rapid development of the Internet of Things, location-based services are becoming increasingly important, especially in indoor environments. Visible light positioning (VLP) has garnered widespread attention due to its high accuracy, low cost, and immunity to the radio frequency electromagnetic interference. However, traditional VLP relies on line-of-sight paths, making it impractical in complex and dynamic indoor environments. In this paper, we propose a non-line-of-sight (NLOS) visible light positioning and communication system based on LoRa modulation to address the issue of link obstruction. LoRa is employed to recover position information transmitted from light emitting diodes (LEDs) over NLOS links, enabling reliable communication under weak lighting conditions. We establish a geometric relationship between the LED and the virtual image of the photodetector (PD). Leveraging the NLOS channel model, we derive the relationship between the received signal strength and the distance from LEDs to the virtual image of the PD. Through this relationship, the trilateration method is applied to calculate the position of the receiver. Based on experimental results, the proposed system achieves 90th percentile localization accuracy of less than 25 and 35 cm for the PD heights of 60 and 80 cm, respectively.

Improved Particle Filter in Machine Learning-Based BLE Fingerprinting Method to Reduce Indoor Location Estimation Errors

Indoor position fingerprint-based location estimation methods have been widely used by applications on smartphones. In these localization estimation methods, it is very popular to use the RSSI (Received Signal Strength Indication) of signals to represent the position fingerprint. This paper proposes the design of a particle filter for reducing the estimation error of the machine learning-based indoor BLE location fingerprinting method. Unlike the general particle filter, taking into account the distance, the proposed system designs improved likelihood functions, considering the coordinates based on fingerprint points using mean and variance of RSSI values, combining the particle filter with the k-NN (k-Nearest Neighbor) algorithm to realize the reduction in indoor positioning error. The initial position is estimated by the position fingerprinting method based on the machine learning method. By comparing the fingerprint method based on k-NN with general particle filter processing, and the fingerprint estimation method based on only k-NN or SVM (Support Vector Machine), experiment results showed that the proposed method has a smaller minimum error and a better average error than the conventional method.

P4 FANET In-band Telemetry (FINT) for AI-assisted wireless link failure forecasting and recovery

This paper introduces a novel framework for enhancing quality of service predictability in Flying ad hoc Networks (FANET) by leveraging P4 data-plane programmability. The proposed solution, P4 FANET In-Band Telemetry (FINT), is specifically designed to tailor the limited resources in wireless networks and is extended to collect not only the standard INT metadata but also novel essential Unmanned Aerial Vehicles (UAV) real-time metrics, including Received Signal Strength Indicator (RSSI), geolocation information and CPU load. These parameters are then fed into an artificial intelligence (AI) system, enabling proactive prediction of FANET link failures. By integrating P4 FINT and AI, our framework aims to improve the availability and overall performance of UAV-based networks through advanced link failure forecasting.

A lossless quantization approach for physical-layer key generation in vehicular ad hoc networks based on received signal strength

Vehicular Ad Hoc Networks (VANETs) provide various benefits and play a crucial role in improving efficiency and ensuring human safety across different applications. However, these advantages also give rise to security challenges and privacy concerns, necessitating a thorough examination of security attacks. Current key generation schemes, particularly those based on the Physical-Layer Model (PLM), face limitations such as low key generation rates and inadequate randomness. This paper introduces an innovative key generation method that utilizes adaptive physical-layer techniques and lossless quantization. The method involves an eight-level quantization process, which enables precise granularity and adaptive selection of quantization thresholds to tailor the computation of Received Signal Strength (RSS) measurements to individual needs. The adaptive approach ensures the retention of information within RSS measurements, resulting in reduced bit disagreement rates and enhanced randomness of the generated keys. Simulated evaluations demonstrate the effectiveness of the proposed method, showing superior performance in terms of bit generation, entropy, and secrecy rates, while also minimizing the occurrence of unnecessary measurements. This advancement holds significant promise for strengthening the security framework within VANETs.

Path Loss Models for 5G Communications System in Corridors Environment

This paper presents the propagation path loss channel models, developed from real-field measurement in Universiti Teknologi Malaysia (UTM) Kuala Lumpur, Malaysia. The purpose of the study is to characterize the channel at 28 GHz for 5G communications system in line-of-sight corridors environment. Measurement campaigns were conducted to measure the wireless signal of received signal strength at three different construction of straight corridors, narrow, wide, and open corridors. The large-scale path loss models are developed using the closed-in reference distance and floating–intercept modeling approaches. Besides contributing path loss models at 28 GHz for different corridors dimension, the result found in this work discovered the breakpoint distance of radio propagation is seen varies differently at those corridors.

An infrastructure‐less emergency location detection system using basic cellular device‐to‐device connection

AbstractOne of the most time‐sensitive and critical tasks for first responders in the aftermath of a disaster is to locate people for effective and successful rescue operations. The existing solutions for large‐scale location detection of citizens depend on expensive equipment that may not be readily available in many urban areas and also cannot cover a vast area quickly. To address this challenge, the present study proposes a novel location estimation method by creating a Mobile Ad Hoc Network (MANET) using citizens' mobile phones. The proposed solution can accurately estimate the location of mobile devices in a MANET based on their Received Signal Strength (RSS) and communicate the estimated location coordinates to first responders without any dependencies on telecommunication infrastructures. This system will help first responders locate and map mobile devices in the affected area in a matter of seconds to improve the efficiency and effectiveness of postdisaster rescue operations. The envisioned solution offers unique capabilities that differentiate it from previously existing methods. The proposed method directly estimates device locations using their basic cellular signal strength without any dependency on GPS and relies only on basic cellphone signals. Therefore, nonsmartphones can also join and function in the envisioned MANET system. It also does not impose any computational operations or requirements for installing an app on citizens' phones. The performance of the proposed system was empirically evaluated using a simulation analysis. The results indicated that the method could efficiently and accurately discover more than 93% of mobile devices and estimate their locations.

A Deep Learning-Based Reliable Link Prediction Model for Achieving Traffic-Aware Routing in Mobile Ad-hoc Networks

One of the most promising wireless network architectures is the mobile ad-hoc network (MANET). Researchers have introduced enormous protocols for efficient routing, but it does not provide a reliable communication link for data transmission. Therefore, this research proposes a reliable link prediction-based traffic-aware deep learning routing protocol in MANET to maintain path stability and reliability to construct efficient routing. The reliable traffic-aware link prediction model used in this research is Fuzzy-based Deep Extreme [Formula: see text]-Learning (FDEQL) model. The fuzzy logic rule is used to compute the status of a wireless link to build stable and faster paths toward destinations. Traffic patterns can affect the efficiency of a node. So, to cope with the traffic pattern in MANET, the point-to-point (P2P) and end-to-end (E2E) traffic matrices are initially constructed. To evaluate whether the wireless link is reliable or not, the proposed approach utilizes fuzzy rules by considering essential parameters such as neighborhood overlap (NOVER), bipartivity index (BI), node mobility (NM), data rate (DR), received signal strength indicator (RSSI) and buffer occupancy (BO). The output is the [Formula: see text]-value for reliable link prediction. The performance of a proposed model is validated with other baseline methods based on various measures such as energy consumption, route failure, throughput, delay, packet delivery ratio (PDR), normalized routing load (NRL) and buffer occupancy by varying the mobility speed from 5 to 30[Formula: see text]m/sec, number of nodes and simulation time, respectively. At the mobility speed of 10[Formula: see text]m/sec, the proposed model has a delay of 0.08 sec, PDR of 99% and throughput of 1903.4[Formula: see text]kbps. The proposed model achieves a delay of 19.37[Formula: see text]msec, PDR of 96.22%, and throughput of 132.95[Formula: see text]kbps, respectively, for 30 nodes. If the simulation runs for 300 sec, the suggested model achieves a delay of 2.98 sec and a PDR of 0.946, respectively.

Experimental analysis for comparison of wireless transmission technologies: Wi-Fi, Bluetooth, ZigBee and LoRa for mobile multi-robot in hostile sites

This research paper conducts a thorough comparison of four prominent transmission technologies suitable for mobile robots operating in challenging environments. Emphasizing key factors such as signal strength, noise resistance, and data transfer efficiency, the study aims to identify the optimal communication solution in hostile conditions. The exploration delves into the intricacies of received signal strength indication (RSSI) and signal-to-noise ratio (SNR), revealing distinctive traits and trade-offs among the technologies. Navigating through the complexities of frequency bands, modulation types, and communication topologies, the paper examines the impact of obstacles, energy consumption dynamics, and potential real-world applications. Beyond contributing to the fields of robotics and communication, the study offers practical insights for stakeholders seeking resilient and efficient transmission methods for mobile robotic applications. Advocating for long range (LoRa) as the preferred transmission technology in hostile environments, the paper highlights its unmatched immunity to noise, stability, and minimal energy consumption. These findings provide valuable guidance for technology choices in collaborative mobile robot operations under challenging conditions. This research sets the stage for future developments in robotic communication, underscoring the crucial role of selecting the right transmission means for mission-critical applications in hostile environments.

Received Signal Strength Indicator-Based Indoor Localization Using Nonlinear Dual Set-Membership Filtering

Received Signal Strength Indicator-Based Indoor Localization Using Nonlinear Dual Set-Membership Filtering

Description and analysis of Sigfox received signal strength indicator dataset by using statistical techniques

Low power wide area network (LPWAN) technology has expanded and is essential in the development of applications for the internet of things (IoT). The Sigfox LPWAN network is characterized by its long-range coverage, low cost and power consumption. In this article, a set of 5174 values is analyzed, containing 1606 null RSSI data, obtained with the Sipy module and MicroPython, which provide a coverage map of several points with a resolution of 200 meters deployed in Quito–Ecuador. It is evaluated the type of distribution to which the set of network measurements is adjusted and an optimal 900 MHz propagation model in suburban environments is determined from the measurements obtained from the known base station. As a result, the lost values of RSSI were predicted using the inverse normal distribution method in the original values, observing that they conform to a logistic distribution. The data from the base station were subjected to a data augmentation algorithm designed in MATLAB, determining that the stanford university interim (SUI) model reduces the precision error in the trend of the curve by not presenting changes greater than 5 dB, achieving a precision of 97% with respect to the fit of the curve of the data.

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

A Breakthrough Authentication Protocol for Fortifying IoT-Cloud Security

Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).

Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Wi‐Fi fingerprint‐based indoor location: Multiple fingerprints and multiple classifiers with two‐layer fusion weights

SummaryIndoor location based on Wi‐Fi fingerprint has attracted extensive attention recently, since it only requires a mobile device and existing network; it does not require additional infrastructure and hardware. However, localization using only received signal strength fingerprint is susceptible to dynamic environments and device heterogeneity. Localization using signal strength differences fingerprint and hyperbolic location fingerprint which extracted from received signal strength fingerprint is a powerful measure to overcome the device heterogeneity. Existing fusion‐based methods do not fully use of the mutual fusion function of multiple fingerprints, which leads to low positioning accuracy. In order to further improve the positioning accuracy, we propose a multifingerprint multiclassifier and two‐layer fusion weight positioning model. The proposed approach first creates a multiple fingerprints group by gleaning signal strength differences fingerprint and hyperbolic location fingerprint from received signal strength fingerprint. In order to deal with the accuracy decline caused by a single classifier in complex scenes, three typical classifiers, random forest (RF), K‐nearest neighbor (KNN), and support vector machine (SVM), are adopted, and a fusion weight selection algorithm is proposed to improve the localization accuracy by choosing fusion weights intelligently from the two‐layer fusion weights. Experiment results show that the proposed scheme is effective to resolve device heterogeneity, and the position accuracy is improved about 16.9% and 5.8%, respectively, compared with single fingerprint and classifier schemes. The results show that, compared with the single fingerprint scheme, the position accuracy is improved at least 16.9% and 28.4% for the homogeneous and heterogeneous equipments, respectively; as for the proposed multiclassifier fusion method, it achieves at least 5.8% and 8.7% accuracy improvement for the cases with homogeneous and heterogeneous equipments, respectively, compared with other traditional classifiers such as RF, KNN, and SVM.

A Combined Filtering Method for ZigBee Indoor Distance Measurement.

Indoor distance measurement technology utilizing Zigbee's Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon's Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee's transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm's effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications.

Assessing Short-range Shore-to-Shore (S2S) and Shore-to-Vessel (S2V) WiFi Communications

Wireless communications increasingly enable ubiquitous connectivity for a large number of nodes, applications and scenarios. One of the less explored scenarios are aquatic ecosystems, specially when enabled by near-shore and short-range communications. Overwater communications are impaired by a number of distinguishing dynamic factors, such as tides, waves or node mobility, that lead to a widely fluctuating and unpredictable channel. In this work, we empirically characterize near-shore, overwater channels at 2.4 GHz under realistic conditions, including tidal variations, and relatively short TX-RX separations. To this end, we conducted experiments in a coastal estuarine region and on a harbor to characterize Shore-to-Shore (S2S) and Shore-to-Vessel (S2V) communication channels, respectively, and to identify major factors impairing communication in such scenarios. The empirical results show that constructive/destructive interference patterns, varying reflecting surface, and node mobility (i.e. travel direction and particular maneuvers) have a relevant and noticeable impact on the received signal strength. Thus, a set of parameters should be simultaneously considered for improving the performance of communication systems supporting S2S and S2V links, namely tidal variations, reflection surface changes, antenna height, TX-RX alignment and TX-RX separation. The results useful provide insights into realistic S2S and S2V link design and operation.

An efficient estimator for source localization in WSNs using RSSD and TDOA measurements

Range-based localization has received considerable attention in wireless sensor networks due to its ability to efficiently locate the unknown source of a signal. However, the localization accuracy with a single set of measurements may be inadequate, especially in dynamic and noisy environments. To mitigate this problem, received signal strength difference (RSSD) and time difference of arrival (TDOA) measurements are used to develop an efficient estimator to reduce the bias and improve localization accuracy. First, the RSSD/TDOA-based maximum likelihood (ML) localization problem is transformed into a hybrid information nonnegative constrained least squares (HI-NCLS) framework. Then, this framework is used to develop an effective bias-reduction localization approach (BRLA) with a two-step linearization process. The first step employs a linear solving method (LSM) which exploits an active set method to obtain a sub-optimal estimator. The second step uses a bias reduction method (BRM) to mitigate the correlation from linearization and a weighted instrumental variables matrix (IVM) which is weakly correlated with the noise but strongly correlated with the data matrix (DM) is used in place of the DM. Performance results are presented which demonstrate that the proposed BRLA provides better localization performance than state-of-the-art methods in the literature.

Distributed Embedded System for Air Quality Monitoring based on Long Range (LoRa) Technology

This study addresses the persistent challenge of urban air quality deterioration through the introduction of the Distributed Embedded System (DES). In response to limitations associated with conventional air quality measurement methods, the DES system offers a cost-effective and portable alternative for real-time monitoring. The study focuses on implementing a low-cost distributed system and developing effective communication protocols between sensor nodes. Equipped with Metal Oxide Semiconductor (MOS) sensors for pollutant gases, optical sensors for particulate matter, and meteorological sensors, the system strategically deploys multiple nodes within a 4 km range of urban areas. Real-time AQI and pollution severity have been measured for various locations. Implementation of Long Range (LoRa) communication technology allows the sensor nodes to efficiently transmit data to a central base station. Observations of received signal strength and signal quality indicate reliable and effective communication. To validate accuracy and reliability, acquired DES system data undergoes comparative analysis with data from a government-established meteorological station, revealing a strong correlation. This innovative approach presents a promising solution for widespread, continuous, and cost-effective real-time air quality monitoring in urban areas. The DES system addresses key challenges associated with air pollution, offering a portable and accessible tool that could revolutionize urban air quality management.