Received Signal Strength Indicator Research Articles

RSSI 데이터 기반 머신러닝 실내 측위 연구

With the recent development of science and technology, localization technology has made it possible to provide customized mobile services based on real-time location information to users through smartphones and Internet of Things devices. In particular, GPS-based real-time localization is commonly used. However, unlike outdoors, not only does the GPS reception rate decrease significantly indoors, but it is also difficult to accurately measure the user's indoor location using only GPS latitude and longitude information. In this study, we propose to measure the user's indoor location using RSSI (received signal strength indicator). For this purpose, an indoor localization study was conducted by applying machine learning classifiers, SVM, decision tree, ExtraTrees, random forest, and KNN to RSSI data. Among the machine learning classifiers, random forest exhibited the best performance. Therefore, we applied random forest-based RFE to extract features from RSSI data. We confirmed that even with a smaller amount of data, it was possible to achieve more accurate indoor positioning. In addition, it was confirmed through regression analysis that latitude and longitude can be estimated from RSSI data, so that indoor as well as outdoor locations can be estimated.

Analysis of Propagation Characteristics in Unmanned Aerial Vehicle (UAV) System

Unmanned Aerial Vehicle (UAV) has gained great attention to the spread of communication in civilian and military applications. UAV communication channel has its own characteristics compared to cellular and satellite systems, which are widely used. Thus, an accurate channel characterization is very important to optimize the performance and design of an efficient UAV communication system. However, several challenges exist in UAV channel modeling. For example, channel propagation characteristics of UAVs are still less explored. Therefore, this research discusses the propagation characteristics of UAV communication systems. Due to the limitation of the measurement tools, the propagation characteristics identified in this research was the pathloss coefficient value and optimum height based on the value of Received Signal Strength Indicator (RSSI) measurement results at different distance and heights. The link communication used 433 MHz telemetry. The results of pathloss coefficient at heights of 10 m, 20 m, and 30 m are 1.56 m, 1.77 m, and 1.99 m. While the results of the optimum height of 10 m, 20 m, and 30 m are 1.39 m, 1.32 m, and 1.47 m.

Prototype Design of a Fishing Boat Safety Monitoring System Using LoRa and Microsensor Devices

This paper reports a prototype of a low-cost tracking and monitoring system to address the challenges faced by Indonesian fishermen. The lack of safety equipment in their boats exposes them to high risks of work-related accidents and illnesses. Data from the National Basarnas Center reveals a staggering 24,000 annual fatalities among fishermen during their activities. These issues stem from a combination of factors: poorly designed boats, low prioritization of safety, and the lack of readily available preventive measures. Moreover, the development of telecommunications infrastructure in aquatic areas presents its unique obstacles. In response, this study proposes a prototype design for a "Fishing Boat Safety Monitoring System" utilizing LoRa and Microsensors for proactive and preventive measurement by tracking the boat position and sending the data via a long-distance wireless transmission with Low-Power Wide Area Network (LPWAN) scheme based on frequency-spread spectrum technology. This LPWAN serves as the substitution for cellular network which is usually not available in the ocean. The tracking system uses a low-cost TTGO T-Beam LoRa32 V1.1 Microcontroller Unit (MCU) board that has an embedded SX1276 LoRa module and Neo-6M GPS module. The system also uses a GY-25 gyroscope microsensor. The system implemented a 923 MHz LoRa signal for point-to-point communication between the transmitter to receiver. This research has successfully yielded a developed device capable of tracking the location of boats up to 2 km from the shoreline with -113 dBm received signal strength indicator (RSSI) and around 60% of data quality of service (QoS). Further research will explore the use of high-gain antennas and signal amplifiers integration with embedded LoRa on the MCU board to expand the coverage area of the LoRa signal.

Software Defined Radio for GNSS Radio Frequency Interference Localization.

The use of radio direction finding techniques in order to identify and reject harmful interference has been a topic of discussion both past and present for signals in the GNSS bands. Advances in commercial off-the-shelf radio hardware have led to the development of new low-cost, compact, phase coherent receiver platforms such as the KrakenSDR from KrakenRF whose testing and characterization will be the primary focus of this paper. Although not specifically designed for GNSSs, the capabilities of this platform are well aligned with the needs of GNSSs. Testing results from both benchtop and in the field will be displayed which verify the KrakenSDR's phase coherence and angle of arrival estimates to array dependent resolution bounds. Additionally, other outputs from the KrakenSDR such as received signal strength indicators and the angle of arrival confidence values show strong connections to angle of arrival estimate quality. Within this work the testing that will be primarily presented is at 900 MHz, with results presented from a government-sponsored event where the Kraken was tested at 1575.42 MHz. Finally, a discussion of calibration of active antenna arrays for angle of arrival is included as the introduction of active antenna elements used in GNSS signal collection can influence angle of arrival estimation.

Adaptive elliptic trajectory-based received signal strength indicator antenna tracking algorithm

The continuous telemetry transmission between unmanned aerial vehicles (UAVs) and ground control stations is important particularly in scenarios lacking global positioning system (GPS) data. This paper proposes an adaptive novel elliptic trajectory formula-based tracking algorithm for received signal strength indicator (ANETF-RSSI) which dynamically optimizes model parameters based on energy-associated RSSI measurement errors. ANETF-RSSI generates a variable two-dimensional (2D) RSSI map to identify optimal paths, even under challenging conditions like circular flight paths, varying operating ranges, and accelerated maneuvering, which causes uncertainty into RSSI measurements during flight. In contrast to previous methods, the proposed approach eliminates the reliance on telemetry data such as GPS or complex multiantenna configurations, ensuring robust UAV communication continuity across routes ranging from 100 m to 100 km, even as the UAV rotates around the antenna. This method offers substantial contributions, including enhanced monitoring precision, simplified hardware configurations, continuous tracking with superior accuracy, and adaptability to diverse range routes without the need for preflight parameter tuning. Performance evaluations demonstrate that the proposed ANETF-RSSI method consistently outperforms existing technique, improving nominal performance by 32.02% in the most challenging operational scenarios and achieving a remarkable 48.76% improvement in minimum RSSI values. Consequently, this research provides a versatile and adaptive tracking solution for unexpected UAV flight trajectories.

Indoor Localization System Based on RSSI-APIT Algorithm.

An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system's localization capabilities. In a complex environment of 100 m2 in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m.

Wi-Fi Received Signal Strength Indicator (RSSI) Factors in Influencing Indoor Signal: A Review

The Received Signal Strength Indicator (RSSI) is a crucial metric for assessing the quality of Wi-Fi signals, measured in decibels (dBm). The RSSI value is affected by several factors, such as distance from the access point, obstructions in the signal path, and interference from other electronic devices. Although these factors are commonly influencing RSSI, there is a lack of study to reveal the significance factors that influence indoor signal. This review article explores the various factors that influence the strength of Wi-Fi signals indoors, with a specific focus on the RSSI. This article discusses these factors in detail and their impact on the RSSI and overall quality of the Wi-Fi signal from literature. By understanding these factors, network administrators and users can take steps to optimize their Wi-Fi networks and improve the reliability and performance of their devices.

A hybrid RSSI and AoA indoor positioning approach with adapted confidence evaluator

Aiming at the indoor positioning system of hybrid received signal strength indicator (RSSI) and angle of arrival (AoA), this paper proposes an indoor positioning algorithm with an adaptive confidence based multi-objective optimization evaluator (ACMOOE) which improves the positioning accuracy by adjusting the influence of the two positioning methods adaptively. A multi-objective optimization algorithm based positioning model is established and particle swarm optimization is applied to reduce the positioning accuracy loss caused by the approximate transformation process. An adaptive confidence evaluation method of RSSI and AoA target is designed, which reduces the positioning accuracy loss caused by unreasonable weight setting. Finally, in order to verify the proposed algorithm, an indoor wireless sensing system is built in the actual indoor scene. Experimental results show that compared with the traditional hybrid positioning algorithm, the positioning error of the proposed ACMOOE is 0.45 m which improves the positioning accuracy by 18.7%.

Reliable routing in MANET with mobility prediction via long short-term memory

A MANET consists of a self-configured group of transportable mobile nodes that lacks a central infrastructure to manage network traffic. To facilitate communication, govern route discovery, and manage resources, all moving nodes in multi-hop wireless networks (MANETs) work together. These networks struggle with dependability, energy consumption, and collision avoidance. The goal of this research project is to establish a new, dependable MANET routing model, where the selection of predictor nodes comes first. For selecting predictor nodes based on factors like distance, security (risk), Receiver Signal Strength Indicator (RSSI), Packet Delivery Ratio (PDR), and energy, the adaptive weighted clustering algorithm (AWCA) is used in this case. Using the Interfused Slime and Battle Royale Optimization with Arithmetic Crossover (IS&BRO–AC) model, the node with the lower weight is selected as the Cluster Head (CH). Additionally, mobility prediction is carried out, in which the node mobility is forecast using Improved Long Short Term Memory (LSTM) while taking distance and Receiver Signal Strength Indicator (RSSI) into account. Based on the forecast, trustworthy data transfer is implemented, ensuring more accurate and dependable MANET routing. The examination of RSSI, PDR, and other metrics is completed at the end.

Firefly-Aquila optimized Deep Q network for handoff management in context aware video streaming-based heterogeneous wireless networks

Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB.

FUBA: A fuzzy‐based unmanned aerial vehicle behaviour analytics for trust management in flying ad‐hoc networks

AbstractFlying Ad‐Hoc Network (FANET) is a promising ad hoc networking paradigm that can offer new added value services in military and civilian applications. Typically, it incorporates a group of Unmanned Aerial Vehicles (UAVs), known as drones that collaborate and cooperate to accomplish several missions without human intervention. However, UAV communications are prone to various attacks and detecting malicious nodes is essential for efficient FANET operation. Trust management is an effective method that plays a significant role in the prediction and recognition of intrusions in FANETs. Specifically, evaluating node behaviour remains an important issue in this domain. For this purpose, the authors suggest using fuzzy logic, one of the most commonly used methods for trust computation, which classifies nodes based on multiple criteria to handle complex environments. In addition, the Received Signal Strength Indication (RSSI) is an important parameter that can be used in fuzzy logic to evaluate a drone's behaviour. However, in outdoor flying networks, the RSSI can be seriously influenced by the humidity of the air, which can dramatically impact the accuracy of the trust results. FUBA, a fuzzy‐based UAV behaviour analytics is presented for trust management in FANETs. By considering humidity as a new parameter, FUBA can identify insider threats and increase the overall network's trustworthiness under bad weather conditions. It is capable of performing well in outdoor flying networks. The simulation results indicate that the proposed model significantly outperforms FNDN and UNION in terms of the average end‐to‐end delay and the false positive ratio.

Linear-in-dB Logarithmic Signal Strength Sensor Circuit for Wireless Power Transfer Receivers

Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works.

Bluetooth 5.0 Suitability Assessment for Emergency Response within Fire Environments

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual’s location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0’s performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters.

Smart Traffic Light Prototype Implementation using LoRa Technology

Smart traffic light systems have the potential to significantly improve traffic flow and safety in both urban and suburban areas. Most smart traffic lights were deployed in a city with adequate infrastructure. However, building a smart traffic light in suburban areas can be challenging due to the limited availability of communication infrastructure. This paper aims to build and implement a smart traffic light system in a suburban area by incorporating LoRa (Long Range) technology for communication. LoRa is a low-power wireless technology that allows long-distance communication between devices and has low data rates and power consumption. It was created especially for Internet of Things (IoT) devices that run on batteries. The peer-to-peer method was used, which utilized three LoRa nodes for enabling the communication between traffic lights. Usability testing was conducted to determine the impact of the smart traffic light system on traffic flow and safety. The result of the testing shows that the system is necessary to improve traffic management in suburban areas, and the respondents were positive about its potential benefits. Received Signal Strength (RSS) was measured in two different environments: paddy field and residential areas in Kampung Alor Ara, Arau, Perlis. Results show a slight difference in RSS indicator versus distance for the placement of traffic lights in three-junction lanes. The optimum distance between any two traffic lights is 100m in the paddy field area. While for residential areas, the optimum distance is only 50m. The successful implementation of this prototype demonstrates the potential of LoRa technology in enhancing traffic management in suburban areas. Further research such as implementing artificial intelligence (AI) and utilizing advanced LoRa devices and antennas is needed to explore the scalability and long-term sustainability of the system.

An improved indoor positioning based on crowd-sensing data fusion and particle filter

Due to the lack of global positioning system (GPS) signals in some enclosed areas, indoor localisation has recently gained significant importance for academics. However, indoor localisation has a number of challenges and defects, including accuracy, cost, coverage, and ease of use. This paper explores the integration between the inertial measurement unit (IMU) and Wi-Fi-based received signal strength indicator (RSSI) measurements, demonstrating their combined potential for robust indoor localisation. IMUs excel at capturing precise short-term motion dynamics, offering insights into an object’s acceleration and orientation. Conversely, RSSI measurements serve as valuable indicators for relative positioning within indoor environments. By fusing data from these sources, our approach compensates for the inherent weaknesses of each sensor type. To achieve accurate indoor positioning, we employ techniques such as sensor fusion, Wi-Fi fingerprinting, and dead reckoning. Wi-Fi fingerprinting allows us to create a database that maps RSSI measurements to specific locations, while dead reckoning helps mitigate drift and inaccuracies. By combining these methods, we estimate a device’s position with increased precision. Through experimental evaluation, we assess the performance and efficiency of our integrated approach, comparing the estimated path or new location with a predefined reference path. The findings emphasise a significant improvement in accuracy, with the integration of crowd-sensing, particle filtering, and magnetic fingerprinting techniques resulting in a notable increase from 80.49% to 96.32% accuracy.

Development of a Precision Feeding System with Hierarchical Control for Gestation Units Using Stalls

To obtain good productive performance, sows have different nutrition requirements at different gestation periods. However, in gestation stalls, conventional feeders have large relative errors, management is difficult because of the large numbers of sows, and there are shortcomings in feeding precision and data management. In order to achieve precision feeding and enhance the control of multiple feeders for gestating sows housed in stalls, this study was carried out to investigate a precision feeding system that could be controlled at multiple levels. This system consisted of an electronic sow feeder (ESF), controller area network (CAN), personal digital assistant (PDA), central controller, and Internet of Things platform (IoTP). The results of the experiment showed that relative errors of 60 ESFs delivering feed were within ±2.94%, and the coefficient of variation was less than 1.84%. When the received signal strength indicator (RSSI) ranged from −80 dbm to −70 dbm, the packet loss rate of the PDA was 3.425%. When the RSSI was greater than −70 dbm, no packet loss was observed, and the average response time was 556.05 ms. The IoTP was at the performance bottleneck when the number of concurrent threads was greater than 1700. These experimental results indicated that the system was not only highly accurate in delivering feed, but was also highly reliable in the transmission of information, and therefore met the production requirements of an intensive gestation house.

LTE RSSI Based Vehicular Localization System in Long Tunnel Environment

Current navigation systems rely heavily on global navigation satellite systems (GNSSs), which provide stable positioning results in open-sky environments but exhibit severe performance degradation in areas where the signal is compromised, such as dense urban environments, underground parking lots, and tunnels. Current navigation systems use an initial entry velocity to estimate a vehicle's position in a tunnel. Therefore, when the speed of a vehicle changes, a corresponding error occurs with respect to the determined position of the vehicle. This study proposes a novel localization technology to estimate a vehicle's position using long-term evolution (LTE) signals measured using a smartphone in a tunnel. Many antennas are installed along the length of the tunnel to increase LTE coverage. Thus, the LTE received signal strength indicator (RSSI) forms a unique pattern with several peaks in the tunnel. The accumulated LTE RSSI pattern was used to estimate the vehicle position in the tunnel. After the LTE fingerprint inside the tunnel was constructed, the position with the highest correlation with the LTE RSSI sequence of the user buffer was determined as the current vehicle location. To demonstrate the feasibility of the proposed system, we conducted an extensive field test in an actual tunnel using various off-the-shelf smartphones. Experimental results show that the proposed technology provides stable performance throughout the tunnel space.

Analysis of Listen Before Talk Protocol Implementation in Terrestrial Network Using Multi-Node LoRa Communication

Wireless communication today plays a very important role in technological development but often faces challenges in terms of reliability and quality of data transmission; this study implements the Listen Before Talk (LBT) protocol in the terrestrial network environment through Multi-Node LoRa communication to test its effectiveness in avoiding packet loss ending the quality of communication in variations of the land environment. Test results showed that LBT effectively produced zero percentage packet loss at 50 to 500 meters. At the same time, LoRa devices could maintain communication quality even though the Received Signal Strength Indicator (RSSI) value decreased with the distance. The findings provide positive indications of the potential of LBT protocols inning data transmission integrity in terrestrial environments and foster the development of reliable and high-quality wireless communications in complex ground network environments.

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

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

Enhancing workplace safety: A flexible approach for personal protective equipment monitoring

Workplace safety is a prominent concern, motivating researchers across diverse disciplines to investigate valuable ways to address its challenges. However, creating an efficient system to address this issue remains a significant challenge. Since many accidents happen due to improper usage or complete removal of Personal Protective Equipment (PPE), one straightforward method for enhancing workplace security involves monitoring their usage This paper introduces an Operator Area Network (OAN) system which improves the existing solutions by increasing portability across different users and environments, non-intrusiveness and privacy. To enhance robustness in detecting the situations in which PPEs are not used correctly, we take advantage of Machine Learning to analyse the received signal strength indicator (RSSI) between PPEs in the same OAN The novelty of this work is that it does not exploit RSSI as a proxy of the distance but instead recognizes a signature of the correct wearing of the PPE By employing this system, employers can effectively ensure the proper usage of PPE devices at their worksites while also minimizing any adverse effects on workers’ comfort and reducing the setup burden for employers. The system runs a Support Vector Machine (SVM) model several times per second and employs a post-processing algorithm to enhance its initial accuracy further As a result, the system effectively reduces false positives by about 80% and swiftly detects instances of improper usage of the worker’s PPE, raising the alarm in less than seven seconds. Moreover, the post-processing algorithm can be customized to meet the specific needs of different use cases, allowing for a flexible trade-off between the detection time interval and the overall accuracy of the detection system.