Received Signal Strength Indicator Values Research Articles

Prototype Gelang Tangan Pendeteksi Jarak Physical Distancing Man To Man

This research is motivated by the phenomenon of the Covid-19 pandemic in several countries, one of which is in Indonesia. To avoid Covid-19 infection, the government has implemented several policies such as physical contact restrictions (physical distancing). When implementing physical distancing, a person is not allowed to shake hands and maintain a distance when interacting with other people, especially with people who are sick or at high risk of suffering from COVID -19. However, on the other hand, the physical distancing policy that has been implemented has not been maximized. This happens due to several factors. Among them is the existence of a misunderstanding in society that causes negligent nature in implementing physical distancing efforts. This journal focuses on the use of technology to calculate physical distance distances automatically using the calculation of the received signal strength indicator (RSSI) by utilizing Bluetooth low energy (BLE) as the communication medium. RSSI is a measurement of the power received by a wireless device. The basic principle of this system is the existence of connectivity between wristbands using Bluetooth communication media so that the system can calculate physical distance distances automatically using the RSSI value.

Prototype Gelang Tangan Pendeteksi Jarak Physical Distancing Man To Man

This research is motivated by the phenomenon of the Covid-19 pandemic in several countries, one of which is in Indonesia. To avoid Covid-19 infection, the government has implemented several policies such as physical contact restrictions (physical distancing). When implementing physical distancing, a person is not allowed to shake hands and maintain a distance when interacting with other people, especially with people who are sick or at high risk of suffering from COVID -19. However, on the other hand, the physical distancing policy that has been implemented has not been maximized. This happens due to several factors. Among them is the existence of a misunderstanding in society that causes negligent nature in implementing physical distancing efforts. This journal focuses on the use of technology to calculate physical distance distances automatically using the calculation of the received signal strength indicator (RSSI) by utilizing Bluetooth low energy (BLE) as the communication medium. RSSI is a measurement of the power received by a wireless device. The basic principle of this system is the existence of connectivity between wristbands using Bluetooth communication media so that the system can calculate physical distance distances automatically using the RSSI value.

Towards a fast and stable filter for RSSI-based handoff algorithms in dense indoor WLANs

In dense indoor wireless networks, handoffs occur frequently. The criteria to trigger handoffs are not defined by the IEEE 802.11 standard, thus being specific to each manufacturer’s implementation. Current handoff implementations typically use RSSI (Received Signal Strength Indicator) as a performance metric and commonly cause association instability in dense environments, a well-known problem referred to as the ping-pong effect. In this paper, we present a deep analysis of RSSI traces collected in dense indoor environments using the FIBRE testbed. Based on that, we conclude that the RSSI time series presents deep fast fades that occur frequently in bursts of small sizes, which can cause ping-pongs. Motivated by this behavior, we propose a new and simple filtering mechanism called Maximum which targets to eliminate these valleys in the RSSI time series. In a nutshell, this filter chooses the maximum RSSI value from a sliding window containing the last few RSSI samples of the series. We conduct simulations based on real RSSI traces from static and mobile scenarios to evaluate Maximum with respect to other filtering mechanisms found in the literature. Additionally, we present a simplified model of the behavior of Maximum that allows us to study the probability of unwanted handoffs as a function of the RSSI of the available access points. Our analysis reveals that Maximum is able to offer a better tradeoff between handoff triggering delay and stability in mobile scenarios, while also performing well in static scenarios, effectively avoiding the occurrence of ping-pongs in most cases.

A Method for Locating Tools in the Railway Moving Area Optimized Based on Received Signal Strength Indicator and a Fuzzy Neural Network

To solve the problem that maintenance tools are difficult to retrieve once being lost in maintenance of an electric section of the railway system, this study proposed a new method for estimating the object location optimized by a fuzzy neural network based on received signal strength indicator. Through a radio frequency identification reader and antennae, the received signal strength indicator values feed back by the dropped tools were collected. The environmental attenuation factor was fitted with field data. In the pre-processing stage, K-means algorithm optimized by the genetic algorithm, thus improving accuracy of data. The processed data were trained and tested by the fuzzy neural network, so that they could more accurately estimate the actual location of dropped tools. Data were collected on the railway lines of National Local Joint Engineering Research Center of Safety Guarantee Technology for Operation and Maintenance of Rail Transport Infrastructure. The collected data were used for subsequent processing and locating tools. The experimental results show that the proposed location method offers advantages in improving the location accuracy of dropped tools, which can improve the retrieval efficiency of tools in the field.

WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model.

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

Location detection of the mobile sensor with fingerprinting‐based cascade artificial neural network model using received signal strength indicator in 3D indoor environment

SummaryThe problem of localization of mobile sensor nodes has been extensively studied in the literature in recent years. In any localization technique, the aim is to determine the location of the sensor node of unknown location with low error. In this article, a cascading artificial neural network (ANN)‐based location detection algorithm is proposed, which detects the location of a mobile sensor node in 3D indoor environment. In a 3D indoor environment of 6 × 20 × 3 m3, received signal strength indicator (RSSI) signals were collected using a mobile node with XBee sensor, and a fingerprint database was created. Cascade ANN system was trained using this database. Then, while a mobile node is in any location, RSSIs measured by anchor nodes are given as an input to the cascade ANN system, and the location of the mobile node is determined. Fingerprint steps 1 and 0.5 m were taken, and two applications were carried out in the article. According to the RSSI values taken from 100 different coordinates for the test, the total error was 3216 and 2838 cm, respectively. The average error is 32.16 and 28.38 cm.

An SDN‐based technique for reducing handoff times in WiFi networks

SummaryThe handoff process in WiFi networks has a very important role to provide continuity of the service to wireless users where the access points (APs) are in numerous numbers. In traditional WiFi networks, the handoff process takes a few seconds and is initiated by the wireless devices, but these few seconds could be a reason for information loss when dealing with delay sensitive applications. In this paper, we propose a reduced detection and discovery time approach (DeRy), which generates a solution based on software defined WiFi networks (SD‐WiFi). A simple network management protocol (SNMP) manager and received signal strength indicator (RSSI) manager are employed to report the AP traffic conditions and RSSI values to the SDN controller and instead of the wireless devices, the handoff decisions are taken at the controller end. When to initiate the handoff (detection phase) and which destination AP to associate to (discovery phase) are handled by the centralized SDN controller. To implement DeRy, extensive simulation runs are carried out on Mininet‐NS3‐WiFi network simulator. The simulation results show that the DeRy significantly reduces the handoff times by 60–70% and reduces the average number of retransmission by 4–49%, hence maintaining the throughput for delay sensitive applications such as VoIP, when compared to the standard RSSI‐based handoff scheme, channel measurement based access selection scheme (CMAS) and (DL‐SINR) downlink‐signal to interference plus noise ratio AP selection scheme (DASA).

Fingerprint Feature Extraction for Indoor Localization.

This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.

Optimization of Indoor Bluetooth Ranging Model Based on RSSI

Because the received signal strength indication (RSSI) ranging technology has problems with line-of-sight and multipath effects in indoor environments, the actual received RSSI value is unstable. In order to reduce the influence of RSSI value volatility on ranging accuracy, according to the fluctuation characteristics of the signal itself, a combined filtering method of Gaussian, median and mean is proposed to process the collected RSSI values, and the least squares method is used to fit and optimize the ranging parameter. Experiments show that using the RSSI intensity value processed by the combined filtering method to establish a model to achieve ranging, the maximum absolute error is about 2 m, and the absolute average error is about 0.763 m. The accuracy of the ranging has been significantly improved, and the ranging model has been optimized.

Multi-barycenter Nodes Localization Method in Wireless Sensor Network Based on Improved RSSI

In order to improve the accuracy of nodes in wireless sensor networks (WSNs). An multi-barycenter localization algorithm based on improved received signal strength indication (RSSI) ranging is proposed. The algorithm first optimizes the RSSI values by the iterative filtering, the distance values of WSNs nodes could be more accurately calculated. And then a multi-barycenter algorithm is introduced to optimize the trilateral localization. The simulation results show that the algorithm can improve the positioning accuracy by 30% compared with the traditional trilateral localization algorithm. Finally, through the localization test of 5 WSN nodes, the measured localization deviation of the WSN nodes is controlled at about 2 m when the distance of sensor node is 80 m. The proposed method could improve localization accuracy effectively without increasing computing resources.

COMPARISON OF WIRELESS ADAPTERS INTERFERENCE BASED ON DIFFERENCES OF FLOOR POSITION

The wireless network adapter has now been widely developed. The wireless adapter receives signal strength has several levels. It depends on the signal strength of the device with respect to the wireless signal and access point. The external adapter is used by computers that are not equipped with a wireless network adapter. Usually, internet users use a USB wireless adapter. An external adapter can also amplify signal reception from hotspots. The problem is how users consider using an external wireless adapter or just an onboard wireless adapter when there are many networks interfering with the weak signal strength of the access point. The method is implemented by measuring the value of the Received Signal Strength Indicator (RSSI). The purpose of this study is to compare the received signal strength in conditions where the signal before interference and after experiencing interference (interference). Meanwhile, the measured RSSI value is divided into four floors and with different distances for each floor. Each floor is measured by five distances. The average value of signal strength at a distance of 10 meters is -74 dBm using the onboard wireless adapter and -69 dBm using a USB wireless adapter. The value obtained after the interference is -75 dBm and the USB wireless adapter gets -61 dBm. The reduction of the RSSI value between interference and non-interference is 3%. The onboard wireless adapter is affected by interference while the external wireless adapter is not affected.

Non-Invasive Monitoring of Respiratory Rate and Respiratory Status during Sleep Using a Passive Radio-Frequency Identification System

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

Combining RSSI and Accelerometer Features for Room-Level Localization.

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.

Estimation of the Path-Loss Exponent by Bayesian Filtering Method.

Regarding wireless sensor network parameter estimation of the propagation model is a most important issue. Variations of the received signal strength indicator (RSSI) parameter are a fundamental problem of a system based on signal strength. In the present paper, we propose an algorithm based on Bayesian filtering techniques for estimating the path-loss exponent of the log-normal shadowing propagation model for outdoor RSSI measurements. Furthermore, in a series of experiments, we will demonstrate the usefulness of the particle filter for estimating the RSSI data. The stability of this algorithm and the differences in determined path-loss exponent for both method were also analysed. The proposed method of dynamic estimation results in significant improvements of the accuracy of RSSI values when compared with the experimental measurements. It should be emphasised that the path-loss exponent mainly depends on the RSSI data. Our results also indicate that increasing the number of inserted particles does not significantly raise the quality of the estimated parameters.

Statistical modeling of received signal strength for an FSO link over maritime environment

Free space optical communications (FSO) have the potential to substantially improve communications technology in terms of channel capacity and offer an alternative to their RF counterpart. Additional characteristics related to security, immunity, flexibility and low cost issues render FSO a reasonable candidate for military applications. FSO technology does not come without challenges. Its major issue is the local meteorological parameters that give rise to various atmospheric phenomena. The purpose of this work is to facilitate the performance prediction of an FSO communication link over a maritime environment by utilizing macroscopic meteorological parameters, i.e. air temperature, wind speed, relative humidity, air pressure, dew point, solar radiation and sea temperature, obtained from point measurements. The received signal strength indicator (RSSI) of the FSO receiver has been utilized as the performance metric of the channel and a closed form expression has been deduced. The model has then been validated against real meteorological data and the predicted RSSI values exhibited a reasonably strong correlation with the observed ones. Atmospheric turbulence has been taken into account using the Navy Surface Layer Model (NAVSLaM) to estimate the structure index parameter from the same meteorological data and thus allowed for a statistical correlation between the refractive index structure parameter and RSSI.

Hybrid Machine Learning Classifiers for Indoor User Localization Problem

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value.

Coordinated sensing coverage optimisation in sensor networks using RSSI

We present a coordinated algorithm for sensors' effective deployment, which aims to optimise coverage and surveillance in distributed sensor networks. In this paper, the easy accessed received signal strength indication (RSSI) is adopted to form virtual force, the virtual force algorithm for coverage optimisation will encounter oscillation problems when it has nearly reached an optimised position. We propose a new method which combines virtual force algorithm and the whale swarm algorithm (WSA) according to the RSSI value. When the number of continuous oscillations using a virtual force algorithm is greater than the threshold we set in advance, we optimise the cooperative position of adjacent multi-nodes with the whale swarm algorithm. The regular signal transmission model and radio irregularity model (RIM) are used to simulate the ideal environment and the actual environment, and simulation results demonstrate that the coverage ratio is above 95%, which verifies the effectiveness and feasibility of this algorithm.

CollabLoc: Privacy-Preserving Multi-Modal Collaborative Mobile Phone Localization

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

Deployment and analysis of Bluetooth low energy network

Along with the development of modern communication technologies such as 4G and 5G, Internet of Thing (IoT) technologies are being implemented more widely. The need for remote monitoring of environmental and patient parameters can be realised by means that represent IoT technologies. The localisation of mobile sensor nodes for low power IoT can be realised by tracking the Received Signal Strength Indicator (RSSI) values. This paper examines the RSSI values of sensor nodes in the Bluetooth Low Energy network for IoT.