Highest Received Signal Strength Research Articles

Graph-Based Machine Learning for Practical Indoor Localization

Machine learning approaches using channel state information (CSI) measurements can achieve accurate indoor localization. In this research, we propose a graph-based indoor localization system, that constructs a graph convolutional network (GCN) based on the geography of access points (APs). An AP pair with a high received signal strength indicator forms a link. Our GCN is efficient in finding relevant CSI measurements received from different APs. In a complex building environment, without any information on the building structure, our scalable system obtains an improved localization accuracy of 1.19 m. To the best of our knowledge, this is the first approach that introduces a graph-based network to find spectral features to improve indoor localization accuracy.

BeamSurfer: Minimalist Beam Management of Mobile mm-Wave Devices

Management of narrow directional beams is critical for mm-wave communication systems. Translational or rotational motion of the user can cause misalignment of transmit and receive beams with the base station losing track of the mobile. Reacquiring the user can take about one second in 5G New Radio systems and significantly impair performance of applications, besides being energy intensive. It is therefore important to manage beams to continually maintain high received signal strength and prevent outages. It is also important to be able to recover from sudden but transient blockage caused by a hand or face interposed in the Line-of-Sight (LoS) path. This work presents a beam management protocol called BeamSurfer that is targeted to the use case of users walking indoors near a base station. It is designed to be minimalistic, employing only in-band information, and not requiring knowledge such as location or orientation of the mobile device or any additional sensors. Evaluations under pedestrian mobility show that 96% of the time it employs beams that are within 3 dB of what an omniscient Oracle would have chosen as the pair of optimal transmit-receive LoS beams. It also recovers from transient LoS blockage by continuing to maintain control packet communication over a pre-selected reflected path that preserves time-synchronization with the base station, which allows it to rapidly recover to the LoS link as soon as it is unblocked.

Study on received signal strength of femtocell with circular and rectangular microstrip patch antenna designed at 2.55 GHz

In large cities, the coverage and quality of telecommunication services inside buildings is low and needs more effort for its improvement. Signal attenuators like number of walls, number of floors and others affect the quality of received signal. Therefore, femtocells are used for improving the poor performance of network inside buildings and other dead zone areas. The aim of this work is to compare the received signal strength of femtocell with rectangular and circular microstrip patch antenna designed at 2.55 GHz. This work considers the received signal strength of femtocell with the designed rectangular and circular microstrip patch antennas. The performance analysis uses Multi Wall Multi Floor indoor propagation model. The result showed that the total gain of rectangular and circular microstrip patch antenna are 3.6528 dB and 2.924 dB, respectively. The received signal strength of femtocell with rectangular microstrip patch antenna is larger than that of circular microstrip patch antenna designed at the same frequency. The effect of number of walls and floors on the received signal strength of femtocell is also clearly indicated. Generally, it is found that much higher received signal strength is observed in rectangular microstrip patch antenna than in circular one. The outcome of this work shows that femtocells are capable of enhancing signal quality for indoor users.

A Novel Efficient Heuristic Based Localization Paradigm in Wireless Sensor Network

Wireless sensor network (WSN) is applicable in all IoT applications, thus it has many advancements. However, it has many drawbacks like localization, link failure, and so on. In addition, the reduction of received signal strength (RSS) often causes path loss, while transferring the data when the path is lost then it drops the packets. To address this problem, the current research aimed to develop a novel grey wolf ant lion recurrent (GWALR) localization model in WSN to find the location of each unknown node. Moreover, the fitness function of GWALR is utilized to track the location of each node. The key focus of this proposed model is to find the location of unknown nodes and to improve the RSS by reducing the localization error. In addition, the model that attained high RSS measure has better data broadcasting rate. Finally, the performance of the proposed approach is compared with existing works and attained better accuracy and reduced error rate. Thus the outcome of the proposed model proved the efficiency of the proposed work by gaining maximum throughput ratio as 7000bps, data broadcasting rate as 99%, accuracy 99.8% and reduced error rate as 1.4%.

CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

Wi-Fi RSS-Based Approach for Locating the Position of Indoor Wi-Fi Access Point

This paper presents an approach to automatically detect the position of the Wi-Fi access points. It uses Wi-Fi received signal strength as well as some characteristics of the buildings such as the height of the building and the movement direction of the user to detect the position of the access points. This approach comprised of two phases: in phase one, a dynamic threshold is computed for each detected access point using the highest received signal strength. Then the threshold is used to detect a small area surrounding the access point. In phase two, it detects the position of the access point by monitoring the angle between the user and the access point, if the angle is in a certain range, then the position of the access point is detected. The experiments results show a high accuracy achieved by the proposed approach. Moreover, the results show that the proposed approach is promising.

A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment.

Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.

Improving Relay Selection Scheme for Connecting VANET to Internet over IEEE 802.11p

Vehicular ad hoc networks (VANETs) enable vehicles to communicate with each other (V2V) as well as with roadside infrastructure units (V2I). These units provide different services such as driver information systems and Internet access. When one node will connect to the Internet, first it must discovery the route to these units. Based only on mobility parameters, we can select longest life time routes to routing packet, but these routes may be with low quality of signal. So, a dropping packet and enforcing retransmission will be certainly happened. For that reason, we propose, in this paper, an efficient routing protocol that improve relay selection scheme by considering link life time and quality of signal as parameters of relay selection, in order to make vehicles able to select longest life time and best quality of signal route to the roadside units. Our relay selection scheme consists to select next forwarding node based on predicted life time of links, received signal strength indicator (RSSI) and signal to interference-noise rate (SINR). The objective is to be sure that data packets will be routed on longest life time, highest received signal strength and strongest routes. The simulation results show that the proposed protocol enhances throughput and decreases the packet loss and overhead with comparable end to end delay.

Indoor Mobile Localization Based on Wi-Fi Fingerprint's Important Access Point

With the development of the wireless communication technology and intelligent mobile phone, the positioning services based on Wi-Fi and mobile phone are increasingly demanded. In this paper, a Wi-Fi fingerprint localization method is proposed on the basis of important access points (IAP). For the Wi-Fi fingerprint, Wi-Fi access point with the highest received signal strength (RSS) is denoted as the important access point. At the localization stage, the fingerprints are chosen with the same IAP as the estimated fingerprint from the database. Then, the distance and the AP repetition of the fingerprints are used to calculate the similarity degree. The location of the fingerprint which matches the estimated fingerprint well can be regarded as the estimated location. Experimental results show that the proposed algorithm can achieve high accuracy in indoor environment.

Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

The wide spread of the 802.11-based wireless technology brings about a good opportunity for the indoor positioning system. In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR), which consists of offline training stage and online location stage. The model that describes the relations between the position and the received signal strength (RSS) of the mobile device is established at the offline training stage by SVR, and at the online location stage the exact position is determined by this model. Due to the complex indoor environment, RSS is vulnerable and changeable. To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction model. At the online location stage,k-times continuous measurement is utilized to obtain the high quality RSS input, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation shows that the proposed method has a higher positioning accuracy compared with the probability and neutral network method, and the demand for the storage capacity and computing power is also low at the same time.

Occurrence-Based Fingerprint Clustering for Fast Pattern-Matching Location Determination

Fingerprint clustering is an efficient approach to address the scalability problem of the pattern-matching localization system in large-scale environments. In this letter, we propose an occurrence-based fingerprint clustering algorithm that exploits the useful information embedded in the statistics of received signal strength measurements. Our algorithm allows that a reference point can have more than one fingerprint according to the highest received signal strength, and can be grouped into several clusters according to its highest and second highest received signal strengths. Simulation and experimental results show that our algorithm performs consistently a little better than the peer one without clustering in terms of improved localization accuracy and reduced fingerprint comparisons. Compared with two other clustering algorithms, our algorithm also achieves higher localization accuracy and reduces false cluster selection.

IEEE 802.11 무선랜 환경에서의 스니핑 기반 전송률 측정 기법(VirtFrame)에 관한 연구

IEEE 802.11 wireless LAN has become the center of attention for one of the most dominant wireless networking technologies nowadays. In densely deployed wireless LANs, mobile stations are exposed to a number of AP, thus it is needed to select the best AP to associate with. The most common approach is to select the AP with the highest received signal strength. However it does not consider traffic load imposed to each AP so that it may cause the poor network performance. Therefore, in this paper, we propose a throughput estimation scheme for neighboring APs by sniffing the traffic within 802.11 networks. We devise a tool, named VirtFrame, which is to estimate the station`s capable throughput from neighbor APs based on the channel access time by virtually combining the sniffed frames. Simulation results show that our proposed scheme well matches that there exists correlation between the channel access time and the actual throughput of the APs.

실내 환경에서 래터레이션을 이용한 위치 측위 기법

래터레이션 기법을 사용한 실내 위치 측위 기법은 단말기의 위치 예측을 위하여 RSS(Received Signal Strength)를 기반으로 예측된 단말기와 AP(Anchor Point)사이의 거리와 AP의 위치 정보를 사용한다. 따라서 단말기와 AP사이의 거리 예측 정확도가 단말기 위치 예측 정확도에 많은 영향을 미치게 된다. 무선 전파 환경은 시간과 공간에 따라 변화하므로 가장 높은 RSS 측정 값이 거리 예측에 가장 적합한 RSS 값은 아닐 수 있다. 이러한 문제를 해결하기 위하여 다수의 AP들이 사용되었으나 일정 수준 이상의 AP의 사용은 오히려 위치 측위 대상 면적이 증가하게 되어 단말기 위치 예측 정확도를 악화시킨다. 따라서 본 논문에서는 위치 측위 대상 면적을 감소시킴으로써 위치 예측 정확도를 향상시키기 위한 기법을 제안한다. 이를 위하여 본 논문에서는 단말기의 신호를 수신한 AP들로 구성된 다각형의 무게중심을 사용하여 단말기의 상대적인 위치를 예측하였다. 일단 단말기의 상대적인 위치가 예측되면 단말기에서 지리적으로 가장 근접한 AP를 단말기 위치 예측을 위한 기준 AP로 선택함으로써 측위 대상 다각형의 면적을 줄일 수 있다. 본 논문에서 구현한 실내 측위 시스템을 이용한 다양한 환경에서의 실험을 통하여 위치 예측 정확도 측면에서의 제안 기법의 타당성을 검증하였다. In an indoor localization method taking the lateration-based approach, the location of a target is estimated with the location of anchor points (APs) and the approximated distances between the target and APs using received signal strength (RSS) measurements. The accuracy of distance estimation affects the localization accuracy of a lateration-based method. Since a radio propagation environment varies randomly in time and space, the highest RSSs do not necessarily give the best estimation of the distances between a target and APs. Thus, all APs hearing a target have been used for localization. However, the accuracy of a lateration-based method degrades if more APs beyond a certain threshold are used because the area of polygon with the APs increases. In this paper, we focus on reducing the size of the polygon to further increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon. Once the relative location is estimated, only the APs which are closest to the target are used for localization to reduce the area of the polygon with the APs. We validate the proposed method by implementing an indoor localization system and evaluating the accuracy of the proposed method in the various experimental environments.

Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems

Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of applications. Nevertheless high resolution RSS based location awareness requires tedious sensor data collection and training of classifier which lengthens location system development life cycle. We present a rapid development approach based on online and incremental learning method which significantly reduces development time while providing competitive accuracy in comparison with other methods. ConSelFAM (Context-aware, Self-scaling Fuzzy ArtMap) extends the Fuzzy ArtMap neural network system. It enables on the fly expansion and reconstruction of location systems which is not possible in previous systems.