An efficient indoor localization system based on Affinity Propagation and Support Vector Regression

Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression (SVR) based Wi-Fi positioning. By signal perturbation, more RSS training samples are generated, thus enhancing the generalization ability of SVR. In addition, access point (AP) selection method is applied to reduce the input dimension by discarding the redundant APs. The proposed method is compared with previous classical methods in a real wireless indoor environment. Experimental results show that the proposed method improves accuracy while reducing calibration cost.

The increasing demand of indoor Location Based Services (LBS) has made Indoor Positioning System (IPS) a hot research topic. Recently, various machine learning techniques have been developed to tackle the problems of IPS. The WLAN fingerprinting technique is one of the most cost effective choices for IPS due to the use of existing Wi-Fi networks in commercial and public buildings. However, the variation of Received Signal Strength (RSS) at any specific location due to the indoor fading issue leads to serious challenges in the accuracy of the distance estimates. In this paper, we propose a new technique by using Probabilistic Neural Network (PNN) multi-classifier based on Multiple Service Set Identifiers (MSSIDs) that are configured on the same access point. A spatial voting scenario for three PNN-classifiers is proposed as a tool to determine the location of the user. Using a spatial voting based technique is specifically designed to tackle the negative impact of the multi-path propagation on the performance of IPS. The proposed system is performed inside the College of Engineering and Applied Sciences (CEAS) at Western Michigan University. The performance of the proposed system compared with some of the common methods such as K-Nearest Neighbors (K-NN) and multi-class support vector machine (SVM). According to the experimental results, the spatial voting of the three PNN-dassifiers can significantly mitigate the adverse effects of RSS variation. The performance of the multi-classifier exhibits superior performance to that of the other single classifiers in all Area of Interest (AOI). Over 3000 m2 AOI, the proposed system achieves localization accuracy < 0.73m and with precision 90 % of the distance error less than 2m.

In wireless local area networks (WLANs) made up of Extended Service Sets, access point (AP) selection is a key issue to improve the network performance and balance the traffic load. WLANs operating in the shared Industrial, Scientific and Medical band can benefit from the use of cognitive radio (CR) techniques to enable dynamic access to spectrum holes that are free from interference. In this paper, we propose an optimal Green AP Selection (GAPS) scheme, in which AP selection is optimized to maximize the system throughput while minimizing the energy consumption, for multi-rate WLANs enhanced by CRs. Different from most existing AP selection schemes, GAPS takes into account of the state transition tendency of APs and the influence of Automatic Rate Fallback mechanism in the Distributed Coordination Function. The AP selection problem is formulated as a restless bandit problem and solved by the primal-dual index heuristic algorithm based on first order relaxation to yield the GAPS scheme with the "indexability" property and hence a low complexity. GAPS is further divided into offline computation, which accounts for the bulk of the computations, and online selection, with a low complexity to facilitate implementation. Extensive simulation results illustrate the significant performance improvements of GAPS compared with existing AP selection schemes in different scenarios.

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy &lt;0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

In view of the characteristics of intermittence and fluctuation of wind power output and the trend of large scale grid connection of wind power and power grid to assume the function of renewable energy consumption, this paper proposes a multi-objective coordinated planning method based on AP (Affinity Propagation) clustering algorithm combined with improved MPGA (Multi-Population Genetic Algorithm) for power grid considering wind power integration. Firstly, the AP clustering algorithm is used to cluster the typical wind farm operation scenarios. Then, the power grid planning optimization model considering wind power consumption and line load balancing rate is established. Finally, the feasibility and validity of the proposed method are verified in the IEEE 18-bus system.

The ensemble of conceivable molecules is referred to as the Chemical Space. In this article we describe a hierarchical version of the Affinity Propagation (AP) clustering algorithm and apply it to analyze the LINGO-based similarity matrix of a 500 000-molecule subset of the PubChem database, which contains more than 19 million compounds. The combination of two highly efficient methods, namely the AP clustering algorithm and LINGO-based molecular similarity calculations, allows the unbiased analysis of large databases. Hierarchical clustering generates a numerical diagonalization of the similarity matrix. The target-independent, intrinsic structure of the database , derived without any previous information on the physical or biological properties of the compounds, maps together molecules experimentally shown to bind the same biological target or to have similar physical properties.

WLAN is now widely used for emergency purposes because of its fast deployment capability. Efficient and intelligent arrangement and selection of access points (AP) are very crucial in emergency situations. Coverage and capacity are two central problems faced when APs are placed in a disaster scene. The paper proposes a power efficient algorithm for AP selection in emergency situations. The current AP selection algorithm only considers received signal strength, and this algorithm arise problems like overloading in APs, congestion and power wastage in networks. But the proposed Dynamic Load Balancing Fairness Algorithm (DLBFA) overcomes all these problems by using a new selection score, obtained by optimizing channel throughput and voice quality. Apart of this score the number of nodes connected to an AP and RSSI are also considered. The main advantage of DLBA is that it can work on both data and voice applications.

It is usually possible to associate with more than one Access Point (AP) in IEEE 802.11 Wireless LANs. AP selection is a crucial issue because the performance achieved by a user heavily depends on the AP selected. A received signal strength (RSS)-based association mechanism is specified by the IEEE 802.11 standard. However, this does not consider the channel conditions and AP load, which leads to a low throughput and a low user transmission rate. An alternative to using the RSS is to exploit the airtime metric, which provides users with information on how busy the channel is, but the results of our simulations show that the airtime metric cannot indicate the exact status of the channel. In this paper, we present a new association framework using channel occupancy in order to provide users with exact channel status information of APs. Via the proposed scheme, users can compare the achievable throughput provided by each AP and select the best AP. We validate our proposed association method via simulation results, which show that the throughput improvement of the method is worthy of notice.

Background: Cell Free massive MIMO, containing a very large number of distributed access points (APs), which is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell-free M-MIMO is maximum when selecting optimal access points (AP) from the large number of APs. The linear precoding methods of zeroforcing (ZF) and minimum mean square error (MMSE) are utilized in this study because they are devoid of self-interference and so improve the system capacity. Objective: The objective of this study is to maximize the system data rate in a cell-free M-MIMO network. Method: To maximize the system data rate, the maximum channel gain-based Access Point Selection (MCGAPS), Distance based Access Point Selection (DAPS), and Random-Access Point Selection (RAPS) algorithms are used to pick access points (APs) in a cell-free M-MIMO network. Because the MCGAPS algorithm selects those APs with the highest channel gain, the system’s rate is improved. Result &amp; Discussion: The DAPS algorithm is used to choose the closest APs to the user. The APs were randomly chosen using RAPS. Random user selection (RUS) algorithm schedules the same number of users. Conclusion: It is observed that the DAPS and RUS algorithms jointly improve the system rate significantly in cell-free massive MIMO system compared to the other proposed algorithms.

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

Indoor localization systems have already wide applications for providing localized information and directions. They can also be very useful in medical applications, as an individual’s indoor movements can be indicative of his/her clinical status. Research for indoor localization systems is ongoing and there are numerous solutions with different hardware or algorithmic approaches, for different use cases. Ease of setup and cost are significant factors when using indoor localization systems for medical applications. This paper proposes an indoor localization system with room-level accuracy, focusing on an easy installation procedure that can be easily followed from non-technical staff, as well as on low cost, so that it can be widely adopted. The proposed system has been mainly designed for behavioural monitoring of adults in an indoor environment. The system operates by tracking a carried device based on the processing of RSS (Received Signal Strength) measurements from Bluetooth Low Energy beacons placed in the area. The proposed system has been evaluated in realistic settings and and achieves an accuracy of 95.31% in room estimation.

Device-free passive (DFP) localization systems are a key solution for location-based services because they do not require any wireless device on a human body. Most of the existing DFP localization systems are based on the received signal strength (RSS) measurement only. However, the localization accuracy of RSS only-based systems is easily affected by the spatial and temporal variances of RSS due to multipath fading and noise, even in a static environment. In this paper, we propose a novel localization system for DFP using signal subspace eigenvectors from an antenna array. We present a fingerprinting technique using multiclass support vector machines (SVMs) based on a combination of array signal features with spatial and temporal averaging. We then evaluate the localization accuracy of our proposed system in different propagation environments: line-of-sight (LOS) and non-line-of-sight (NLOS). In addition, we analyze two types of receive antenna placement: centralized and distributed antennas. The experimental results show that the localization accuracy can be improved by the proposed system, particularly in the centralized antenna case. Moreover, they show that the proposed system can improve localization accuracy compared to the conventional RSS-only based system.

PurposeWireless local area networks (WLANs) are the predominant option for broadband wireless access network, and multiple access points (APs) will be much more available for wireless stations (WSTAs). Call admission control (CAC) on AP selection problem over 802.11 WLAN is a critical issue. In the existing architecture, strongest‐signal‐first is the default AP selection mechanism in 802.11 WLAN which uses the single criterion, received signal strength indicator, to select AP. However, this method suffers from bandwidth deficiency and unbalanced load among APs due to the uneven distribution of user load, thus degrading the system throughput. Instead, the purpose of this paper is to propose a multi‐criteria CAC on AP selection algorithms.Design/methodology/approachThe distributed multi‐criteria considered in order are RSSI, minimum required bandwidth of WSTA, estimated effective bandwidth (EEB) and AP‐WSTA distance. A semiMarkov model considering both packet retransmission limit, packet error rate and collision effect is proposed to predict the system throughput and validated through simulation results. Two multi‐criteria AP selection algorithms after EEB is evaluated are proposed and compared based on this analytical model.FindingsThe proposed algorithms outperform the traditional SSF algorithm in terms of the balance index for AP and the average system throughput.Originality/valveThe paper presents performance analysis for multi‐criteria CAC for distributed access point selection in WLANs.