Proceedings Article•
Improving RSS-Based Indoor Positioning Algorithm via K-Means Clustering
27 Apr 2011-pp 1-5
TL;DR: This paper aims to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach, and indicates that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm.
Abstract: Advances in mobile technologies and devices has changed the way users interact with devices and other users. These new interaction methods and services are offered by the help of intelligent sensing capabilities, using context, location and motion sensors. However, indoor location sensing is mostly achieved by utilizing radio signal (Wi-Fi, Bluetooth, GSM etc.) and nearest neighbor identification. The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs). Accordingly, in this paper, we aim to improve the KNN algorithm by enhancing the neighboring point selection by applying k-means clustering approach. In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user. Then the closest group to the mobile user is used to calculate the MU's location. The evaluation results indicate that the performance of clustered KNN is closely tied to the number of clusters, number of neighbors to be clustered and the initiation of the center points in k-mean algorithm.
Citations
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TL;DR: An overview of indoor fingerprint positioning based on Wi-Fi is provided and it is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.
Abstract: The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.
182 citations
Cites methods from "Improving RSS-Based Indoor Position..."
...This paper focuses on the fingerprint positioning technology based on Wi-Fi, as shown in Figure 1 (which is based on the Figure 1 in reference [12])....
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116 citations
TL;DR: The recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations as well as the traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), andreference signal received quality (RSRQ) are presented.
Abstract: The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.
75 citations
Cites methods from "Improving RSS-Based Indoor Position..."
...In [229], K-means-based approach was used to improve the performance of a distance estimation KNN which determines the close distance values of a mobile user’s nearest location....
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01 Aug 2017
TL;DR: The localization mechanism of the proposed system is based on a simple location algorithm stemming from the Received Signal Strength (RSS) footprinting method, which allow us to detect reference zones inside closed environments.
Abstract: This paper presents the design and implementation of an Internet of Thing (IoT)-based system for indoor localization using Bluetooth Low Energy (BLE) technology. Our solution consists of two main systems: an acquisition system and a central server, under the Client-Server paradigm and the IoT philosophy. We report the development of different modules: measurement (Bluetooth beacons), data aggregation and transmission, storage, web-interface and cloud services for data, and results visualization. The localization mechanism of the proposed system is based on a simple location algorithm stemming from the Received Signal Strength (RSS) footprinting method, which allow us to detect reference zones inside closed environments. We performed real experiments in order to assess the proposed system. This paper reports the design, implementation and evaluation of an IoT-based system for indoor location using Bluetooth Low Energy (BLE) technology.
73 citations
Cites methods from "Improving RSS-Based Indoor Position..."
...Two different RSSI classification algorithms [16] were implemented to estimate the position using the instantaneous RSS obtained during the real-time stage: • k-Nearest Neighbours (k-NN) classifier, with centroid locations in the median values of the training data, using euclidean distance metrics....
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18 Jul 2016
TL;DR: An Iterative Weighted KNN (IW-KNN) indoor localization method based on RSSI of the BLE (Bluetooth Low Energy), which has a low power consumption, a long life expectancy and a weighting factor is applied to neighbors for localization.
Abstract: With the dense deployment of low power node such as Internet of Things (IoT), indoor localization is growing rapidly, has gained a lot of interests in commercial applications. This paper proposes an Iterative Weighted KNN (IW-KNN) indoor localization method based on RSSI (Receive Signal Strength indicator) of the BLE (Bluetooth Low Energy), which has a low power consumption, a long life expectancy. An enormous amount of RSSI are collected to build a fingerprints database. IW-KNN has three principal improvement approaches. Firstly, Euclidean distance, Cosine similarity are combined to measure the similarity of two RSSI vectors, which can take both length, direction of vector into consideration. Secondly, unlike traditional KNN (K Nearest Neighbors) which estimates a position by a majority vote of its neighbors, a weighting factor is applied to neighbors for localization. Thirdly, IW-KNN selects different iBeacons to obtain RSSI at each iteration, calculates the mean position as the final result after several iterations. Compared with several indoor localization methods, namely, traditional KNN, Similarity improved KNN, Weighted KNN, IW-KNN can estimate positions effectively, decrease the mean error by 1.5 to 2.7 meters in our experimental environment.
57 citations
References
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26 Mar 2000
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Abstract: The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
8,667 citations
"Improving RSS-Based Indoor Position..." refers methods in this paper
...In the proposed method, k-means clustering algorithm groups nearest neighbors according to their distance to mobile user....
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TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
5,744 citations
"Improving RSS-Based Indoor Position..." refers methods in this paper
...The algorithm is composed of the following steps in Table 1 [16]....
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TL;DR: A novel system for the location of people in an office environment is described, where members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors.
Abstract: A novel system for the location of people in an office environment is described. Members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors. The paper also examines alternative location techniques, system design issues and applications, particularly relating to telephone call routing. Location systems raise concerns about the privacy of an individual and these issues are also addressed.
4,315 citations
"Improving RSS-Based Indoor Position..." refers methods in this paper
...The most common algorithm adopted for Received Signal Strength (RSS)-based location sensing is K Nearest Neighbor (KNN), which calculates K nearest neighboring points to mobile users (MUs)....
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TL;DR: This paper presents LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings and demonstrates that active RFID is a viable and cost-effective candidate for indoor location sensing.
Abstract: Growing convergence among mobile computing devices and embedded technology sparks the development and deployment of "context-aware" applications, where location is the most essential context. In this paper we present LANDMARC, a location sensing prototype system that uses Radio Frequency Identification (RFID) technology for locating objects inside buildings. The major advantage of LANDMARC is that it improves the overall accuracy of locating objects by utilizing the concept of reference tags. Based on experimental analysis, we demonstrate that active RFID is a viable and cost-effective candidate for indoor location sensing. Although RFID is not designed for indoor location sensing, we point out three major features that should be added to make RFID technologies competitive in this new and growing market.
2,615 citations
TL;DR: The state-of-the-art positioning designs are surveyed, focusing specifically on signal processing techniques in network-aided positioning, to provide new directions for future research.
Abstract: Wireless positioning has attracted much research attention and has become increasingly important in recent years. Wireless positioning has been found very useful for other applications besides E911 service, ranging from vehicle navigation and network optimization to resource management and automated billing. Although many positioning devices and services are currently available, it is necessary to develop an integrated and seamless positioning platform to provide a uniform solution for different network configurations. This article surveys the state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years.
604 citations