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Server-based indoor location detection system

Perente, O.K. | Serif, T.

Conference Object | 2018 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10995 LNCS , pp.142 - 153

With the advancement of technology and telecommunication services, data consumption rates are increasing ever since. People for long have started using applications with the help of contextual information to improve their user experience. Thus, providing a cross-platform location service to further enrich such applications has become a necessity. For this purpose, numerous client-based indoor location systems on mobile devices are developed to perform this task. Nevertheless, most of the time these systems suffer from elimination of features from operating systems for security purposes. Indeed, with the current security trends, to e . . .nsure the privacy of mobile users, mobile operating system designers are progressively eliminating certain low-level features such as reading RSSI and introducing randomized MAC addresses. Thus, in this study, the authors propose, design and implement a server-based indoor positioning system to eliminate platform dependency and to provide the location detection in wide range of devices. The designed server-based system is scalable and platform independent; hence can run on virtually any family of smart device. Furthermore, the evaluation findings indicate that the proposed system performs in acceptable accuracy to client-based systems compared to more complex and costly implementations. © Springer International Publishing AG, part of Springer Nature 2018 Daha fazlası Daha az

Indoor location detection with a RSS-based short term memory technique (KNN-STM)

Altintas, B. | Serif, T.

Conference Object | 2012 | 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2012 , pp.794 - 798

The interaction between devices and users has changed dramatically with the advances in mobile technologies. User friendly devices and services are offered by utilizing smart sensing capabilities and using context, location and motion sensor data. However, indoor location sensing is mostly achieved by measuring radio signal (WiFi, Bluetooth, GSM etc.) strength and nearest neighbor identification. The algorithm that is most commonly used for Received Signal Strength (RSS) based location detection is the K Nearest Neighbor (KNN). KNN algorithm identifies an estimate location using the K nearest neighboring points. Accordingly, in this . . . paper, we aim to improve the KNN algorithm by integrating a short term memory (STM) where past signal strength readings are stored. Considering the limited movement capabilities of a mobile user in an indoor environment, user's previous locations can be taken into consideration to derive his/her current position. Hence, in the proposed approach, the signal strength readings are refined with the historical data prior to comparison with the environment's radio map. Our evaluation results indicate that the performance of enhanced KNN outperforms KNN algorithm. © 2012 IEEE Daha fazlası Daha az

Improving RSS-based indoor positioning algorithm via K-means clustering

Altintas, B. | Serif, T.

Conference Object | 2011 | 17th European Wireless Conference 2011, EW 2011 , pp.681 - 685

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. © VDE VERLAG GMBH Daha fazlası Daha az

RoboMapper: An automated signal mapping robot for RSSI fingerprinting

Serif, T. | Perente, O.K. | Dalan, Y.

Conference Object | 2019 | Proceedings - 2019 International Conference on Future Internet of Things and Cloud, FiCloud 2019 , pp.364 - 370

With the ever-increasing numbers of mobile devices, location-based services became a crucial part of mobile development. Many indoor location detection systems are developed to solve positioning problem where satellite-based solutions prone to failure. Among many proposed solutions, fingerprinting technique proved to be the most reliable approach for indoor location. However, it comes with a cost; it entails a time-consuming learning phase which should be repeated many times during the system's life time to preserve system accuracy. Thus, we propose an automated signal mapping robot called RoboMapper to alleviate time-consuming natu . . .re of the learning phase of fingerprinting technique. With the help of its accurate distance keeping mechanisms, RoboMapper can construct the signal map of the environment so that the created map can be used for user positioning. Our findings indicate that using RoboMapper 2.68-meter positioning accuracy with 70% probability can be achieved. © 2019 IEEE Daha fazlası Daha az

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