Wi-Fi Location Determination for Semantic Locations

In Wi-Fi location determination literature, little attention is paid to locations that do not have numeric, geometric coordinates, though many users prefer the convenience of non-coordinate locations (consider the ease of giving a street address as opposed to giving latitude and longitude). It is not often easy to tell from the title or abstract of a Wi-Fi location determination article whether or not it has applicability to semantic locations such as room-level names. This article surveys the literature through 2011 on Wi-Fi localization for symbolic locations

SENSOR FUSION AND TEMPORAL INTEGRATION FOR TOUCH INTERFACE INDOOR POSITIONING

Dalam kunjungan wisata atau budaya, panduan terhadap objek menarik sangat berguna untuk menambah pengetahuan dan pengalaman pengunjung di lokasi tersebut. Dewasa ini, dengan bantuan teknologi modern, aplikasi bergerak mampu menjadi pemandu wisata mandiri otomatis dengan sistem sadar konteks. Kebanyakan, unsur konteks yang digunakan dalam aplikasi-aplikasi ini adalah posisi dua dimensi (2D). Meskipun begitu, ada beberapa kemungkinan lain agar tiap unsur konteks dari perangkat pintar ini dapat diteliti lebih lanjut. Berkat sensor dari ponsel pintar, konteks-konteks tersebut, yang terdiri dari konteks dalam 3 dimensi (3D) dari posisi dan orientasi (dalam sumbu X, Y, dan Z), dapat ditangkap oleh ponsel pintar. Dimensi-dimensi ini akan diteliti untuk mendapatkan kemungkinan keberhasilan digunakannya ponsel pintar yang digenggam sebagai pointer terhadap objek menarik. Hal ini dilakukan karena posisi 2D tidak bisa menangani konteks ketinggian. Sehingga, pengalaman pengguna dapat ditingkatkan karena mereka tidak terhalang secara visual dan audio. Tetapi, sensor-sensor ini memiliki galat pengukuran yang tinggi. Sehingga, suatu penggabungan sensor diterapkan untuk menangani galat tersebut. Penelitian ini menerapkan metode untuk memperkirakan orientasi sudut dan posisi dengan berbagai filter, yakni Complementary Filter dan Kalman Filter. Complementary Filter melibatkan gyroscope, magnetometer, dan accelerometer dari sensor inersial ponsel pintar. Sedangkan, Kalman Filter melibatkan accelerometer dan hasil Wi-Fi fingerprinting yang didapatkan dari pengamatan lingkungan. Evaluasi perkiraan-perkiraan hasil penggabungan observasi sensor oleh filter-filter tersebut menggunakan ilustrasi grafis dan evaluasi statistika untuk mengukur kualitas reduksi galat dari tiap filter. Hasil dari performa filter menunjukkan bahwa kualitas perkiraan orientasi oleh Complementary Filter cukup baik untuk menghasilkan sudut yang sesuai. Namun, perkiraan posisi oleh Kalman Filter menunjukkan hasil yang kurang baik akibat integrasi ganda terhadap derau dan pengaruh besar Wi-Fi fingerprinting. Hasil Wi-Fi fingerprinting menunjukkan perkiraan posisi yang tidak akurat. Hal ini menunjukkan bahwa perkiraan posisi tidak dapat digunakan dalam penelitian ini. Sedangkan, dalam percobaan menunjuk objek di laboratorium, perkiraan orientasi sudut memberikan hasl yang cukup baik dengan ponsel pintar. Secara ringkas, perkiraan posisi dan orientasi 3D dengan Complementary Filter dan Kalman Filter dalam ponsel untuk pointer tidak dapat digunakan menurut penelitian ini. Meskipun begitu, masih perlu diteliti mengenai penerapan filter lainnya untuk perkiraan posisi dan observasi lain untuk membantu perkiraan yang baik. Walaupun penggunaan filter dan observasi lain dapat mengorbankan sumber daya dari ponsel pintar. ======================================================================================================== During cultural or tourism visits, a guide of the interesting objects is useful to enhance the knowledge and the experience of the visitors. Nowadays, because of the modern technologies, mobile applications are capable to be a personal autonomous guide in the case of context-aware system. Mostly, the context element used in these applications is the position in two dimension (2D). However, there are more possibilities using the context elements from smartphone that can be explored. Thanks to smartphone sensors, the contexts which can be captured by smartphone are composed in 3 dimensions (3D) of both position and orientation (in X, Y, and Z axes). Those dimensions are used to explore the feasibility of smartphone which can held by hand as pointer to interesting objects, which can’t be handled by 2D position only. Thus, the user experience can be enhanced, as they don’t get vision-blocked or audio-blocked. However, those sensors have erroneous measurements. Hence, a sensor fusion is applied to overcome this drawback. The sensor fusion can be implemented not only using the internal smartphone sensors, but also the external environment. In this case of indoor environment, the Wi-Fi fingerprinting approach, which widely used as indoor positioning algorithm, can be considered as external observation. Even though so, the quality of the fusion should be studied to assure that it is feasible to use smartphone a pointing device in indoor environment. This study proposed a method to estimate orientation and position using different filters, namely Complementary Filter and Kalman Filter respectively. The complementary filter involves the gyroscope, magnetometer, and accelerometer from the smartphone inertial navigation sensors, while the Kalman Filter involves accelerometer and the Wi-Fi fingerprinting result which come from environmental measurement. To evaluate these estimations, the graphical representation and statistical evaluation are used to measure the filters’ quality in reducing the errors. The results of the filters’ performance showed that orientation estimation was adequate to give acceptable angle. But, unfortunately, position estimation had resulted in poor performance because of the double integration toward noise and the heavy influence from Wi-Fi fingerprinting. The Wi-Fi fingerprinting resulted inaccurate positioning. This concluded that the position estimation cannot be used at all in this study. In laboratory object pointing field experiment, the orientation estimation gave passable estimation to locate an object by a fixed smartphone position. To sum up, the 3D position and orientation estimation using Complementary Filter and Kalman Filter might not be feasible according to this study. However, regarding to 3D position estimation, possibly there are other methods than Kalman Filter which might be used as state estimator. And also, there are various external measurements which might help to achieve better estimation. Although, the drawbacks between the more sophisticated methods and the computation power and capability of smartphone should be considered for a good user experience

Improving fingerprint-based positioning by using IEEE 802.11mc FTM/RTT observables

Received signal strength (RSS) has been one of the most used observables for location purposes due to its availability at almost every wireless device. However, the volatile nature of RSS tends to yield to non-reliable location solutions. IEEE 802.11mc enabled the use of the round trip time (RTT) for positioning, which is expected to be a more consistent observable for location purposes. This approach has been gaining support from several companies such as Google, which introduced that feature in the Android O.S. As a result, RTT estimation is now available in several recent off-the-shelf devices, opening a wide range of new approaches for computing location. However, RTT has been traditionally addressed to multilateration solutions. Few works exist that assess the feasibility of the RTT as an accurate feature in positioning methods based on classification algorithms. An attempt is made in this paper to fill this gap by investigating the performance of several classification models in terms of accuracy and positioning errors. The performance is assessed using different AP layouts, distinct AP vendors, and different frequency bands. The accuracy and precision of the RTT-based position estimation is always better than the one obtained with RSS in all the studied scenarios, and especially when few APs are available. In addition, all the considered ML algorithms perform pretty well. As a result, it is not necessary to use more complex solutions (e.g., SVM) when simpler ones (e.g., nearest neighbor classifiers) achieve similar results both in terms of accuracy and location error.This research was partially supported by MCIN/AEI/10.13039/ 501100011033 and ERDF “A way of making Europe” under grant PGC2018-099945-BI00, and by the European GNSS Agency (GSA) under grant GSA/GRANT/04/2019/BANSHEEPeer ReviewedPostprint (published version

RFID Localisation For Internet Of Things Smart Homes: A Survey

The Internet of Things (IoT) enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. The IoT incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. Localisation in indoor positioning systems plays an important role in the IoT. Location Based IoT applications range from tracking objects and people in real-time, assets management, agriculture, assisted monitoring technologies for healthcare, and smart homes, to name a few. Radio Frequency based systems for indoor positioning such as Radio Frequency Identification (RFID) is a key enabler technology for the IoT due to its costeffective, high readability rates, automatic identification and, importantly, its energy efficiency characteristic. This paper reviews the state-of-the-art RFID technologies in IoT Smart Homes applications. It presents several comparable studies of RFID based projects in smart homes and discusses the applications, techniques, algorithms, and challenges of adopting RFID technologies in IoT smart home systems.Comment: 18 pages, 2 figures, 3 table

A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem