3 results found
Zafari F, Gkelias A, Leung K, A Survey of Indoor Localization Systems and Technologies
Indoor localization has recently witnessed an increase in interest, due tothe potential wide range of services it can provide by leveraging Internet ofThings (IoT), and ubiquitous connectivity. Different techniques, wirelesstechnologies and mechanisms have been proposed in the literature to provideindoor localization services in order to improve the services provided to theusers. However, there is a lack of an up-to-date survey paper that incorporatessome of the recently proposed accurate and reliable localization systems. Inthis paper, we aim to provide a detailed survey of different indoorlocalization techniques such as Angle of Arrival (AoA), Time of Flight (ToF),Return Time of Flight (RTOF), Received Signal Strength (RSS); based ontechnologies such as WiFi, Radio Frequency Identification Device (RFID), UltraWideband (UWB), Bluetooth and systems that have been proposed in theliterature. The paper primarily discusses localization and positioning of humanusers and their devices. We highlight the strengths of the existing systemsproposed in the literature. In contrast with the existing surveys, we alsoevaluate different systems from the perspective of energy efficiency,availability, cost, reception range, latency, scalability and trackingaccuracy. Rather than comparing the technologies or techniques, we compare thelocalization systems and summarize their working principle. We also discussremaining challenges to accurate indoor localization.
Zafari F, Papapanagiotou I, Devetsikiotis M, et al., An iBeacon based Proximity and Indoor Localization System
Indoor localization and Location Based Services (LBS) can greatly benefitfrom the widescale proliferation of communication devices. The basicrequirements of a system that can provide the aforementioned services areenergy efficiency, scalability, lower costs, wide reception range, highlocalization accuracy and availability. Different technologies such as WiFi,UWB, RFID have been leveraged to provide LBS and Proximity Based Services(PBS), however they do not meet the aforementioned requirements. Apple'sBluetooth Low Energy (BLE) based iBeacon solution primarily intends to provideProximity Based Services (PBS). However, it suffers from poor proximitydetection accuracy due to its reliance on Received Signal Strength Indicator(RSSI) that is prone to multipath fading and drastic fluctuations in the indoorenvironment. Therefore, in this paper, we present our iBeacon based accurateproximity and indoor localization system. Our two algorithms Server-SideRunning Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximitydetection accuracy of iBeacons by 29% and 32% respectively, when compared withApple's current moving average based approach. We also present our novelcascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoorlocalization. Our cascaded filter approach uses a Kalman Filter (KF) to reducethe RSSI fluctuation and then inputs the filtered RSSI values into a ParticleFilter (PF) to improve the accuracy of indoor localization. Our experimentalresults, obtained through experiments in a space replicating real-worldscenario, show that our cascaded filter approach outperforms the use of only PFby 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environmentsrespectively, and achieves a localization error as low as 0.70 meters in 2Denvironment and 0.947 meters in 3D environment.
Zafari F, Papapanagiotou I, Christidis K, 2016, Microlocation for Internet-of-Things-Equipped Smart Buildings, IEEE Internet of Things Journal, Vol: 3, Pages: 96-112
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