Location Fingerprinting With Bluetooth Low Energy Beacons
TL;DR: This work provides a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device, and investigates the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency.
Abstract: The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a $\sim\! 600\ \mbox{m}^2$ testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve $30\ \mbox{m}^2$ ), compared to $100\ \mbox{m}^2$ ) and < 8.5 m for an established WiFi network in the same area.
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Proceedings Article•
01 Jan 2007TL;DR: In this paper, the Gaussian Process Latent Variable Model (GPLVM) is used to reconstruct a topological connectivity graph from a signal strength sequence, which can be used to perform efficient WiFi SLAM.
Abstract: WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
488 citations
TL;DR: An algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel- separation fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons is proposed.
Abstract: Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of <2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than <3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than <3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of <3.88 m at 90% of the time, which performs 49.58% more accurate than <8.00 m from the PM + EKF algorithm and 21.41% better than <4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.
371 citations
Cites background or methods from "Location Fingerprinting With Blueto..."
...Related Work Current localization algorithms for BLE beacons can be divided into three classes: proximity [21–23], range-based [21,24–26] and FP [4,21,27,28]....
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...Related Work Current localization algorithms for BLE beacons can be divided into three classes: proximity [21,22,23], range-based [21,24–26] and FP [4,21,27,28]....
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...To balance the power consumption and accuracy, each beacon was set to 10 Hz sample rate with ́16 dBm transmit power [4]....
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...The research [4] provides a detailed study of the effects of beacon density, transmit power and transmit frequency for BLE FP....
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...To balance the power consumption and accuracy, each beacon was set to 10 Hz sample rate with −16 dBm transmit power [4]....
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TL;DR: A technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches is provided, and the existing approaches are classified in a structure in order to guide the review and discussion of the different approaches.
Abstract: Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.
348 citations
Cites background from "Location Fingerprinting With Blueto..."
...As Bluetooth beacons compete with Wi-Fi, Faragher and Harle [59] provided a quantitative comparison with WiFi fingerprinting....
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...Bluetooth technology has been considered for indoor position systems as a competitor to Wi-Fi, in particular since the widespread adoption of Bluetooth Low Energy (BLE), due to its availability (it is supported by most modern smartphones), low cost, and very low power consumption, which allows fixed emitters to run on batteries for several months or even years [59]....
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TL;DR: Four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network are compared in terms of localization accuracy and power consumption when IoT devices are used.
Abstract: In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost-efficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.
346 citations
Cites background from "Location Fingerprinting With Blueto..."
...With the recent emergence of BLE and beacons, it has becomemore feasible to place inexpensive beacons around an environment than it is to rearrange existing hardware and use that for localization [17], [18]....
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TL;DR: This paper proposes a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent and utilizes variational autoencoders as the inference engine for generalizing optimal policies.
Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users’ feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.
314 citations
Cites background from "Location Fingerprinting With Blueto..."
...1) While WiFi fingerprinting has been studied widely in the past decade for indoor positioning and the accuracy is in the range of 10 m, Bluetooth low energy (BLE) is in its infancy for indoor localization and has yielded more fine-grained results [13]....
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References
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20 Sep 2013
TL;DR: SmartSLAM moves between different sensor fusion algorithms depending on the current level of certainty in the system, reducing the computational load of the tracking engine, maintaining good positioning performance, improving battery life and freeing CPU cycles for foreground processes.
Abstract: The most promising solution to the ubiquitous positioning problem is the smartphone, and many smartphone-based indoor tracking methods exist today. To ensure consumer acceptance of the technologies, it is critical that these systems do not have a significant effect on the battery life of the device. Methods exploiting signal fingerprinting have been shown to provide good performance with low processing overhead but require prior surveying. Methods exploiting opportunistic sensing and machine learning techniques such as Simultaneous Localization and Mapping (SLAM) need no prior data but at the cost of high computational load. This paper describes a smartphone-based indoor positioning system that exploits a new intelligent filtering approach to reduce this computational load. SmartSLAM moves between different sensor fusion algorithms depending on the current level of certainty in the system, reducing the computational load of the tracking engine, maintaining good positioning performance, improving battery life and freeing CPU cycles for foreground processes.
69 citations
22 Jun 2010
TL;DR: An indoor room-level Bluetooth-based localization system, based on a voting scheme, that eliminates cells close to a base station detecting devices with a weak signal is proposed.
Abstract: Location-Based Services have become quite popular in mobile computing. However, these services are often not effective when devices are in indoor environments. This paper presents an indoor room-level Bluetooth-based localization system. Fixed stations distributed throughout the environment get the RSSI from user devices. With these data, we compute the distance between the device and the stations, based on the signal propagation. In this paper, we propose a room localization method, based on a voting scheme, that eliminates cells close to a base station detecting devices with a weak signal. Another contribution of this paper is the empirical evaluation of the methods within a real environment.
66 citations
Additional excerpts
...[7], [21] to fingerprinting [6], [21]....
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