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Journal ArticleDOI

RSSI-Based Indoor Localization With the Internet of Things

04 Jun 2018-IEEE Access (IEEE)-Vol. 6, pp 30149-30161
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.
Citations
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Journal ArticleDOI
TL;DR: This article examines two memoryless positioning techniques, KNN and Naive Bayes, and compares them with simple trilateration, in terms of accuracy, precision, and complexity, and presents a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies.
Abstract: In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.

27 citations


Cites background from "RSSI-Based Indoor Localization With..."

  • ...A comparison of the energy requirements of the different IoT technologies can be found in [30]....

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  • ...A detailed explanation of the trilateration process followed can be seen in [30]....

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Journal ArticleDOI
TL;DR: This paper proposes a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency, and provides design guidelines for high- Accuracy and high-efficiency multi- agent localization systems.
Abstract: Ultra-wideband technology has the merits of high temporal resolution and stability, and it has been widely used for high-accuracy localization and tracking. However, most ultra-wideband localization systems need multiple anchors for trilateration, which results in high system cost, large messages overhead, and insufficient extraction of information. In this paper, we propose a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency. In the proposed method, the anchor broadcasts the first two messages and then each agent responds one message to the anchor (quasi-)simultaneously. Based on the received message with superpositioned agent responses, the time-of-flight and angle-of-arrival information from all agents to the anchor can be extracted altogether. We implement the localization system in two indoor environments, and show that the proposed method can achieve decimeter-level accuracy for multiple agents using three messages. Our method provides design guidelines for high-accuracy and high-efficiency multi-agent localization systems.

26 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the improved conventional KNN algorithm can achieve very high positioning accuracy and is totally suitable for several specific 2-D indoor positioning applications.
Abstract: Enhancing the accuracy of indoor visible light positioning systems with simple, real-time, and stable methods is one of the interesting challenges in recent research. In this paper, a relatively minor mean positioning error of 8 mm and a 42-52% improvement in computational time could be achieved within a real space of 1.2 m $\\times1.2$ m $\\times1.2$ m by transcending the serious limitations of the traditional k-nearest neighbors (KNN) algorithm. These disadvantages (slow execution time, high error formation) are a result of finding the nearest neighbors from all the fingerprints, averaging the Euclidean distances, and the excessive passivity of the K value. To overcome the above limitations of KNN, we proposed a maximum received signal strength recognition (MRR) technique and weighted optimum KNN (WOKNN) algorithm, which is a combination of optimum KNN (OKNN) and weighted KNN (WKNN). While MRR was used to reduce the computational time, WOKNN was used to solve the remaining problems. Specifically, OKNN was used to automatically determine the best number of nearest neighbors for each position in the area under consideration, and WKNN helped improve the errors that come from the Euclidean distance averaging process. Based on positive experimental results and a meaningful comparison with various versions of KNN, we demonstrated that the improved conventional KNN algorithm can achieve very high positioning accuracy and is totally suitable for several specific 2-D indoor positioning applications.

25 citations


Cites background or methods from "RSSI-Based Indoor Localization With..."

  • ...The RSS is a popular, simple approach that provides acceptable positioning error for indoor positioning applications [13]; This approach is also applied for other types of signals such as WIFI [15], [16], Bluetooth [17], [18], Lorawan [13], and Zigbee [19], [20]....

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  • ...Each method has its own advantages and disadvantages, of which RSS is considered as one of the cheapest and simplest solutions [13]....

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Journal ArticleDOI
TL;DR: The capability of LoRa for accurate and precise indoor localization in a typical apartment setting is demonstrated and experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line ofsight scenario with a precision better than 25 cm in all cases.
Abstract: With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates.

25 citations


Cites background from "RSSI-Based Indoor Localization With..."

  • ...Beyond the accuracy and coverage (maximum range), LoRa has lower power consumption than WiFi and UWB, and similar power consumption as RFID and BLE [69,70]....

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Proceedings ArticleDOI
01 Dec 2020
TL;DR: Wang et al. as discussed by the authors proposed a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks.
Abstract: This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.

24 citations

References
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Journal ArticleDOI
TL;DR: This paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
Abstract: The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

4,335 citations


"RSSI-Based Indoor Localization With..." refers background in this paper

  • ...These devices are capable of communicating with the IoT to allow for smart buildings to poses a greater amount of control that could never have been achieved before [1], [2]....

    [...]

Journal ArticleDOI
01 Nov 2007
TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Abstract: Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

4,123 citations


"RSSI-Based Indoor Localization With..." refers background or methods in this paper

  • ...AoA systems use an array of antennae to determine the angle, from which the signal propagated [8], [19], [20]....

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  • ...The most common technologies are: WiFi, Bluetooth, Radio Frequency Identification (RFID), Ultra-Wide Band (UWB) and cellular [8]....

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  • ...So far, a standard model for indoor localization has not been developed due to obstacles, floor layouts, and reflections of signals that can occur [8]....

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  • ...other methods need to be used in order to determine a device’s location [8]–[10]....

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  • ...For instance, there are many more obstacles indoors, including furniture, walls, and people, which can reflect the signals produced, increasing multipath effects [7], [8], [15]....

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Journal ArticleDOI
TL;DR: The authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity for smart objects, describing activity-, policy-, and process-aware smart objects and demonstrating how the respective architectural abstractions support increasingly complex application.
Abstract: The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application.

1,459 citations


"RSSI-Based Indoor Localization With..." refers background in this paper

  • ...These devices are capable of communicating with the IoT to allow for smart buildings to poses a greater amount of control that could never have been achieved before [1], [2]....

    [...]

Journal ArticleDOI
TL;DR: This paper aims to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time ofFlight (RTOF), and received signal strength (RSS) based on technologies that have been proposed in the literature.
Abstract: Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

1,447 citations


"RSSI-Based Indoor Localization With..." refers background in this paper

  • ...to ToA in that it requires devices to have synchronized clocks, but it uses the signal propagation time to multiple receivers to find the absolute signal propagation time [20]....

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  • ...AoA systems use an array of antennae to determine the angle, from which the signal propagated [8], [19], [20]....

    [...]

  • ...Through the use of synchronized clocks, the signal propagation time between the transmitter and receiver can be determined [19], [20]....

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Journal ArticleDOI
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.

736 citations


"RSSI-Based Indoor Localization With..." refers background in this paper

  • ...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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