RSSI-Based Indoor Localization With the Internet of Things
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.
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24 Nov 2020
TL;DR: In this paper, an NB classifier was designed for a UWB-based localization system and the data were classified into three categories: high, medium, and low accuracy with the help of NB classifiers and RMSE.
Abstract: In this chapter, we have designed an NB classifier for a UWB-based localization system. With the help of NB classifier and RMSE, the data are classified into three categories: high, medium, and low accuracy. ROCs are plotted to show the effec-tiveness of the NB classifier. As our developed technique obtains more than 90% classification accuracy, we have tested it into two different environments: LOS and partial NLOS conditions. Furthermore, to test the accuracy, small-sized and medium-sized rooms were used. From our measurements, it is observed that the accuracy of the developed NB classifier is dependent upon the environment. For LOS and NLOS envi-ronments, the accuracy are around 97% and 87.38%, respectively. Our future research will concentrate on technique that can further improve the localization classification and improve the positioning accuracy of the IPS.
5 citations
Cites methods from "RSSI-Based Indoor Localization With..."
...There are many algorithms for trilateration in literature such as [26]-[28] and references therein....
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01 Oct 2019
TL;DR: This paper sheds light on the utility of the four illustrative radio-based localization techniques, including satellite navigation and the use of BLE, LoRaWAN, and Cat-NB1 communication technologies, and shows that multi-radio– enabled localization solutions are more flexible than those based on a single localization solution and provide space for further optimizations, especially with regards to energy consumption.
Abstract: The value of the location data for the Internet of Things (IoT) devices is undisputed, and today’s radio technologies provide various means to obtain these data. In this paper, we shed light on the utility of the four illustrative radio-based localization techniques, including satellite navigation and the use of BLE, LoRaWAN, and Cat-NB1 communication technologies. First, we instrument a prototype device comprising all these solutions, thus confirming the feasibility of multi-radio– enabled IoT devices supporting localization. Then, we employ this prototype to empirically assess the energy utility of different localization approaches. Our results demonstrate that in terms of energy consumption, the difference between these techniques approaches five orders of magnitude. By conducting a follow-up survey of the relevant research papers, we assess other important performance metrics, such as the operating environment and the required infrastructure, as well as their effect on the localization accuracy. By combining these results, we show that multi-radio– enabled localization solutions are more flexible than those based on a single localization solution and provide space for further optimizations, especially with regards to energy consumption. Development of such devices is an crucial step toward enabling “anytime, anywhere” localization for IoT devices.
5 citations
Cites background from "RSSI-Based Indoor Localization With..."
...6 m, 10 beacons below 2 m CDF 90% BLE [11] experiment DSBa indoor 110 m square, 3 beacons 7 m max BLE [14] experiment DCPa, RSS indoor (conf....
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...1 m max LoRaWAN [14] experiment DCPa, RSS indoor (conf....
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30 Jan 2021
TL;DR: DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization using deep learning methods and is found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes.
Abstract: LoRa technology has received a lot of attention in the last few years Numerous success stories about using LoRa technology for the Internet of Things in various implementations Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications LoRa technology is found more stable and is more resilient to environmental changes Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint All of these features are used for training in-deep learning methods It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization
5 citations
Cites background from "RSSI-Based Indoor Localization With..."
...Sadowski et al [10] found that LoRa has a poor performance for indoor localization however on the other side diverse the development of IoT LoRa technology was found to have some advantages as an alternative wireless-based technology....
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DOI•
TL;DR: This study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments and found the accuracy in all the experimental results to be at the decimeter level.
Abstract: A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.
5 citations
15 Jun 2020
TL;DR: A new lookup algorithm based on chord protocol is presented that permits to provide the current location of a specific target in an extended fog-based indoor localization platform within the shortest delays and without the need for strong nodes capabilities.
Abstract: The interest in indoor localization systems has grown outstandingly in recent years given the variety of fields of application. In certain technologies, the indoor localization platforms consist of a set of wireless nodes deployed inside the building and others attached to the mobile targets. These wireless nodes collect the required data for localization and send them to a central unit for processing because of their limited power and computation capabilities. The main goal of these systems is to provide the position of the targets in a timely fashion. However, when the number of smart objects increases in larger networks, the delay becomes significant and the lookup process of the target's position becomes challenging. In such a situation, there is a tradeoff between the lookup time efficiency and the central unit storage capabilities. The fog computing presents a promising solution for high computational and storage applications. It is a distributed architecture that brings both computational power and large storage capability to the edge level via distributed and interconnected fog nodes. We present in this paper a new lookup algorithm basedon chord protocol that permits to provide the current location of a specific target in an extended fog-based indoor localization platform within the shortest delays and without the need for strong nodes capabilities.
5 citations
Cites background from "RSSI-Based Indoor Localization With..."
...The indoor localization services are growing sharply, and they become an urgent need especially with the progress of the Internet of Things (IoT) technology [16] [12]....
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References
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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]....
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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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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]....
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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]....
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...Through the use of synchronized clocks, the signal propagation time between the transmitter and receiver can be determined [19], [20]....
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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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