Localization in Long-range Ultra Narrow Band IoT Networks using RSSI

Internet of things wireless networking with long range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.Comment: Accepted in ICC 17. To be presented in IEEE International Conference on Communications (ICC), Paris, France, 201

Experimental performance evaluation of outdoor TDoA and RSS positioning in a public LoRa network

This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,...) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm

Position certainty propagation: a location service for MANETs

International audienceLocalization in Mobile Ad-hoc Networks (MANETs) and Wireless Sensor Networks (WSNs) is an issue of great interest, especially in applications such as the IoT and VANETs. We propose a solution that overcomes two limiting characteristics of these types of networks. The first is the high cost of nodes with a location sensor (such as GPS) which we will refer to as anchor nodes. The second is the low computational capability of nodes in the network. The proposed algorithm addresses two issues; self-localization where each non-anchor node should discover its own position, and global localization where a node establishes knowledge of the position of all the nodes in the network. We address the problem as a graph where vertices are nodes in the network and edges indicate connectivity between nodes. The weights of edges represent the Euclidean distance between the nodes. Given a graph with at least three anchor nodes and knowing the maximum communication range for each node, we are able to localize nodes using fairly simple computations in a moderately dense graph

Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network

Internet of Things (IoT) applications that value long battery lifetime over accurate location-based services benefit from localization via Low Power Wide Area Networks (LPWANs) such as LoRaWAN. Recent work on Angle Of Arrival (AoA) estimation with LoRa enables us to explore new optimizations that decrease the estimation error and increase the reliability of Time Difference Of Arrival (TDoA) methods. In this paper, particle filtering is applied to combine TDoA and AoA measurements that were collected in a dense urban environment. The performance of this particle filter is compared to a TDoA estimator and our previous grid-based combination. The results show that a median estimation error of 199 m can be obtained with a particle filter without AoA, which is an error reduction of 10 % compared to the grid-based method. Moreover, the median error is reduced with 57 % if AoA measurements are used. Hence, more accurate and reliable localization is achieved compared to the performance of other baseline methods

Методи визначення координат об'єктів з використанням технології LoRa

Робота виконана на 98 сторінках, складається з 5 розділів. В них увійшло 30 ілюстрацій, 35 таблиць та 23 джерела в переліку посилань. Актуальність. Останні 20 років ознаменувались різким ростом інтернет-технологій. Провідне місце серед них посідає Інтернет речей (Internet of Things, IoT). IoT містить мільярди взаємопов'язаних пристроїв, кількість яких збільшується кожного дня. Виробниками пропонується різноманіття приладів для забезпечення особистої безпеки, підвищення побутового комфорту або розширення комунікаційних можливостей. Однією з областей застосування технології IoT, яка досить стрімко розширюється, є відслідковування місцезнаходження об’єктів. Спектр означених технологій сягає від відображення детальної інформації про поточний стан об'єкта, його on-line розташування, побудови маршрутів руху міського транспорту, пошуку метеостанцій у важкодоступних місцях до контролю за поштовими відправленнями клієнтів. Зв’язок роботи з науковими програмами, планами, темами. Дисертаційне дослідження проводилися відповідно до тематики наукових досліджень ТОВ "Радионикс" Метою дисертаційної роботи є збільшення точності методу позиціонування об’єктів на місцевості з використанням технології LoRa за рахунок вибору оптимальної математичної моделі втрат сигналу. Для досягнення поставленої мети в роботі вирішувалися наступні задачі: проаналізовано існуючі підходи та методи геолокації; побудовано математичні моделі щодо розв’язання завдання; розроблено алгоритм трилатерації об’єктів; створено модуль прийому/передачі для експериментальної перевірки ефективності здійснених заходів; програмно реалізовано Simulink-модель для імплементації розроблених рішень у системи позиціонування; запропоновано стартап-проект. Об’єктом дослідження є системи позиціонування об’єктів на місцевості. Предметом дослідження є метод трилатерації з використанням RSSI ранжування. Методи дослідження. Методи наукового пізнання (аналіз, синтез, індукція, дедукція, аналогія), порівняльний аналіз математичний моделей: Логарифмічних втрат, Окомури-Хата та модель ITU-R. Наукова новизна. Експериментально досліджено та показано, що для вирішення задачі трилатерації модель рекомендована ITU-R найточніше враховує параметри навколишнього середовища, в порівнянні з моделями Логарифмічних втрат, Окомури-Хата. Запропоновано Simulink-модель, яка дозволяє досліджувати вплив факторів навколишнього середовища на процес моделювання втрат. Така модель може бути імплементована безпосередньо в мережу LoRa. Практичне значення отриманих результатів. Розроблено та програмно реалізовано алгоритм математичної моделі трилатерації; створено дослідний зразок прийомо-передавача технології LoRa; запропоновано методику експериментальних досліджень для моделей втрат потужності у просторі базуючись на технології LoRa; визначено рішення для зменшення похибок визначення координат об’єктів.The work is performed on 98 pages, consists of 5 sections. They include 30 illustrations, 35 tables and 23 sources in the list of references. Topicality. The last 20 years have been marked by a sharp rise in Internet technology. The leading place among them is occupied by the Internet of Things (IoT). The IoT contains billions of interconnected devices, the number of which is increasing every day. Manufacturers offer a variety of devices to ensure personal safety, increase home comfort or expand communication capabilities. One of the applications of IoT technology, which is expanding rapidly, is the tracking of the location of objects. The range of these technologies ranges from displaying detailed information about the current state of the object, its on-line location, construction of public transport routes, search for weather stations in hard-to-reach places to control customer mail. Connection of work with scientific programs, plans, topics. The dissertation research was conducted in accordance with the topics of scientific research of the Department of KEOA of the National Technical University of Ukraine "Kyiv Polytechnic Institute. Igor Sikorsky ». The aim of the dissertation is to increase the accuracy of the method of positioning objects on the ground using LoRa technology by choosing the optimal mathematical model. To achieve this goal, the following tasks were solved in the work: the existing approaches and methods of geolocation are analyzed; mathematical models for solving the problem are built; an algorithm for object trilateration has been developed; the module of reception / transfer for experimental check of efficiency of the carried-out actions is created; software implemented Simulink-model for implementation of developed solutions in positioning systems; proposed startup project. The object of research is the positioning systems of objects on the ground. The subject of the study is the method of trilateration using RSSI ranking. Research methods. Methods of scientific knowledge (analysis, synthesis, induction, deduction, analogy), comparative analysis of mathematical models: Logarithmic losses, Okomuri-Khata and ITU-R model. Scientific novelty of the obtained results. It has been experimentally investigated and shown that the recommended ITU-R model for solving the trilateration problem is more efficient in comparison with the Logarithmic Loss models, Okomuri-Khata. A Simulink model is proposed, which allows to study the influence of environmental factors on the process of loss modeling. Such a model can be implemented directly into the LoRa network. The practical significance of the results. The algorithm of the mathematical model of trilateration is developed and programmatically implemented; a prototype of the transceiver of LoRa technology was created; the technique of experimental researches for models of power losses in space based on LoRa technology is offered; identified solutions to reduce errors in determining the coordinates of objects

Crowdsourcing error impact on indoor positioning

Nowadays, with the rapid development of communication technology, plenty of new applications of 5G and IoT have appeared which requires high accuracy positioning skills. Wi-Fi based fingerprinting method is one of the most promising approaches for indoor positioning. Crowdsourcing is an appropriate fingerprint data collecting method on one hand. However, it is vulnerable to different kinds of crowdsourcing errors which add errors to the fingerprint database and can decrease the accuracy of positioning on another hand. The main target of this thesis is to statistically analyze the behavior of the crowdsourcing data collected by different devices, and the effects of different kinds of intentionally or unintentionally added errors through MATLAB. From the analysis results, it can be concluded that two different kinds of manually added errors perform complete differently. Data modified with all constant RSS values, out of author’s expectation, achieves a decent accuracy similar to the original data. While data modified with only position error shows a behavior that the positioning accuracy drops with the increase of modified data proportion. Most of the distributions are closest to the Burr type XII distribution, which is particularly useful for modeling histograms