Positioning is becoming a key component in many Internet of Things (IoT) applications. The main challenges and limitations are the narrow bandwidth, low power and low cost which reduces the accuracy of the time of arrival (TOA) estimation. In this paper, we consider the positioning scenario of Narrowband IoT (NB-IoT) that can benefit from observed time difference of arrival (OTDOA). By applying the deep learning based technique, we explore the generalization and feature extraction abilities of neural networks to tackle the aforementioned challenges. As demonstrated in the numerical experiments, the proposed algorithm can be used in different inter-site distance situations and results in a 15% and 50% positioning accuracy improvement compared with Gauss-Newton method in line-of-sight (LOS) scenario and non-line-of-sight (NLOS) scenario respectively.
The future of industrial applications is shaped by intelligent moving IoT devices, such as flying drones, advanced factory robots, and connected vehicles, which may operate (semi-)autonomously. In these challenging scenarios, dynamic radio connectivity at high frequencies -- augmented with timely positioning-related information -- becomes instrumental to improve communication performance and facilitate efficient computation offloading. Our work reviews the main research challenges and reveals open implementation gaps in Industrial IoT (IIoT) applications that rely on location awareness and mul…
The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems. In this article, we address the prospects of 5G New Radio (NR) sidelink based V2X networks and their applicability for increasing the situational awareness, in terms of continuous tracking of moving connected machines and vehicles, in industrial systems. For increased system flexibility and fast deployments, we assume that the locations of …
The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error s…
Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on th…
Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI), and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this paper, we investigate the indoor positioning under the 5G new radio (NR), which h…
Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrest…
With the rapid development of the Internet of Things (IoT), Indoor Positioning System (IPS) has attracted significant interest in academic research. Ultra-Wideband (UWB) is an emerging technology that can be employed for IPS as it offers centimetre-level accuracy. However, the UWB system still faces several technical challenges in practice, one of which is Non-Line-of-Sight (NLoS) signal propagation. Several machine learning approaches have been applied for the NLoS component identification. However, when the data contains a very small amount of NLoS components it becomes very difficult for ex…
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 …
UE localization has proven its implications on multitude of use cases ranging from emergency call localization to new and emerging use cases in industrial IoT. To support plethora of use cases Radio Access Technology (RAT)-based positioning has been supported by 3GPP since Release 9 of its specifications that featured basic positioning methods based on Cell Identity (CID) and Enhanced-CID (E-CID). Since then, multiple positioning techniques and solutions are proposed and integrated in to the 3GPP specifications. When it comes to evaluating performance of the positioning techniques, achievable …
Geneva School of Business Administration, HES-SO Geneva（日内瓦高等商学院（HES-SO日内瓦））
The primary expectation from positioning systems is for them to provide the users with reliable estimates of their position. An additional piece of information that can greatly help the users utilize position estimates is the level of uncertainty that a positioning system assigns to the position estimate it produced. The concept of dynamically estimating the accuracy of position estimates of fingerprinting positioning systems has been sporadically discussed over the last decade in the literature of the field, where mainly handcrafted rules based on domain knowledge have been proposed. The emer…
The 30-300GHz millimeter wave (mmWave) band is currently being pursued to combat the rising capacity demands in 5G, WiFi, and IoT networks. Due to the high frequency, impulse radio (IR) in this band is better suited for positioning than other existing low-frequency bands. Besides precision positioning, the exceptionally wide bandwidth also enables concurrent use of multiple center frequencies in the same application, which opens up additional avenues of information encoding in IR mmWave networks. In this paper, we propose a new mmWave IR framework that can simultaneously detect direction of ar…
Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 μW for a single measurement is achieved. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-no…
Massachusetts Institute of Technology（麻省理工学院）；Khalifa University（哈利法大学）
Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to iden…
With the unprecedented demand for location-based services in indoor scenarios, wireless indoor localization has become essential for mobile users. While GPS is not available at indoor spaces, WiFi RSS fingerprinting has become popular with its ubiquitous accessibility. However, it is challenging to achieve robust and efficient indoor localization with two major challenges. First, the localization accuracy can be degraded by the random signal fluctuations, which would influence conventional localization algorithms that simply learn handcrafted features from raw fingerprint data. Second, mobile …
On the of the applications in the realm of the Internet-of-Things (IoT) is real-time localization of assets in specific application environments where satellite based global positioning is unviable. Numerous approaches for localization relying on wireless sensor mesh systems have been evaluated, but the recent Bluetooth Low Energy (BLE) 5.1 direction finding features based on Angle-of-Arrival (AoA) promise a low-cost solution for this application. In this paper, we present an implementation of a BLE 5.1 based circular antenna array, and perform two experimental evaluations over the quality of …
Concordia University（康考迪亚大学）；Defence Research and Development Canada（加拿大国防研究与发展中心）；University of Toronto（多伦多大学）
Recent advancements in Internet of Things (IoTs) have brought about a surge of interest in indoor positioning for the purpose of providing reliable, accurate, and energy-efficient indoor navigation/localization systems. Ultra Wide Band (UWB) technology has been emerged as a potential candidate to satisfy the aforementioned requirements. Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation. On the one hand, achieving high precision in positioning relies on the identific…
Internet of Things (IoT)-based indoor localization has gained significant popularity recently to satisfy the ever-increasing requirements of indoor Location-based Services (LBS). In this context, Inertial Measurement Unit (IMU)-based localization is of interest as it provides a scalable solution independent of any proprietary sensors/modules. Existing IMU-based methodologies, however, are mainly developed based on statistical heading and step length estimation techniques that suffer from cumulative error issues and have extensive computational time requirements limiting their application for r…
