Coverage measurements of NB-IoT technology

Abstract. The narrowband internet of things (NB-IoT) is a cellular radio access technology that provides seamless connectivity to wireless IoT devices with low latency, low power consumption, and long-range coverage. For long-range coverage, NB-IoT offers a coverage enhancement (CE) mechanism that is achieved by repeating the transmission of signals. Good network coverage is essential to reduce the battery usage and power consumption of IoT devices, while poor network coverage increases the number of repetitions in transmission, which causes high power consumption of IoT devices. The primary objective of this work is to determine the network coverage of NB-IoT technology under the University of Oulu’s 5G test network (5GTN) base station. In this thesis work, measurement results on key performance indicators such as reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal to noise plus interference (SINR) have been reported. The goal of the measurement is to find out the NB-IoT signal strength at different locations, which are served by the 5GTN cells configured with different parameters, e.g., Tx power levels, antenna tilt angles. The signal strength of NB-IoT technology has been measured at different places under the 5GTN base station in Oulu, Finland. Drive tests have been conducted to measure the signal strength of NB-IoT technology by using the Quectel BG96 module, Qualcomm kDC-5737 dongle and Keysight Nemo Outdoor software. The results have shown the values of RSRP, RSRQ, RSSI, and SINR at different locations within several kilometres of the 5GTN base stations. These values indicate the performance of the network and are used to assess the performance of network services to the end-users. In this work, the overall performance of the network has been checked to verify if network performance meets good signal levels and good network coverage. Relevant details of the NB-IoT technology, the theory behind the signal coverage and comparisons with the measurement results have also been discussed to check the relevance of the measurement results

Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications

Předkládaná disertační práce je zaměřena na „Výzkum spolehlivé komunikace pro IoT aplikace v bezdrátových sítích využívajících technologie Multi-RAT LPWAN“. Navzdory značnému pokroku v oblasti vývoje LPWA technologií umožňující masivní komunikace mezi zařízeními (mMTC), nemusí tyto technologie výkonnostně dostačovat pro nově vznikající aplikace internetu věcí. Hlavním cílem této disertační práce je proto nalezení a vyhodnocení limitů současných LPWA technologií. Na základě těchto dat jsou nevrženy nové mechanismy umožňující snazší plánování a vyhodnocování síťového pokrytí. Navržené nástroje jsou vyladěny a validovány s využitím dat získaných z rozsáhlých měřících kampaních provedených v zákaznických LPWA sítích. Tato disertační práce dále obsahuje návrh LPWA zařízení vybavených více komunikačními rozhraními (multi-RAT) které mohou umožnit překonání výkonnostních limitů jednotlivých LPWA technologií. Současná implementace se zaměřuje zejména na snížení spotřeby zařízení s více rádiovými rozhraními, což je jejich největší nevýhodou. K tomuto účelu je využito algoritmů strojového učení, které jsou schopné dynamicky vybírat nejvhodnější rozhraní k přenosu.This doctoral thesis addresses the “Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications”. Despite the immense progress in massive Machine-Type Communication (mMTC) technology enablers such as Low-Power Wide-Area (LPWA) networks, their performance does not have to satisfy the requirements of novelty Internet of Things (IoT) applications. The main goal of this Ph.D. work is to explore and evaluate the limitations of current LPWA technologies and propose novel mechanisms facilitating coverage planning and assessment. Proposed frameworks are fine-tuned and cross-validated by the extensive measurement campaigns conducted in public LPWA networks. This doctoral thesis further introduces the novelty approach of multi-RAT LPWA devices to overcome the performance limitation of individual LPWA technologies. The current implementation primarily focuses on diminishing the greatest multi-RAT solutions disadvantage, i.e., increased power consumption by employing a machine learning approach to radio interface selection.

Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

Outdoor ranging and positioning based on LoRa modulation

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We present results for outdoor ranging and positioning based on the Long-Range (LoRa) modulation, which is a widespread wireless IoT technology. LoRa systems typically operate in sub-GHz frequency bands with only 125 kHz bandwidth, which restricts the ranging performance due to the limited temporal resolution. The investigated LoRa ranging method operates in the 2.4 GHz ISM frequency band and supports wider bandwidths, achieving for outdoor ranging a mean distance error below 50 m over distances of up to 1400 m. Positioning based on LoRa ranging obtained a measured Distance Root Mean Square (DRMS) error of 30 m, which reduced to 5 m for tracking of a mobile node, making it a suitable method for approximate outdoor positioning and tracking of IoT devices without using Global Navigation Satellite Systems (GNSS)

Technologies for urban and rural internet of things

Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented

Exploiting PHY for improving LoRa based communication and localisation system

LoRa is an emerging technology of low-power wide-area networks (LPWANs) operating on industrial, scientific and medical (ISM) bands to provide connectivity for Internet of Thing (IoT) devices. As the number of devices increases, the network suffers from scalability issues. Therefore, we design a cloud radio access network (C-RAN or Cloud-RAN) with multiple LoRa gateways to solve this problem. Furthermore, we develop novel algorithms to provide accurate localisation for LoRa devices. This thesis makes three new contributions to LoRa based communication and localisation system as follows. The first contribution is a compressive sensing-based algorithm to reduce the uplink bit rate between the gateways and the cloud server. The proposed novel compression algorithm can reduce the bandwidth usage for the fronthaul without decreasing LoRa packet delivery rates. Our evaluation shows that with four gateways up to 87.5% PHY samples can be compressed and 1.7x battery life for end devices can be achieved. The second contribution is a novel algorithm to improve the resolution of the radio signals for localisation. The proposed algorithm synchronises multiple non-overlapped communication channels by exploiting the unique features of the LoRa radio to increase the overall bandwidth. We evaluate its performance in an outdoor area of 100 m × 60 m, which shows a median error of 4.4 m, and a 36.2% error reduction compared to the baseline. The above approach improves the accuracy of outdoor localisation; however, it does not work for indoor localisation due to the increase of multiple radio propagation paths. Therefore, our third contribution is an improved super-resolution algorithm for indoor localisation. By exploiting both the original and the conjugate of the physical layer, the algorithm can resolve the multiple paths from multiple reflectors in clustered indoor environments. We evaluate its performance in an indoor area of 25 m × 15 m, which shows that a median error of 2.4 m can be achieved, which is 47.8% and 38.5% less than the baseline approach and the approach without using the conjugate information, respectively. Our evaluation also shows that, different to previous studies in Wi-Fi localisation systems that have significantly wider bandwidth, time-of-fight (ToF) estimation is less effective to LoRa localisation systems with narrowband radio signals

In situ underwater microwave oil spill and oil slick thickness sensor

Nearly 30 percent of oil drilled globally is done offshore. Oil spillage offshore have far-reaching consequences on the environment, aquatic lives, and livelihoods as it was evident in the numerous accidents such as the Deepwater Horizon and Bonga oil spillages. Apart from detecting oil spillages, the determination of the oil slick thickness is very important. This is to enable the estimation of the volume and spread of oil discharged in oceans, seas and lakes. This information could guide the oil spill countermeasures and provide the basis for legal actions against the defaulting parties. The viability of the use of radar in the detection of oil spill has already been established by airborne and space borne synthetic aperture radar (SAR). Notwithstanding, the high latency associated with SARs and its susceptibility of false positive and false negative detection of oil slick makes it vulnerable. It has also not been very successful in the determination of oil slick thickness. In situ methods such as the capacitive, conductive and optical based approaches have been used to detect as well as determine oil slick thickness. Some of these contact-based approaches are susceptible to corrosion, fouling and require several calibrations. Radio frequency (RF) signals in seawater suffer from attenuation and dispersion due to the high conductivity of the medium. Antennas, ideally matched to free space, suffer impedance mismatches when immersed in seawater. In this thesis, we proposed the novel approach of using microwave techniques to detect oil spillage and determine oil slick thickness based on a contact-based in situ approach. The work began by undertaking an investigation into the properties of the North Sea water which was used as the primary transmission medium for the study. Subsequently, the research developed an ultrawideband antenna that radiated underwater, which was encapsulated in polydimethylsiloxane (PDMS). The antenna-sensor with a Faraday cage was used to develop a novel microwave oil spill sensor. A communication backbone was designed for the sensor using long range (LoRa) 868 MHz frequency based on a bespoke braid antenna buffered by oil impregnated papers to ameliorate against the influence of the seawater surface. Using a four layered RF switch controller and an antenna array consisting of four antenna-sensors, a novel microwave oil slick thickness sensor was developed. The antenna-sensors were arranged in a cuboid fashion with antenna-sensor 3 and antenna-sensor 4 capable of detecting oil slick thickness at 23 mm and 46 mm using their transmission coefficient (S43) of -10 dB and -19 dB compared to that of the pure seawater respectively. For the 69 mm and 92 mm thickness, the transmission coefficient (S21) of antenna-sensor 1 and antenna-sensor 2 was used to determine these thicknesses with values of -13.5 dB and -24.14 dB with respect to that of pure seawater

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business