Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method

5G and beyond networks

This chapter investigates the Network Layer aspects that will characterize the merger of the cellular paradigm and the IoT architectures, in the context of the evolution towards 5G-and-beyond, including some promising emerging services as Unmanned Aerial Vehicles or Base Stations, and V2X communications

A use case of low power wide area networks in future 5G healthcare applications

Abstract. The trend in all cellular evolution to the Long-Term Evolution (LTE) has always been to offer users continuously increasing data rates. However, the next leap forwards towards the 5th Generation Mobile Networks (5G) will be mainly addressing the needs of devices. Machines communicating with each other, sensors reporting to a server, or even machines communicating with humans, these are all different aspects of the same technology; the Internet of Things (IoT). The key differentiator between Machine-to-Machine (M2M) communications and IoT will be the added -feature of connecting devices and sensors not only to themselves, but also to the internet. The appropriate communications network is the key to allow this connectivity. Local Area Networks (LANs) and Wide Area Networks (WANs) have been thought of as enablers for IoT, but since they both suffered from limitations in IoT aspects, the need for a new enabling technology was evident. LPWANs are networks dedicated to catering for the needs of IoT such as providing low energy consumption for wireless devices. LPWANs can be categorized into proprietary LPWANs and cellular LPWANs. Proprietary LPWANs are created by an alliance of companies working together on creating a communications standard operating in unlicensed frequency bands. An example of proprietary LPWANs is LoRa. Whereas cellular LPWANs are standardized by the 3rd Partnership Project (3GPP) and they are basically versions of the LTE standard especially designed for machine communications. An example of cellular LPWANs is Narrowband IoT (NB IoT). This diploma thesis documents the usage of LoRa and NB IoT in a healthcare use case of IoT. It describes the steps and challenges of deploying an LTE network at a target site, which will be used by the LoRa and NB IoT sensors to transmit data through the 5G test network (5GTN) to a desired server location for storing and later analysis.Matalan tehonkulutuksen ja pitkänkantaman teknologian käyttötapaus tulevaisuuden 5G:tä hyödyntävissä terveydenhoidon sovelluksissa. Tiivistelmä. Pitemmän aikavälin tarkastelussa matkaviestintäteknologian kehittyminen nykyisin käytössä olevaan Long-Term Evolution (LTE) teknologiaan on tarkoittanut käyttäjille yhä suurempia datanopeuksia. Seuraavassa askeleessa kohti 5. sukupolven matkaviestintäverkkoja (5G) lähestytään kehitystä myös laitteiden tarpeiden lähtökohdista. Toistensa kanssa kommunikoivat koneet, palvelimille dataa lähettävät anturit tai jopa ihmisten kanssa kommunikoivat koneet ovat kaikki eri puolia samasta teknologisesta käsitteestä; esineiden internetistä (IoT). Oleellisin ero koneiden välisessä kommunikoinnissa (M2M) ja IoT:ssä on, että erinäiset laitteet tulevat olemaan yhdistettyinä paitsi toisiinsa myös internettiin. Tätä kytkentäisyyttä varten tarvitaan tarkoitukseen kehitetty matkaviestinverkko. Sekä lähiverkkoja (LAN) että suuralueverkkoja (WAN) on pidetty mahdollisina IoT mahdollistajina, mutta näiden molempien käsitteiden alle kuuluvissa teknologioissa on rajoitteita IoT:n vaatimusten lähtökohdista, joten uuden teknologian kehittäminen oli tarpeellista. Matalan tehonkulutuksen suuralueverkko (LP-WAN) on käsite, johon luokitellaan eri teknologioita, joita on kehitetty erityisesti IoT:n tarpeista lähtien. LP-WAN voidaan jaotella ainakin itse kehitettyihin ja matkaviestinverkkoihin perustuviin teknologisiin ratkaisuihin. Itse kehitetyt ratkaisut on luotu lukuisten yritysten yhteenliittymissä eli alliansseissa ja nämä ratkaisut keskittyvät lisensoimattomilla taajuuksilla toimiviin langattomiin ratkaisuihin, joista esimerkkinä laajasti käytössä oleva LoRa. Matkaviestinverkkoihin perustuvat lisensoiduilla taajuuksilla toimivat ratkaisut on puolestaan erikseen standardoitu 3GPP-nimisessä yhteenliittymässä, joka nykyisellään vastaa 2G, 3G ja LTE:n standardoiduista päätöksistä. Esimerkki 3GPP:n alaisesta LPWAN-luokkaan kuuluvasta teknologiasta on kapea kaistainen IoT-teknologia, NB-IoT. Tässä diplomityössä keskitytään terveydenhoidon käyttötapaukseen, missä antureiden mittaamaa tietoa siirretään langattomasti käyttäen sekä LoRa että NB-IoT teknologioita. Työssä kuvataan eri vaiheet ja haasteet, joita liittyi kun rakennetaan erikseen tiettyyn kohteeseen LTE-verkon radiopeitto, jotta LoRa:a ja NB-IoT:a käyttävät anturit saadaan välittämään mitattua dataa halutulle palvelimelle säilytykseen ja myöhempää analysointia varten. LTE-radiopeiton rakensi Oulun yliopiston omistama 5G testiverkko, jonka tarkoitus on tukea sekä tutkimusta että ympäröivää ekosysteemiä tulevaisuuden 5G:n kehityksessä

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Machine learning for localization in narrowband IoT networks

Low power wide area networks (LPWANs) are designed for Internet of Things (IoT) appli- cations because of their long-range coverage, low bit rate, and low battery consumption. In the LPWAN networks, Narrow-band IoT (NB-IoT) is a type of network that uses the licensed cellular spectrum, working over the deployed LTE infrastructure. It is rising as a promising technology because of its characteristics and deployment advantages against other LPWAN networks. In NB-IoT networks, localization is an essential service for applications such as smart cities, traffic control, logistics tracking, and others. The outdoor localization is often performed using a Global Navigation Satellite System (GNSS) like Global Positioning System (GPS) to send the current device position with some meters accuracy. However, due to GPS¿s power and size drawbacks, recent reports focus on alternatives to replace GPS-based localization systems with cost and power efficient solutions. This work analyses a database collected over an NB-IoT deployed network in the city of Antwerp in Belgium and implements a solution for outdoor localization based on Machine Learning (ML) methods for distance estimation. The data analysis starts in the pre-processing step, where the databases are cleaned and prepared for the ML analysis. The following process merges and debugs the data to obtain an integrated database with classification for urban and rural areas. The localization solution performs a support vector regression, random forest regression, and a multi-layer perceptron regression using as input parameters the received signal strength indicator (RSSI) and the base station (BS) position details in order to predict the distance to the IoT nodes and estimate the current position (latitude and longitude) of them. This implementation includes hyper-parameter tuning, the train and test process, and mathematical calculations to obtain the estimated position with mean and median location estimation errors expressed in meters. The implementation of the methodology processes results in 280 and 220 meters corre- sponding to the mean and median location errors for the urban area and 920 and 570 meters for the rural area. The accuracy levels obtained in the results turn this solution suitable for the most common uses of localization in IoT instead of using a GPS device. As a result, this study proposes a new approach for localization in IoT networks. In addition to the implemented solution defines valuable research lines to improve the accuracy levels and generate more contributions to optimize the equipment resources and reduce the IoT device¿s final cost.OutgoingObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Low-Power Wide-Area Networks: A Broad Overview of its Different Aspects

Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.publishedVersio