KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35m and 0.37m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles

Smart and Intelligent Automation for Industry 4.0 using Millimeter-Wave and Deep Reinforcement Learning

Innovations in communication systems, compute hardware, and deep learning algorithms have led to the advancement of smart industry automation. Smart automation includes industrial sectors such as intelligent warehouse management, smart infrastructure for first responders, and smart monitoring systems. Automation aims to maximize efficiency, safety, and reliability. Autonomous forklifts can significantly increase productivity, reduce safety-related accidents, and improve operation speed to enhance the efficiency of a warehouse. Forklifts or robotic agents are required to perform different tasks such as position estimation, mapping, and dispatching. Each of the tasks involves different requirements and design constraints. Smart infrastructure for first responder applications requires robotic agents like Unmanned Aerial Vehicles (UAVs) to provide situation awareness surrounding an emergency. An immediate and efficient response to a safety-critical situation is crucial, as a better first response significantly impacts the safety and recovery of parties involved. But these UAVs lack the computational power required to run Deep Neural Networks (DNNs) that are used to provide the necessary intelligence. In this dissertation, we focus on two applications in smart industry automation. In the first part, we target smart warehouse automation for Intelligent Material Handling (IMH), where we design an accurate and robust Machine Learning (ML) based indoor localization system for robotic agents working in a warehouse. The localization system utilizes millimeter-wave (mmWave) wireless sensors to provide feature information in the form of a radio map which the ML model uses to learn indoor positioning. In the second part, we target smart infrastructure for first responders, where we present a computationally efficient adaptive exit strategy in multi-exit Deep Neural Networks using Deep Reinforcement Learning (DRL). The proposed adaptive exit strategy provides faster inference time and significantly reduces computations

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

WLAN-paikannuksen elinkaaren tukeminen

The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information. For the purposes of indoor positioning, however, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors. Arriving around the same time, wireless local area networks (WLAN) have gained widespread support both in terms of infrastructure deployments and client proliferation. A promising approach to bridge the location context then has been positioning based on WLAN signals. In addition to being readily available in most environments needing support for location information, the adoption of a WLAN positioning system is financially low-cost compared to dedicated infrastructure approaches, partly due to operating on an unlicensed frequency band. Furthermore, the accuracy provided by this approach is enough for a wide range of location-based services, such as navigation and location-aware advertisements. In spite of this attractive proposition and extensive research in both academia and industry, WLAN positioning has yet to become the de facto choice for indoor positioning. This is despite over 20 000 publications and the foundation of several companies. The main reasons for this include: (i) the cost of deployment, and re-deployment, which is often significant, if not prohibitive, in terms of work hours; (ii) the complex propagation of the wireless signal, which -- through interaction with the environment -- renders it inherently stochastic; (iii) the use of an unlicensed frequency band, which means the wireless medium faces fierce competition by other technologies, and even unintentional radiators, that can impair traffic in unforeseen ways and impact positioning accuracy. This thesis addresses these issues by developing novel solutions for reducing the effort of deployment, including optimizing the indoor location topology for the use of WLAN positioning, as well as automatically detecting sources of cross-technology interference. These contributions pave the way for WLAN positioning to become as ubiquitous as the underlying technology.GPS-paikannus avattiin julkiseen käyttöön vuosituhannen vaihteessa, jonka jälkeen sitä on voinut käyttää sijainnin paikantamiseen ulkotiloissa kaikkialla maailmassa. Sisätiloissa GPS-signaali kuitenkin harvoin läpäisee rakennuksia kyllin hyvin voidakseen tarjota vastaavaa paikannustarkkuutta. Langattomat lähiverkot (WLAN), mukaan lukien tukiasemat ja käyttölaitteet, yleistyivät nopeasti samoihin aikoihin. Näiden verkkojen signaalien käyttö on siksi alusta asti tarjonnut lupaavia mahdollisuuksia sisätilapaikannukseen. Useimmissa ympäristöissä on jo valmiit WLAN-verkot, joten paikannuksen käyttöönotto on edullista verrattuna järjestelmiin, jotka vaativat erillisen laitteiston. Tämä johtuu osittain lisenssivapaasta taajuusalueesta, joka mahdollistaa kohtuuhintaiset päätelaitteet. WLAN-paikannuksen tarjoama tarkkuus on lisäksi riittävä monille sijaintipohjaisille palveluille, kuten suunnistamiselle ja paikkatietoisille mainoksille. Näistä lupaavista alkuasetelmista ja laajasta tutkimuksesta huolimatta WLAN-paikannus ei ole kuitenkaan pystynyt lunastamaan paikkaansa pääasiallisena sisätilapaikannusmenetelmänä. Vaivannäöstä ei ole puutetta; vuosien saatossa on julkaistu yli 20 000 tieteellistä artikkelia sekä perustettu useita yrityksiä. Syitä tähän kehitykseen on useita. Ensinnäkin, paikannuksen pystyttäminen ja ylläpito vaativat aikaa ja vaivaa. Toiseksi, langattoman signaalin eteneminen ja vuorovaikutus ympäristön kanssa on hyvin monimutkaista, mikä tekee mallintamisesta vaikeaa. Kolmanneksi, eri teknologiat ja laitteet kilpailevat lisenssivapaan taajuusalueen käytöstä, mikä johtaa satunnaisiin paikannustarkkuuteen vaikuttaviin tietoliikennehäiriöihin. Väitöskirja esittelee uusia menetelmiä joilla voidaan merkittävästi pienentää paikannusjärjestelmän asennuskustannuksia, jakaa ympäristö automaattisesti osiin WLAN-paikannusta varten, sekä tunnistaa mahdolliset langattomat häiriölähteet. Nämä kehitysaskeleet edesauttavat WLAN-paikannuksen yleistymistä jokapäiväiseen käyttöön

Fly, Wake-up, Find: UAV-based Energy-efficient Localization for Distributed Sensor Nodes

A challenging application scenario in the field of industrial Unmanned Aerial Vehicles (UAVs) is the capability of a robot to find and query smart sensor nodes deployed at arbitrary locations in the mission area. This work explores the combination of different communication technologies, namely, Ultra-Wideband (UWB) and Wake-Up Radio (WUR), with a UAV that acts as a "ubiquitous local-host"of a Wireless Sensor Network (WSN). First, the UAV performs the localization of the sensor node via multiple UWB range measurements, and then it flies in its proximity to perform energy-efficient data acquisition. We propose an energy-efficient and accurate localization algorithm - based on multi-lateration - that is computationally inexpensive and robust to in-field noise. Aiming at minimizing the sensor node energy consumption, we also present a communication protocol that leverages WUR technology to minimize ON-time of the power-hungry UWB transceiver on the sensors. In-field experimental evaluation demonstrates that our approach achieves a sub-meter localization precision of the sensor nodes - i.e., down to 0.6 m - using only three range measurements, and runs in 4 ms on a low power microcontroller (ARM Cortex-M4F). Due to the presence of the WUR and the proposed lightweight algorithm, the entire localization-acquisition cycle requires only 31 mJ on the sensor node. The approach is suitable for several emerging Industrial Internet of Things application scenarios where a mobile vehicle needs to estimate the location of static objects without any precise knowledge of their position

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

Etäisyyden huomioiva kaksiulotteinen viivakoodi mobiilikäyttötapauksiin

Global internet use is becoming increasingly mobile, and mobile data usage is growing exponentially. This puts increasing stress on the radio frequency spectrum that cellular and Wi-Fi networks use. As a consequence, research has also been conducted to develop wireless technologies for other parts of the electromagnetic spectrum – namely, visible light. One approach of using the visible light channel for wireless communication leverages barcodes. In this thesis, we propose a 2D barcode that can display different information based on the distance between the barcode and the scanner. Earlier research on distance-sensitive barcodes has focused on providing a closer viewer more information as a closer viewer can see more detail. In contrast, we target use cases where a clear physical separation between users of different roles can be made, such as presentation systems. We evaluate two methods of achieving distance-awareness: color-shifting of individual colors, where a color changes tone at longer distances, and color blending, where two colors blend into a third color at longer viewing distances. Our results show that a modern smartphone is capable of leveraging color-shifting in ideal conditions, but external changes such as ambient lighting render color-shifting unusable in practical scenarios. On the other hand, color blending is robust in varying indoor conditions and can be used to construct a reliable distance-aware barcode. Accordingly, we employ color blending to design a distance-aware barcode. We implement our solution in an off-the-shelf Android smartphone. Experimental results show that our scheme achieves a clear separation between close and far viewers. As a representative use case, we also implement a presentation system where a single barcode provides the presenter access to presentation tools and the audience access to auxiliary presentation material.Maailmanlaajuinen internetin käyttö muuttuu yhä liikkuvammaksi, ja mobiilidatan käyttö kasvaa eksponentiaalisesti. Tämä kohdistaa yhä suurempia vaatimuksia radiotaajuusspektriin, jota mobiili- ja Wi-Fi-verkot käyttävät. Näin ollen tutkijat ovat kehittäneet langattomia teknologioita hyödyntäen myös muita sähkömagneettisen spektrin osia – erityisesti näkyvää valoa. Yksi näkyvän valon sovellus langattomassa viestinnässä ovat viivakoodit. Tässä työssä kehitämme kaksiulotteisen viivakoodin, joka pystyy välittämään eri tietoa katselijoille eri etäisyyksillä. Aiempi etäisyyden huomioivien viivakoodien tutkimus on keskittynyt tarjoamaan lähellä olevalle katselijalle enemmän tietoa, koska läheinen katselija näkee viivakoodin tarkemmin. Sitä vastoin me keskitymme käyttötapauksiin, joissa eri käyttäjäroolien välillä on selkeä etäisyydellinen ero, kuten esimerkiksi esitelmissä puhujan ja yleisön välillä. Tarkastelemme kahta menetelmää: yksittäisten värien muutoksia etäisyyden muuttuessa ja kahden värin sekoittumista etäisyyden kasvaessa. Tulostemme perusteella nykyaikainen älypuhelin pystyy hyödyntämään yksittäisten värien muutoksia ihanteellisissa olosuhteissa, mutta ulkoiset tekijät, kuten ympäristön valaistus, aiheuttavat liian suuria värimuutoksia käytännön käyttötapauksissa. Toisaalta värien sekoittuminen on johdonmukaista muuttuvassa sisäympäristössä ja sitä voidaan käyttää luotettavan viivakoodin luomisessa. Näin ollen me suunnittelemme etäisyyden huomioivan viivakoodin hyödyntäen värien sekoittumista. Toteutamme ratkaisumme yleisesti saatavilla olevalle Android-älypuhelimelle. Kokeellisten tulostemme perusteella menetelmämme saavuttaa selkeän erottelun läheisten ja kaukaisten katselijoiden välillä. Esimerkkikäyttötapauksena toteutamme myös esitelmäjärjestelmän, jossa sama viivakoodi antaa lähellä olevalle puhujalle nopean pääsyn esitystyökaluihin ja kauempana olevalle yleisölle pääsyn esityksen apumateriaaliin

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering