Master of Agribusiness / Department of Agricultural Economics / Terry Griffin / Large acreage farms and even moderate sized farms employing custom applicators and harvesters have multiple machines in the same field at the same time conducting the same field operation. As a method to control input costs and minimize application overlap, these machines have been equipped with automatic section control (ASC). For nearly all these multiple-vehicle operations, over application is a concern especially for more irregularly shaped fields; however modern technology including automated guidance combined with automatic section control allow reduced doubling of input application including seeds, fertilizer, and spray. Automatic section control depends on coverage maps stored locally on each vehicle to determine whether or not to apply input products and up to now, there has not been a clear method to share these maps between vehicles in the same field. Without sharing coverage maps, an individual ASC planting unit only has location data where it has applied individually and no location data for where other planting units have applied seed in that same field. Automatic section control relies upon shared coverage maps to be continually updated between each planting unit and utilizes existing machine telematics infrastructure for map data sharing. Telematics utilizes a cloud computing platform and cellular connectivity which in rural areas is known to have limited service levels. Planting operations were simulated for two 16-row planters, each using two John Deere GreenStar3 2630 monitors, simulated GPS location data stream, electronic rate control units, and individual row unit clutches to have control at the finest granularity. Each simulated planting unit is equipped with automatic section control and telematics gateways to share coverage map data from the first planting unit to JDLink cloud infrastructure then out to the second. This study evaluates the impact that field size and shape have on using multiple ASC planters and coverage map sharing, and estimates seed cost savings from reducing over application because coverage maps are shared between planting units. The impact of sharing coverage maps with both planting units using field boundaries with automatic section control and without using field boundaries were evaluated. Guidance line headings were determined using AgLeader SMS’s mission planning feature to minimize the number of passes across each field based on the field boundary and implement width. Each field was run twice using parallel tracking, once each with and without coverage map sharing to observe the extent of over application. The field level data were then taken to examine a fictious 3,000 acre farming operation where the field level data was used as a partial composition of the farm operation. An embedded Microsoft Excel macro was used to create 8,008 different composition scenarios to determine farm level savings. The average farm savings was $58,909 per year. Additionally, using the 8,008 scenarios, time value of money was examined to determine the the minimum area required annually for five years for this technology to pay back. The average was 133 acres each year for five years. Equipment manufacturers and farmers have interest in these results. In general, equipment manufacturers desire to create a service-based product to be sold such that continual revenue path provides value added services after the precision agriculture hardware is sold. In this study, the existing telematics product offerings are tied to shared coverage maps to provide a value-add to an existing service. Farmers want to ensure this is a sound equipment investment with payback in a relatively short time period. As farm input costs continue to rise especially relative to crop prices, reducing over application will be critical to limit waste.

Předmětem této práce je polohově orientovaná analýza v kontextu optimalizace mobilních sítí. Popisuje nástroj pro odhadování základních parametrů sítě na místech s neznámými parametry sítě na základě databáze RTR NetTest. Je zde stručně představena oblast velkých dat, strojového učení a shrnutí o konceptu a funkcionalitě aplikace NetTest. Práce ukazuje a porovnává skupinu regresních metod na základě jejich komplexnosti a vhodnosti pro vytvoření map odhadovaných parametrů sítě. Po jejich důkladné 1D analýze je IDW a GPR analyzováno ve 2D a využito pro vytvoření skupiny map odhadu parametrů sítě. Je posouzena i jejich přesnost na základě referenčního měření aplikací NetTest.

The emergence of unmanned aerial vehicles (UAVs) along with the implementation of 5G networks offers exciting opportunities in expanding wireless capabilities. Not only is improved wireless performance expected with traditional devices such as mobile phones, but new use cases such as the internet-of-things and autonomous vehicle operation will rely on 5G and future network generations. In such widespread applications, from transportation to vital business operation, reliable and often guaranteed connectivity is required for safety and commercial approval. Introducing UAVs into network processes has been explored and implemented in certain instances to take advantage of the flexibility drone devices offer in their mobility and control to address these evolving network possibilities. While practical UAV deployment in certain network cases has been demonstrated, including coverage restoration in disaster relief scenarios, more ambitious goals of 5G will have additional considerations. This includes autonomous vehicles (AVs) whose operation is defined by levels representing varying degrees of autonomy. With computational requirements exponentially increasing as a vehicle's autonomy level is increased, 5G is expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment. This thesis proposes an UAV based measurement system that would provide coverage verification employing a platform capable of precise RF measurements and enhanced spatial sampling of the environment. Methods employed to traditionally characterize available coverage, including cellular drive tests, do not result in accurate enough measurements for AV use cases. Where lack of coverage in common network processes and use cases can result in dropped calls and poor connectivity in mobile devices, autonomous systems proposed in evolving network generations that deal with safety and mission critical functions must have guaranteed and verified coverage. Data produced in this thesis demonstrates that the proposed UAV based measurement system will improve measurement accuracy and enhanced geographic performance over conventional automotive vehicle based measurement systems / Master of Science / Wireless networks have grown to support vital and everyday processes in modern society. The COVID-19 pandemic proved wireless communication means a necessity to limit daily disruptions, but networks had already been supporting a continuously increasing amount of mobile devices prior to this. Other demonstrations of wireless network capacity include the release of 5G technology, allowing improved performance with traditional devices like smartphones, along with additional use cases this technology enables including the internet-of-things (IoT) and artificial intelligence (AI) leveraged functions for commercial applications. While wireless network capabilities have demonstrated their success in supporting and maintaining some critical functions, it is important to continually look ahead and plan for future network implementations in order to develop and support all desired advancements. Current measurement methods that assist in verifying coverage for current use cases like mobile devices will fall short in verification for more stringent requirements characteristic of AV and other ambitious network goals. The results found in this work then support the need for continuing research of a UAV-leveraged platform in the scope of eventual practical and safe AV integration into society. The focus of this thesis is to then propose and provide initial evaluation of a UAV-leveraged measurement platform to verify the operability of autonomous vehicles (AVs), which are expected to be a major aspect of future network processes. The computational requirements to operate an autonomous vehicle exponentially increase as a vehicle's autonomy level is increased. 5G is then expected to play an integral role in offloading certain vehicle tasks to the cloud. This thesis paper then proposes UAV based measurement opportunities as a method to characterize 5G coverage as part of autonomous vehicle processes to identify the proper level of autonomy that can operate safely given the current RF environment.

This thesis presents/offers a comprehensive view of the issue of mobile Internet aimed at very small businesses and traders. In my thesis I focus on the practical applicability of mobile Internet by means of mobile technologies in the forenamed undertakings. First part of the thesis deals with the methodology of the area of very small businesses and traders and analyses present possibilities of application of mobile technologies and their suitability and usability for very small businesses and traders. Second part of the thesis identifies types of undertaking that can take advantage of mobile Internet and shows that this technology entails time and funds saving. Third part demonstrates concrete applications of mobile Internet in the activities of undertakings and presents the possibilities of gaining a support from the European Union structural funds for the implementation of mobile Internet.

L'un des challenges le plus important pour les opérateurs des réseaux mobiles est de dimensionner le réseau de la manière la plus efficace possible, c'est-à-dire, bien planifier les ressources pour fournir une bonne couverture et une meilleure capacité. Afin de mieux gérer le trafic généré dans le réseau, les opérateurs déploient des petites cellules pour aider les macro-cellules à délester les zones de la cellule où le trafic de données est significativement supérieur au trafic moyen dans la cellule. Cependant, le problème majeur de ces réseaux hétérogènes consiste à bien localiser ces hotspots et puis de mettre en place la meilleure solution pour les absorber. Dans cette thèse, on traite le sujet de localisation de hotspot et on étudie l'impact de son imperfection sur les performances des déploiements des réseaux hétérogènes. Dans une première étape, on propose une nouvelle méthode de localisation de hotspot. Puis, on propose d'évaluer l'impact d'une mauvaise localisation de trafic sur le déploiement des petites cellules à travers une analyse de performances au niveau statique et dynamique. Sachant qu'une grande quantité de trafic de données dans le réseau mobile est générée par des utilisateurs qui sont en mouvement, on propose d'évaluer les performances des petites cellules mobiles. Enfin, la quatrième étape consiste à améliorer encore la solution de déploiement de petites cellules en utilisant les drones. On propose un mécanisme de décongestion du réseau et on discute les avantages et les points à explorer. Une analyse de performance est aussi réalisée pour comparer cette solution avec les solutions classiques de macro cellules et de petites cellules / The continuous increasing traffic in cellular networks has forced the mobile operators to look for efficient and viable options to manage their networks so as to ensure more efficiency over the network life cycle while also evolving with the implementation of new technologies. Traffic hotspot localization can help operators to identify the areas where deploying small cells can reduce the congestion. We firstly propose and assess a new traffic hotspot localization method based on the projection of O&M KPIs on the coverage map. Compared to probing methods, the computational costs and the equipment expenditures are reduced. Moreover, the localization accuracy is improved. Next, in order to evaluate the impact of the limited accuracy of traffic localization tools on small cell deployment, we study the performances of three different scenarios. The first one considers a network of macrocells only and represents a benchmark to decide about the usefulness of small cells. The second one is based on a network of macrocells with a perfectly deployed small cell allowing to identify the limitations of small cell deployment and the last one is with an imperfectly deployed small cell. Realizing that a significant amount of cellular demand is generated on the go and suffers deteriorating quality, we investigate the potential performance gains of using moving small cells. The major outcome is to understand if moving cells leverage the relative operators' investments. Finally, we propose a new mobile data offloading mechanism which capitalizes on drone small cells to alleviate the data traffic load. We realize a performance evaluation and comparison with classic small cell deployment