MOBILITY21: Strategic Investments for Transportation Infrastructure & Technology

America's transportation infrastructure is the backbone of our economy. A strong infrastructure means a strong America - an America that competes globally, supports local and regional economic development, and creates jobs. Strategic investments in our transportation infrastructure are vital to our national security, economic growth, transportation safety and our technology leadership. This document outlines critical needs for our transportation infrastructure, identifies new technology drivers and proposes strategic investments for safe and efficient air, ground, rail and marine mobility of people and goods.Comment: A Computing Community Consortium (CCC) white paper, 4 page

Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Crop and animal production techniques have changed significantly over the last century. In the early 1900s, animal power was replaced by tractor power that resulted in tremendous improvements in field productivity, which subsequently laid foundation for mechanized agriculture. While precision agriculture has enabled site-specific management of crop inputs for improved yields and quality, precision livestock farming has boosted efficiencies in animal and dairy industries. By 2020, highly automated systems are employed in crop and animal agriculture to increase input efficiency and agricultural output with reduced adverse impact on the environment. Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. This Issue Paper presents opportunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adoption. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training needs of the workforce who will be involved in the next-generation agriculture

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Sustainable supply chain management in the digitalisation era: The impact of Automated Guided Vehicles

Internationalization of markets and climate change introduce multifaceted challenges for modern supply chain (SC) management in the today's digitalisation era. On the other hand, Automated Guided Vehicle (AGV) systems have reached an age of maturity that allows for their utilization towards tackling dynamic market conditions and aligning SC management focus with sustainability considerations. However, extant research only myopically tackles the sustainability potential of AGVs, focusing more on addressing network optimization problems and less on developing integrated and systematic methodological approaches for promoting economic, environmental and social sustainability. To that end, the present study provides a critical taxonomy of key decisions for facilitating the adoption of AGV systems into SC design and planning, as these are mapped on the relevant strategic, tactical and operational levels of the natural hierarchy. We then propose the Sustainable Supply Chain Cube (S2C2), a conceptual tool that integrates sustainable SC management with the proposed hierarchical decision-making framework for AGVs. Market opportunities and the potential of integrating AGVs into a SC context with the use of the S2C2 tool are further discussed

COVID-19 accelerated digital transformation: The case of Meituan

The outbreak of the COVID-19 pandemic caused disruptions in supply chains. While most existing research suggests ways to improve organizational resilience, our research raises the question of whether the pandemic has accelerated digital transformation at the organizational level. Using the case of Meituan in China, we investigate the supply chain disruptions caused by COVID-19, the strategies taken by Meituan to address the problems and their strategic implications. Starting as a fast food delivery company, Meituan has developed into a leading online shopping platform in China specialized in using digital technologies to provide on-demand delivery services. Our research on Meituan during COVID-19 shows that companies can leverage emerging technologies to improve business models and deliver long-term strategic benefits through digital transformation, rather than focusing solely on improving organizational resilience to reduce uncertainty risk

Developing a Business Ecosystem around Autonomous Vehicle Infrastructure in Indiana

INDOT will soon be embarking on infrastructure planning to accommodate autonomous vehicles. This new technology affords the ability to impact economic value creation across the supply chain in Indiana, as well as foster economic development in Indiana to support these emerging technologies. This proposal will be a first cut towards exploring the development of a strategy to realize this potential. Our proposal will consist of two phases. Phase 1: A focus on industry choices and plans that can inform INDOT choices. Phase 2: A focus on INDOT’s internal decision making, risk tolerance, and choices regarding infrastructure projects

Automation and Robotics in Forest Harvesting Operations: Identifying Near-Term Opportunities

Technology development, in terms of both capability and cost-effective integration, is moving at a fast pace. While advanced robotic systems are already commonplace in controlled workspaces such as factories, the use of remote controlled or autonomous machines in more complex environments, such as for forest operations, is in its infancy. There is little doubt autonomous machinery will play an important role in forest operations in the future. Many machine functions already have the support of automation, and the implementation of remote control of the machine where an operator can operate a piece of equipment, typically in clear line-of sight, at least is commonly available. Teleoperation is where the operator works from a virtual environment with live video and audio feedback from the machine. Since teleoperation provides a similar operator experience to working in the machine, it is relatively easy for an operator to use teleoperation. Autonomous systems are defined by being able to perform certain functions without direct control of a human operator. This paper presents opportunities for remote control, teleoperated machines in forest operations and presents examples of existing developments and ideas from both forestry and other industries. It identified the extraction phase of harvesting as the most logical placement of autonomous machines in the near-term. The authors recognise that, as with all emerging technologies and sectors, there is ample scope for differences in opinions as to what will be commercially successful in the future

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communicatio

From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management

[EN] The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth.This research article is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 737669.Sáiz Rubio, V.; Rovira Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy. 10(2):1-21. https://doi.org/10.3390/agronomy10020207S121102Himesh, S. (2018). Digital revolution and Big Data: a new revolution in agriculture. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 13(021). doi:10.1079/pavsnnr201813021Digital Agriculture: Improving Profitabilityhttps://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/digital_3/accenture-digital-agriculture-point-of-view.pdfDigital Farming: What Does It Really Mean?http://www.cema-agri.org/publication/digital-farming-what-does-it-really-meanAgriculture Needs to Attract More Young Peoplehttp://www.gainhealth.org/knowledge-centre/worlds-farmers-age-new-blood-neededGenerational Renewalhttps://enrd.ec.europa.eu/enrd-thematic-work/generational-renewal_enWhat is IoT in Agriculture? Farmers Aren’t Quite Sure Despite $4bn US Opportunity—Reporthttps://agfundernews.com/iot-agriculture-farmers-arent-quite-sure-despite-4bn-us-opportunity.htmlPrecision Agriculture Yields Higher Profits, Lower Riskshttps://www.hpe.com/us/en/insights/articles/precision-agriculture-yields-higher-profits-lower-risks-1806.htmlTzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31-48. doi:10.1016/j.biosystemseng.2017.09.007From Dirt to Data: The Second Green Revolution and IoT. Deloitte insightshttps://www2.deloitte.com/insights/us/en/deloitte-review/issue-18/second-green-revolution-and-internet-of-things.html#endnote-sup-9Big Data: The Next Frontier for Innovation, Competition, and Productivity | McKinseyhttps://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovationWolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037How Big Data Will Change Agriculturehttps://proagrica.com/news/how-big-data-will-change-agriculture/Big Data Coordination Platform. Proposal to the CGIAR Fund Councilhttps://cgspace.cgiar.org/handle/10947/4303Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs. Processes, 7(1), 36. doi:10.3390/pr7010036How AI Is Transforming Agriculturehttps://www.forbes.com/sites/cognitiveworld/2019/07/05/how-ai-is-transforming-agriculture/Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. doi:10.1016/j.biosystemseng.2016.06.014Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems Engineering, 153, 110-128. doi:10.1016/j.biosystemseng.2016.11.004Ramin Shamshiri, R., Weltzien, C., A. Hameed, I., J. Yule, I., … E. Grift, T. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1-11. doi:10.25165/j.ijabe.20181104.4278Farming 4.0: The Future of Agriculture?https://www.euractiv.com/section/agriculture-food/infographic/farming-4-0-the-future-of-agriculture/Ag Tech Deal Activity More Than Tripleshttps://www.cbinsights.com/research/agriculture-farm-tech-startup-funding-trends/AI, Robotics, And the Future of Precision Agriculturehttps://www.cbinsights.com/research/ai-robotics-agriculture-tech-startups-future/VineScout European Projectwww.vinescout.euPrecision Farming: A New Approach to Crop Managementhttp://agpublications.tamu.edu/pubs/eng/l5177.pdfZhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. doi:10.1016/s0168-1699(02)00096-0MIAO, Y., MULLA, D. J., & ROBERT, P. C. (2018). An integrated approach to site-specific management zone delineation. Frontiers of Agricultural Science and Engineering, 0(0), 0. doi:10.15302/j-fase-2018230Klassen, S. P., Villa, J., Adamchuk, V., & Serraj, R. (2014). Soil mapping for improved phenotyping of drought resistance in lowland rice fields. Field Crops Research, 167, 112-118. doi:10.1016/j.fcr.2014.07.007Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22-32. doi:10.1016/j.compag.2017.05.001Aravind, K. R., Raja, P., & Pérez-Ruiz, M. (2017). Task-based agricultural mobile robots in arable farming: A review. Spanish Journal of Agricultural Research, 15(1), e02R01. doi:10.5424/sjar/2017151-9573Roldán, J. J., Cerro, J. del, Garzón‐Ramos, D., Garcia‐Aunon, P., Garzón, M., León, J. de, & Barrientos, A. (2018). Robots in Agriculture: State of Art and Practical Experiences. Service Robots. doi:10.5772/intechopen.69874Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Lopez-Granados, F., Brandstoetter, M., Tomic, S., … Debilde, B. (2016). Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 18(4), 574-614. doi:10.1007/s11119-016-9476-3What’s Slowing the Use of Robots in the Ag Industry?https://www.therobotreport.com/whats-slowing-the-use-of-robots-in-the-ag-industry/Bogue, R. (2016). Robots poised to revolutionise agriculture. Industrial Robot: An International Journal, 43(5), 450-456. doi:10.1108/ir-05-2016-0142Features & Benefits OZ Weeding Robothttps://www.naio-technologies.com/en/agricultural-equipment/weeding-robot-oz/Robotics for Sustainable Broad-Acre Agriculturehttps://www.researchgate.net/publication/283722961_Robotics_for_Sustainable_Broad-Acre_AgricultureThe Ultimate Guide to Agricultural Roboticshttps://www.roboticsbusinessreview.com/agriculture/the_ultimate_guide_to_agricultural_robotics/Kweon, G., Lund, E., & Maxton, C. (2013). Soil organic matter and cation-exchange capacity sensing with on-the-go electrical conductivity and optical sensors. Geoderma, 199, 80-89. doi:10.1016/j.geoderma.2012.11.001Agricultural Robots—Present and Future Applications (Videos Included)https://emerj.com/ai-sector-overviews/agricultural-robots-present-future-applications/Köksal, Ö., & Tekinerdogan, B. (2018). Architecture design approach for IoT-based farm management information systems. Precision Agriculture, 20(5), 926-958. doi:10.1007/s11119-018-09624-8Rovira-Más, F., & Sáiz-Rubio, V. (2013). Crop Biometric Maps: The Key to Prediction. Sensors, 13(9), 12698-12743. doi:10.3390/s130912698Oliver, M. A., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. CATENA, 113, 56-69. doi:10.1016/j.catena.2013.09.006Adamchuk, V. ., Hummel, J. ., Morgan, M. ., & Upadhyaya, S. . (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71-91. doi:10.1016/j.compag.2004.03.002Cossell, S., Whitty, M., Liu, S., & Tang, J. (2016). Spatial Map Generation from Low Cost Ground Vehicle Mounted Monocular Camera. IFAC-PapersOnLine, 49(16), 231-236. doi:10.1016/j.ifacol.2016.10.043N. Zhang, & R. K. Taylor. (2001). APPLICATIONS OF A FIELD LEVEL GEOGRAPHIC INFORMATION SYSTEM (FIS) IN PRECISION AGRICULTURE. Applied Engineering in Agriculture, 17(6). doi:10.13031/2013.6829Runquist, S., Zhang, N., & Taylor, R. K. (2001). Development of a field-level geographic information system. Computers and Electronics in Agriculture, 31(2), 201-209. doi:10.1016/s0168-1699(00)00155-1Granular Farm Management Software, Precision Agriculture, Agricultural Softwarehttps://granular.ag/Capterra. Farm Management Softwarewww.capterra.comTop 9 Farm Management Software—Compare Reviews, Features, Pricing in 2019https://www.predictiveanalyticstoday.com/top-farm-management-software/Srivastava, P. K., & Singh, R. M. (2016). GIS based integrated modelling framework for agricultural canal system simulation and management in Indo-Gangetic plains of India. Agricultural Water Management, 163, 37-47. doi:10.1016/j.agwat.2015.08.025Giusti, E., & Marsili-Libelli, S. (2015). A Fuzzy Decision Support System for irrigation and water conservation in agriculture. Environmental Modelling & Software, 63, 73-86. doi:10.1016/j.envsoft.2014.09.020Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., … Quaife, T. (2018). TAMSAT-ALERT v1: a new framework for agricultural decision support. Geoscientific Model Development, 11(6), 2353-2371. doi:10.5194/gmd-11-2353-2018https://dssat.netNavarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., & Torres-Sánchez, R. (2016). A decision support system for managing irrigation in agriculture. Computers and Electronics in Agriculture, 124, 121-131. doi:10.1016/j.compag.2016.04.003Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596-609. doi:10.1016/j.rser.2016.11.191Rupnik, R., Kukar, M., Vračar, P., Košir, D., Pevec, D., & Bosnić, Z. (2019). AgroDSS: A decision support system for agriculture and farming. Computers and Electronics in Agriculture, 161, 260-271. doi:10.1016/j.compag.2018.04.001Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., … Dicks, L. V. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165-174. doi:10.1016/j.agsy.2016.09.009Colaço, A. F., & Molin, J. P. (2016). Variable rate fertilization in citrus: a long term study. Precision Agriculture, 18(2), 169-191. doi:10.1007/s11119-016-9454-9Nawar, S., Corstanje, R., Halcro, G., Mulla, D., & Mouazen, A. M. (2017). Delineation of Soil Management Zones for Variable-Rate Fertilization. Advances in Agronomy, 175-245. doi:10.1016/bs.agron.2017.01.003Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., … Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50. doi:10.1016/j.compag.2015.05.011Precision Agriculture in Europe: Legal, Social and Ethical Considerations—Think Tankhttp://www.europarl.europa.eu/thinktank/en/document.html?reference=EPRS_STU(2017)60320

The Evolution of 5G: Delineating the Impact and Limitations across Transportation, Education, Healthcare, Agriculture, and Manufacturing

A world previously only thought possible in science fiction is about to become a reality. A world where doctors can operate on patients thousands of miles away. A world where students can experience ancient cities and distant galalike they are physically there. A world of fully self-driving cars. A world where every step of the supply chain is automated. A world where factories have a handful of employees overseeing robots handling the entire manufacturing process. A world of fully autonomous farms, where one farmer can manage an entire farm from seed to harvest from their smartphone. The development, implementation, and adoption of 5G cellular networks will make this world a possibility. 5G is the fifth generation of cellular networks. It is the successor of the current fourth generation (4G) networks. 5G technology is characterized by ultra-low latency, massive data rates, near perfect reliability, extreme density of connection, and wide coverage areas. 5G is not just another “G”, it has the potential to completely disrupt the way we work and live (Binney, 2020). 5G networks will impact the world in an almost infinite number of ways. Business models will change, the way people work will change, the way students learn will change, the way patients get health care will change, the way people get their food will change, the way people drive will change. In this thesis, I examine the development, applications, benefits, and socioeconomic impacts of 5G technology, as well as current limitations facing the industry and ways to address them