Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

Impact of prescribed burning for oak regeneration on forest vegetation, white-footed mouse populations, and Lyme disease

In 2009 and 2010, we investigated the impacts of prescribed burns for oak forest management on understory vegetation, white-footed mouse (Peromyscus leucopus) abundance and survival, and questing blacklegged tick (Ixodes scapularis) abundance and tick attachment load. In 2010, understory vegetation diversity and richness significantly decreased as percent burn increased. White-footed mouse abundance and survival were not different between burned and unburned sites; however, abundance was significantly higher in 2009. Attached tick load was significantly higher in 2010, but did not differ between burned and unburned sites. However, tick load increased 3.5 fold in burned sites between years while tick load increased 8 fold in the unburned area. Questing tick abundance was significantly lower in the burned area than the unburned area in both years. These results could assist land managers in developing multipurpose approaches for using prescribed burning for oak forest management

Assessment of postural, locomotor, and physical fitness status in individuals with intellectual and developmental disabilities

Introduction: Postural control and locomotion deficits can be observed during the early years of childhood development and throughout life. For those with disabilities, these deficits can advance past the development years and into adolescence and adulthood while affecting the quality of life and daily activity. Finding interactive rehabilitative activities to delay or limit these deficits is essential for people with disabilities to improve their quality of life, inclusion, and overall movement. Adapted physical activity/sports like badminton and virtual reality could promote improvements in postural and locomotor status for young adults with intellectual and developmental disabilities like cerebral palsy (CP), intellectual disability (ID), and autism spectrum disorder (ASD). Purpose: These studies aim to assess the postural and locator status of young adults with intellectual and developmental disabilities after participating in a 12-week badminton and intensive virtual reality programs. Methods: Study A will follow a multiple baseline approach to access postural control, locomotion, and areas of physical fitness in young adults with IDD utilizing the immersive virtual reality game Fruit Ninja™ while study B will follow and repeated measures design accessing static postural control for students in a comprehensive transition program for intellectual disabilities at a southeastern university

Inferring the photometric and size evolution of galaxies from image simulations

Current constraints on models of galaxy evolution rely on morphometric catalogs extracted from multi-band photometric surveys. However, these catalogs are altered by selection effects that are difficult to model, that correlate in non trivial ways, and that can lead to contradictory predictions if not taken into account carefully. To address this issue, we have developed a new approach combining parametric Bayesian indirect likelihood (pBIL) techniques and empirical modeling with realistic image simulations that reproduce a large fraction of these selection effects. This allows us to perform a direct comparison between observed and simulated images and to infer robust constraints on model parameters. We use a semi-empirical forward model to generate a distribution of mock galaxies from a set of physical parameters. These galaxies are passed through an image simulator reproducing the instrumental characteristics of any survey and are then extracted in the same way as the observed data. The discrepancy between the simulated and observed data is quantified, and minimized with a custom sampling process based on adaptive Monte Carlo Markov Chain methods. Using synthetic data matching most of the properties of a CFHTLS Deep field, we demonstrate the robustness and internal consistency of our approach by inferring the parameters governing the size and luminosity functions and their evolutions for different realistic populations of galaxies. We also compare the results of our approach with those obtained from the classical spectral energy distribution fitting and photometric redshift approach.Our pipeline infers efficiently the luminosity and size distribution and evolution parameters with a very limited number of observables (3 photometric bands). When compared to SED fitting based on the same set of observables, our method yields results that are more accurate and free from systematic biases.Comment: 24 pages, 12 figures, accepted for publication in A&

Three Dimensional Visualization of Fire Spreading Over Forest Landscapes

Previous studies in fire visualization have required high end computer hardware and specialized technical skills. This study demonstrated fire visualization is possible using Visual Nature Studio and standard computer hardware. Elevation and vegetation data were used to create a representation of the New Jersey pine barren environment and a forest compartment within Hobcaw Barony. Photographic images were edited to use as image object models for forest vegetation. The FARSITE fire behavioral model was used to model a fire typical of that area. Output from FARSITE was used to visualize the fire with tree models edited to simulate burning and flame models. Both static and animated views of the fire spread and effects were visualized. The two visualization methods were compared for advantages and disadvantages. VNS visualizations were more realistic, including many effects such as ground textures, lighting, user made models, and atmospheric effects. However the program had higher hardware requirements and sometimes rendered images slowly. ArcScene had lower hardware requirements and produced visualizations with real time movement. The resulting images lacked many of the effects found in VNS and were more simplistic looking

Non-Traditional Entrants to the Profession of Teaching: Motivations and Experiences of Second-Career Educators

Individuals entering the teaching profession from other fields have much to offer in terms of practical experience in fields such as business, private industry, law enforcement, and the military. The authors examine the phenomenon of second-career teachers with specific emphasis on the motivating factors, common experiences, and reflections concerning the decision to leave already established professions in order to pursue a career in teaching. This particular issue of life vocation has particular interest for the Christian, who wishes to discern and follow the call of God in their personal and professional commitments. The article surveys research into second-career teachers and explores the process of professional preparation of these newfound entrants in the world of education as well as their assimilation to the culture of schools. Themes emerge from the literature for further consideration, which the authors argue, must more intentionally-inform career preparation programs, trends of new teacher recruitment, and teacher shortages in American school districts

Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

Imperfect Maintenance Models: Simulation, Estimation, and Goodness-of-Fit Test

The industrial facilities are subject to material maintenance operations in order to keep them in good working condition. Maintenance, contributing through its effects on the operational reliability of materials, plays an important role in risk control. Therefore, it is necessary to model the process of the failures and the maintenance of the complex systems using random point process, called imperfect maintenance models. Before applying the models proposed, it is important to have the methods to choose the fittest model for each dataset. In order to that, we have to do a statistical test, called goodness-of-fit test. In this research, we construct the method of goodness-of-fit test for one of the imperfect maintenance model, namely ARA1-LLP. First, we simulate the dataset based on several imperfect maintenance models using inverse transform sampling. Then, we estimate the model parameters using maximum likelihood estimation (MLE). All the methods are implemented via Rcpp programming, which is highly faster than R, based on benchmark comparison. The test uses four test statistics, namely Laplace, modified Cramer-Von Mises, modified Anderson-Darling, and modified Kolmogorov-Smirnov. The simulation results show that modified Kolmogorov-Smirnov performs well for detecting the true model and Anderson-Darling has the highest power of test. ================================================================= Fasilitas di dalam industri bergantung pada operasi pemeliharaan material dalam rangka untuk menjaga kondisinya dalam keadaan baik. Proses pemeliharaan fasilitas industri memiliki peran penting dalam pengendalian risiko. Oleh karena itu, perlu untuk membuat pemodelan proses kegagalan dan pemeliharaan sistem yang kompleks menggunakan random point process, yang disebut sebagai model pemeliharaan yang tidak sempurna. Sebelum menerapkan suatu model, sangat penting untuk memiliki metode untuk memilih model terbaik untuk setiap data. Oleh karena itu, perlu dilakukan suatu uji statistik, yang disebut uji goodness-of-fit. Dalam penelitian ini, dirancang suatu metode uji goodness-of-fit pada salah satu model pemeliharaan tidak sempurna, yaitu ARA1-LLP. Langkah pertama, kita membangkitkan data berdasarkan beberapa model pemeliharaan tidak sempurna menggunakan metode inverse transform sampling. Kemudian, dilakukan estimasi parameter model menggunakan metode maximum likelihood estimation (MLE). Semua metode diimplementasikan melalui Rcpp programming, yang jauh lebih cepat daripada R programming, berdasarkan hasil uji benchmark. Uji ini menggunakan 4 statistik uji, yaitu Laplace, modified Cramer-Von Mises, modified Anderson-Darling dan modified Kolmogorov-Smirnov. Hasil simulasi menunjukkan bahwa statistik modified Kolmogorov-Smirnov bekerja dengan baik untuk mendeteksi model yang benar dan statistik modified Anderson-Darling memiliki kekuatan uji yang tertinggi

Expansion of Canopy-Forming Willows Over the Twentieth Century on Herschel Island, Yukon Territory, Canada

Canopy-forming shrubs are reported to be increasing at sites around the circumpolar Arctic. Our results indicate expansion in canopy cover and height of willows on Herschel Island located at 70° north on the western Arctic coast of the Yukon Territory. We examined historic photographs, repeated vegetation surveys, and conducted monitoring of long-term plots and found evidence of increases of each of the dominant canopy-forming willow species (Salix richardsonii, Salix glauca and Salix pulchra), during the twentieth century. A simple model of patch initiation indicates that the majority of willow patches for each of these species became established between 1910 and 1960, with stem ages and maximum growth rates indicating that some patches could have established as late as the 1980s. Collectively, these results suggest that willow species are increasing in canopy cover and height on Herschel Island. We did not find evidence that expansion of willow patches is currently limited by herbivory, disease, or growing conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13280-011-0168-y) contains supplementary material, which is available to authorized users