Software Development Support for Shared Sensing Infrastructures: A Generative and Dynamic Approach

International audienceSensors networks are the backbone of large sensing infras-tructures such as Smart Cities or Smart Buildings. Classical approaches suffer from several limitations hampering developers' work (e.g., lack of sensor sharing, lack of dynamicity in data collection policies, need to dig inside big data sets, absence of reuse between implementation platforms). This paper presents a tooled approach that tackles these issues. It couples (i) an abstract model of developers' requirements in a given infrastructure to (ii) timed automata and code generation techniques, to support the efficient deployment of reusable data collection policies on different infrastructures. The approach has been validated on several real-world scenarios and is currently experimented on an academic campus

A multidimensional control architecture for combined fog-to-cloud systems

The fog/edge computing concept has set the foundations for the deployment of new services leveraging resources deployed at the edge paving the way for an innovative collaborative model, where end-users may collaborate with service providers by sharing idle resources at the edge of the network. Combined Fog-to-Cloud (F2C) systems have been recently proposed as a control strategy for managing fog and cloud resources in a coordinated way, aimed at optimally allocating resources within the fog-to-cloud resources stack for an optimal service execution. In this work, we discuss the unfeasibility of the deployment of a single control topology able to optimally manage a plethora of edge devices in future networks, respecting established SLAs according to distinct service requirements and end-user profiles. Instead, a multidimensional architecture, where distinct control plane instances coexist, is then introduced. By means of distinct scenarios, we describe the benefits of the proposed architecture including how users may collaborate with the deployment of novel services by selectively sharing resources according to their profile, as well as how distinct service providers may benefit from shared resources reducing deployment costs. The novel architecture proposed in this paper opens several opportunities for research, which are presented and discussed at the final section.This work was supported by the H2020 EU mF2C project, ref. 730929 and for UPC authors, also by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under contract RTI2018-094532-B-I00.Peer ReviewedPostprint (author's final draft

SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks

Currently, crop management through automatic monitoring is growing momentum, but presents various challenges. One key challenge is to quantify yield traits from images captured automatically. Wheat is one of the three major crops in the world with a total demand expected to exceed 850 million tons by 2050. In this paper we attempt estimation of wheat spikelets from high-definition RGB infield images using a fully convolutional model. We propose also the use of transfer learning and segmentation to improve the model. We report cross validated Mean Absolute Error (MAE) and Mean Square Error (MSE) of 53.0, 71.2 respectively on 15 real field images. We produce visualisations which show the good fit of our model to the task. We also concluded that both transfer learning and segmentation lead to a very positive impact for CNN-based models, reducing error by up to 89%, when extracting key traits such as wheat spikelet counts

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Towards cloud based big data analytics for smart future cities

© 2015, Khan et al.; licensee Springer. A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends

Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

Grain growth behavior and high-temperature high-strain-rate tensile ductility of iridium alloy DOP-26

This report summarizes results of studies conducted to date under the Iridium Alloy Characterization and Development subtask of the Radioisotope Power System Materials Production and Technology Program to characterize the properties of the new-process iridium-based DOP-26 alloy used for the Cassini space mission. This alloy was developed at Oak Ridge National Laboratory (ORNL) in the early 1980`s and is currently used by NASA for cladding and post-impact containment of the radioactive fuel in radioisotope thermoelectric generator (RTG) heat sources which provide electric power for interplanetary spacecraft. Included within this report are data generated on grain growth in vacuum or low-pressure oxygen environments; a comparison of grain growth in vacuum of the clad vent set cup material with sheet material; effect of grain size, test temperature, and oxygen exposure on high-temperature high-strain-rate tensile ductility; and grain growth in vacuum and high-temperature high-strain-rate tensile ductility of welded DOP-26. The data for the new-process material is compared to available old-process data

A mobile-based solution for supporting end-users in the composition of services

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-016-3910-4Currently, technologies and applications evolve to create eco-systems made up of a myriad of heterogeneous and distributed services that are accessible anytime and anywhere. Even though these services can be used individually, it is their coordinated and combined usage what provide an added value to end-users. In addition, user¿s wide adoption of mobile devices for daily activities have fostered a shift in the role played by end-users towards Internet data and services. However, existing solutions to service composition are not targeted to ordinary end-users. More easy-to-use tools have to be offered to end-users to make sure that they are successfully accepted and used by them. To this end, the work presented in this paper supports end-users in the creation of service compositions by using mobile devices. 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Education can improve the negative perception of a threatened long-lived scavenging bird, the Andean condor

Human-wildlife conflicts currently represent one of the main conservation problems for wildlife species around the world. Vultures have serious conservation concerns, many of which are related to people's adverse perception about them due to the belief that they prey on livestock. Our aim was to assess local perception and the factors influencing people's perception of the largest scavenging bird in South America, the Andean condor. For this, we interviewed 112 people from Valle Fértil, San Juan province, a rural area of central west Argentina. Overall, people in the area mostly have an elementary education, and their most important activity is livestock rearing. The results showed that, in general, most people perceive the Andean condor as an injurious species and, in fact, some people recognize that they still kill condors. We identified two major factors that affect this perception, the education level of villagers and their relationship with livestock ranching. Our study suggests that conservation of condors and other similar scavengers depends on education programs designed to change the negative perception people have about them. Such programs should be particularly focused on ranchers since they are the ones who have the worst perception of these scavengers. We suggest that highlighting the central ecological role of scavengers and recovering their cultural value would be fundamental to reverse their persecution and their negative perception by people.Fil: Cailly Arnulphi, Verónica Beatríz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Lambertucci, Sergio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Borghi, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentin