@article{Moysiadis2021,

title = {Smart Farming in Europe},

author = { V. Moysiadis and P. Sarigiannidis and V. Vitsas and A. Khelifi},

url = {https://www.researchgate.net/publication/346716261_Smart_Farming_in_Europe},

doi = {10.1016/j.cosrev.2020.100345},

year  = {2021},

date = {2021-02-01},

journal = {Computer Science Review},

volume = {39},

pages = {100345},

publisher = {Elsevier BV},

abstract = {Smart Farming is the new term in the agriculture sector, aiming to transform the traditional techniques to innovative solutions based on Information Communication Technologies (ICT). Concretely, technologies like Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Image Processing, Machine Learning, Big Data, Cloud Computing, and Wireless Sensor Networks (WSNs), are expected to bring significant changes in this area. Expected benefits are the increase in production, the decrease in cost by reducing the inputs needed such as fuel, fertilizer and pesticides, the reduction in labor efforts, and finally improvement in the quality of the final products. Such innovative methods are crucial in recent days, due to the exponential increase of the global population, the importance of producing healthier products grown with as much fewer pesticides, where public opinion of European citizens is sensitized. Moreover, due to the globalization of the world economy, European countries face the low cost of production of other low-income countries. In this vein, Europe tries to evolve its agriculture domain using technology, aiming at the sustainability of its agricultural sector. Although many surveys exist, most of them tackle in a specific scientific area of Smart Farming. An overview of Smart Farming covering all the involved technologies and providing an extensive reference of good practices around Europe is essential. Our expectation from our work is to become a good reference for researchers and help them with their future work. This paper aims to provide a comprehensive reference for European research efforts in Smart Farming and is two-fold. First, we present the research efforts from researchers in Smart Farming, who apply innovative technology trends in various crops around Europe. Second, we provide and analyze the most significant projects in Europe in the area of Smart Farming. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.},

keywords = {Big Data, Cloud Computing, Image Processing, machine learning, smart farming, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Wireless Sensor Networks (WSNs)},

pubstate = {published},

tppubtype = {article}

}

@article{Jaddoa2020,

title = {Dynamic decision support for resource offloading in heterogeneous Internet of Things environments},

author = { A. Jaddoa and G. Sakellari and E. Panaousis and G. Loukas and P.G. Sarigiannidis},

url = {https://www.researchgate.net/publication/337218323_Dynamic_decision_support_for_resource_offloading_in_heterogeneous_Internet_of_Things_environments},

doi = {10.1016/j.simpat.2019.102019},

year  = {2020},

date = {2020-01-01},

journal = {Simulation Modelling Practice and Theory},

volume = {101},

abstract = {Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices’ resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner. © 2019 Elsevier B.V.},

keywords = {Cloud Computing, Computation offloading, Decision support, Edge computing, EdgeCloudSim simulator, Internet of things, IoT offloading},

pubstate = {published},

tppubtype = {article}

}

@article{Triantafyllou2019b,

title = {Precision agriculture: A remote sensing monitoring system architecture},

author = { A. Triantafyllou and P. Sarigiannidis and S. Bibi},

url = {https://www.researchgate.net/publication/337192880_Precision_Agriculture_A_Remote_Sensing_Monitoring_System_Architecture},

doi = {10.3390/info10110348},

year  = {2019},

date = {2019-01-01},

journal = {Information (Switzerland)},

volume = {10},

number = {11},

abstract = {Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Saffron agriculture in Kozani, Greece. © 2019 by the authors.},

keywords = {Cloud Computing, Communication technologies, Internet of things, precision agriculture, smart farming, wireless sensor networks},

pubstate = {published},

tppubtype = {article}

}