@article{Triantafyllou2021,

title = {Leveraging fairness in LoRaWAN: A novel scheduling scheme for collision avoidance},

author = { A. Triantafyllou, P. Sarigiannidis, T. Lagkas, I. D. Moscholios and A. Sarigiannidis},

url = {https://www.researchgate.net/publication/346627962_Leveraging_Fairness_in_LoRaWAN_A_Novel_Scheduling_Scheme_for_Collision_Avoidance},

doi = {10.1016/j.comnet.2020.107735},

year  = {2021},

date = {2021-02-01},

journal = {Computer Networks},

volume = {186},

pages = {107735},

publisher = {Elsevier BV},

abstract = {The employment of Low-Power Wide Area Networks (LPWANs) has proven quite beneficial to the advancement of the Internet of Things (IoT) paradigm. The utilization of low power but long range communication links of the LoRaWAN technology promises low energy consumption, while ensuring sufficient throughput. However, due to LoRa's original scheduling process there is a high chance of packet collisions, compromising the technology's reliability. In this paper, we propose a new Medium Access Control (MAC) protocol, entitled the FCA-LoRa leveraging fairness and improving collision avoidance in LoRa wide-area networks. The novel scheduling process that is introduced is based on the broadcasting of beacon frames by the network's gateway in order to synchronize communication with end devices. Our results demonstrate the benefits of FCA-LoRa over an enhanced version of the legacy LoRaWAN employing the ALOHA protocol and an advanced adaptive rate mechanism, in terms of throughput and collision avoidance. Indicatively, in a single gateway scenario with 600 nodes, FCA-LoRa can increase throughput by nearly 50%while in a multiple gateway scenario, throughput reaches an increase of 49% for 500 nodes. © 2020 Elsevier B.V.},

keywords = {Collision avoidance, Fairness, Internet of things, LoRa, LoRaWAN, Low-Power Wide Area Networks, Medium Access Control, Scalability},

pubstate = {published},

tppubtype = {article}

}

@article{Kakamoukas2021,

title = {Towards Climate Smart Farming - A Reference Architecture for Integrated Farming Systems},

author = { G. Kakamoukas and P. Sarigiannidis and A. Maropoulos and T. Lagkas and K. Zaralis and C. Karaiskou},

url = {https://www.researchgate.net/publication/349141429_Towards_Climate_Smart_Farming-A_Reference_Architecture_for_Integrated_Farming_Systems},

doi = {10.3390/telecom2010005},

year  = {2021},

date = {2021-02-01},

journal = {Telecom},

volume = {2},

number = {1},

pages = {52--74},

publisher = {MDPI AG},

abstract = {Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions. This work proposes a CSA architecture for fostering and supporting integrated agricultural systems, such as Mixed Farming Systems (MFS), by facilitating the design, the deployment and the management of crop–livestock-= forestry combinations towards sustainable, efficient and climate resilient agricultural systems. Propelled by cutting-edge technology solutions in data collection and processing, along with fully autonomous monitoring systems, eg, smart sensors and unmanned aerial vehicles (UAVs), the proposed architecture called MiFarm-CSA, aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions, through a novel conceptual framework},

keywords = {climate smart agriculture, Internet of things, mixed farming systems, participatory learning, socio-economic modelling, unmanned aerial vehicles},

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{Lytos2020,

title = {Towards smart farming: Systems, frameworks and exploitation of multiple sources},

author = { A. Lytos and T. Lagkas and P. Sarigiannidis and M. Zervakis and G. Livanos},

url = {https://www.researchgate.net/publication/339221382_Towards_Smart_Farming_Systems_Frameworks_and_Exploitation_of_Multiple_Sources},

doi = {10.1016/j.comnet.2020.107147},

year  = {2020},

date = {2020-01-01},

journal = {Computer Networks},

volume = {172},

abstract = {Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain have the capability to collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also on the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status. This survey paper covers the state-of-the-art agriculture systems and big data architectures both in research and commercial status in an effort to bridge the knowledge gap between agriculture systems and exploitation of big data. The first part of the paper is devoted to the exploration of the existing agriculture systems, providing the necessary background information for their evolution until they have reached the current status, able to support different platforms and handle multiple sources of information. The second part of the survey is focused on the exploitation of multiple sources of information, providing information for both the nature of the data and the combination of different sources of data in order to explore the full potential of ICT systems in agriculture. © 2020 The Authors},

keywords = {Agriculture, Big Data, Internet of things, machine learning, smart farming},

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}

}