@article{Spyridis2021,

title = {Modelling and simulation of a new cooperative algorithm for UAV swarm coordination in mobile RF target tracking},

author = { Y. Spyridis, T. Lagkas, P. Sarigiannidis and J. Zhang},

url = {https://www.researchgate.net/publication/346563683_Modelling_and_simulation_of_a_new_cooperative_algorithm_for_UAV_swarm_coordination_in_mobile_RF_target_tracking},

doi = {10.1016/j.simpat.2020.102232},

year  = {2021},

date = {2021-02-01},

journal = {Simulation Modelling Practice and Theory},

volume = {107},

pages = {102232},

publisher = {Elsevier BV},

abstract = {Recent advancements in sensor technology have allowed unmanned aerial vehicles (UAVs) to function as sensing devices in cooperative aerial communication networks, offering novel solutions in applications of environment inspection, disaster detection and search and rescue operations. Towards this trend, the efficient deployment and coordination of UAV networks is of vital importance. Generating controlled experimental conditions to implement and evaluate different approaches in this context can be impractical and costly and thus the solution of modelling is often preferred. This paper introduces a tracking model in which multirotor UAVs, equipped with received signal strength indicator (RSSI) sensors, are organized in a swarm and cooperate to approximate and trail a moving target. The proposed algorithm is able to offer autonomous tracking in large scale environments, by utilising just the strength of the communication signal emitted by a radio frequency transmitter carried by the target. A model of the proposed algorithm is created, and its performance is thoroughly evaluated in a specialized simulator developed in the Processing IDE. Results demonstrate the increased tracking efficiency of the proposed solution compared to a trilateration method. © 2020 Elsevier Ltd},

keywords = {Mobile target tracking, Modelling, RSSI, Simulation, UAV swarm, unmanned aerial vehicles, wireless sensor networks},

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{Spyridis2021b,

title = {Towards 6g iot: Tracing mobile sensor nodes with deep learning clustering in uav networks},

author = { Y. Spyridis and T. Lagkas and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and G. Eleftherakis and J. Zhang},

url = {https://www.researchgate.net/publication/352197709_Towards_6G_IoT_Tracing_Mobile_Sensor_Nodes_with_Deep_Learning_Clustering_in_UAV_Networks},

doi = {10.3390/s21113936},

year  = {2021},

date = {2021-01-01},

journal = {Sensors},

volume = {21},

number = {11},

abstract = {Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},

keywords = {6G, Deep Learning, Graph convolutional network, IoT, RSSI, Sensor tracking, unmanned aerial vehicles},

pubstate = {published},

tppubtype = {article}

}

@article{Loukas2019124,

title = {A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles},

author = { G. Loukas and E. Karapistoli and E. Panaousis and P. Sarigiannidis and A. Bezemskij and T. Vuong},

url = {https://www.researchgate.net/publication/328025147_A_taxonomy_and_survey_of_cyber-physical_intrusion_detection_approaches_for_vehicles},

doi = {10.1016/j.adhoc.2018.10.002},

year  = {2019},

date = {2019-01-01},

journal = {Ad Hoc Networks},

volume = {84},

pages = {124-147},

abstract = {With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research. © 2018 Elsevier B.V.},

keywords = {Aircraft, Automobiles, Cyber security, Cyber-physical systems, Driverless pods, Intrusion detection, Robotic land vehicles, unmanned aerial vehicles, VANET, Vehicles, Vehicular networks},

pubstate = {published},

tppubtype = {article}

}