@conference{Griva2021,

title = {Performance Evaluation of LoRa Networks in an Open Field Cultivation Scenario},

author = { A. Griva and A.D. Boursianis and S. Wan and P. Sarigiannidis and G. Karagiannidis and S.K. Goudos},

url = {https://www.researchgate.net/publication/353498225_Performance_Evaluation_of_LoRa_Networks_in_an_Open_Field_Cultivation_Scenario},

doi = {10.1109/MOCAST52088.2021.9493416},

year  = {2021},

date = {2021-01-01},

journal = {2021 10th International Conference on Modern Circuits and Systems Technologies, MOCAST 2021},

abstract = {The employment of Internet of Things (IoT) technology in agriculture could be beneficial in managing the cultivation production in a highly-customizable way. LoRa (Long Range) is one of the most important technologies in cultivation fields mainly thanks to its ability to provide long-range transmission and low power consumption. In this paper, we evaluate the performance of LoRa networks in an open field cultivation scenario via simulations using FLoRa, an open-source framework in OMNeT++. The number of nodes, the number of gateways, the antenna gain, and the size of the deployment area have a considerable impact on both the data extraction rate and the energy consumption of a LoRa network. Our results show that the optimization of the parameters that affect the performance of a LoRa network could transform traditional agriculture into a new perspective of smart cultivation. Finally, we evaluate the impact of the density and the geometric characteristics of three types of crop (tomatoes, grapes, apples) on the number of intersections that were caused by the insertion of physical objects-obstacles in a LoRa network. © 2021 IEEE.},

keywords = {Data Extraction Rate, Internet of Things (IoT), Long Range (LoRa), Network Energy Consumption, Smart Agriculture},

pubstate = {published},

tppubtype = {conference}

}

@conference{Protopsaltis2020,

title = {Data Visualization in Internet of Things: Tools, Methodologies, and Challenges},

author = { A. Protopsaltis and P. Sarigiannidis and D. Margounakis and A. Lytos},

url = {https://www.researchgate.net/publication/343935293_Data_Visualization_in_Internet_of_Things_Tools_Methodologies_and_Challenges},

doi = {10.1145/3407023.3409228},

isbn = {9781450388337},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 15th International Conference on Availability, Reliability and Security},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Ireland},

series = {ARES '20},

abstract = {As the Internet of Things (IoT) grows rapidly, huge amounts of wireless sensor networks emerged monitoring a wide range of infrastructure, in various domains such as healthcare, energy, transportation, smart city, building automation, agriculture, and industry producing continuously streamlines of data. Big Data technologies play a significant role within IoT processes, as visual analytics tools, generating valuable knowledge in real-time in order to support critical decision making. This paper provides a comprehensive survey of visualization methods, tools, and techniques for the IoT. We position data visualization inside the visual analytics process by reviewing the visual analytics pipeline. We provide a study of various chart types available for data visualization and analyze rules for employing each one of them, taking into account the special conditions of the particular use case. We further examine some of the most promising visualization tools. Since each IoT domain is isolated in terms of Big Data approaches, we investigate visualization issues in each domain. Additionally, we review visualization methods oriented to anomaly detection. Finally, we provide an overview of the major challenges in IoT visualizations.},

keywords = {Anomaly Detection, Big Data, Data visualization, Internet of Things (IoT)},

pubstate = {published},

tppubtype = {conference}

}

@conference{Pliatsios2019416,

title = {Power allocation in downlink non-orthogonal multiple access IoT-enabled Systems: A particle swarm optimization approach},

author = { D. Pliatsios and P. Sarigiannidis},

url = {https://www.researchgate.net/publication/335366103_Power_Allocation_in_Downlink_Non-orthogonal_Multiple_Access_IoT-enabled_Systems_A_Particle_Swarm_Optimization_Approach},

doi = {10.1109/DCOSS.2019.00085},

year  = {2019},

date = {2019-01-01},

journal = {Proceedings - 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019},

pages = {416-422},

abstract = {The exponential proliferation of the Internet of Things (IoT) concept renders it an integral part of the emerging 5th Generation (5G) of mobile networks. In order to address the requirement of massive IoT-type connections, the spectrum has to be better utilized. Non-Orthogonal Multiple Access (NOMA) is a promising technique that can effectively improve the spectrum efficiency. In this paper, we consider the power allocation problem of a downlink Single Input Single Output (SISO) NOMA system. We aim to maximize the total system throughput, while maintaining a good level of fairness among the users. The system available bandwidth is divided into a number of subbands, and each subband is assigned to two users. The combination of the assigned users is based on the performance of each one, in terms of throughput. Two methods are reported for allocating power to each subband. In the first method, the maximum transmission power is equally divided to all bands, whereas, in the second, we use the Particle Swarm Optimization (PSO) algorithm to allocate the power to each band aiming to maximize the total system throughput. The power allocated to each band is further divided into the two multiplexed users. A series of extensive simulations and comparisons were employed to evaluate our proposed power allocation methods. The results indicate that our methods provide a significant improvement over the compared ones, both in terms of achieved system throughput and fairness among the users. © 2019 IEEE.},

keywords = {Internet of Things (IoT), Non Orthogonal Multiple Access (NOMA), Particle Swarm Optimization (PSO)},

pubstate = {published},

tppubtype = {conference}

}