@inproceedings{MTMT:35998520,
	title = {IoT connectivity management by hyperbolic trees},
	url = {https://m2.mtmt.hu/api/publication/35998520},
	author = {Majdán, András and Pašić, Lejla and Ficzere, Dániel and Hollósi, Gergely László and Heszberger, Zalán and Vida, Rolland and Bíró, József},
	booktitle = {NOMS 2025-2025 IEEE Network Operations and Management Symposium},
	doi = {10.1109/NOMS57970.2025.11073575},
	unique-id = {35998520},
	abstract = {The growing scale and complexity of Internet of Things (IoT) networks present critical challenges for maintaining and recovering connectivity, especially in dynamic environments or after large-scale fragmentation caused by disasters. Traditional centralized approaches often face scalability, resilience, and energy efficiency issues. Hyperbolic geometry, which has successfully modeled hierarchical and scale-free structures in large-scale systems such as the Internet's AS topology, offers a promising foundation for addressing these challenges. This paper introduces hyperbolic trees as a novel framework for IoT connectivity management. Nodes are embedded in a hyperbolic plane, and connectivity is maintained or restored through a simple local rule: each node connects to the nearest node with a smaller radial coordinate. This distributed, asynchronous approach allows nodes to autonomously reorganize and recover connectivity without centralized coordination, ensuring scalability and adaptability. We demonstrate that hyperbolic trees provide a robust, energy-efficient solution for both maintaining and recovering IoT network connectivity. Numerical results validate the framework's scalability and effectiveness, highlighting its applicability in scenarios ranging from routine management to disaster recovery. These findings establish hyperbolic trees as a practical and scalable tool for resilient IoT connectivity management in next-generation networks.},
	year = {2025},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@article{MTMT:34763417,
	title = {Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2022 Conference},
	url = {https://m2.mtmt.hu/api/publication/34763417},
	author = {Lee, J.B. and Vida, Rolland},
	doi = {10.1109/JSEN.2024.3361559},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {24},
	unique-id = {34763417},
	issn = {1530-437X},
	year = {2024},
	eissn = {1558-1748},
	pages = {7234-7235}
}

@article{MTMT:35572196,
	title = {HTE 75 Infokommunikációs víziók},
	url = {https://m2.mtmt.hu/api/publication/35572196},
	author = {Sallai, Gyula and Vida, Rolland and Bacsárdi, László and Vágujhelyi, Ferenc and Gyires-Tóth, Bálint Pál and Buttyán, Levente and Baranyi, Péter Zoltán and Rab, Árpád and Bartolits, István},
	journal-iso = {HÍRADÁSTECHNIKA},
	journal = {HÍRADÁSTECHNIKA (1962)},
	volume = {79},
	unique-id = {35572196},
	issn = {0018-2028},
	abstract = {A HTE 75 éves évfordulójához kapcsolódóan a digitalizáció megatrendjének áttekintése után a cikk hét víziót tartalmaz, kitekintéseket a hálózati technológiák, a kvantum-infokommunikáció, a blokklánc technológiák, a mesterséges intelligencia, a kiberbiztonság, az infotáció és az infokommunikációs technológiák társadalmai hatásainak következő évtizedeire, és áttekinti összefonódásaikat.},
	keywords = {Mesterséges intelligencia; kiberbiztonság; infokommunikáció; blokklánctechnológia; kvantumkommunikáció; infokommunikációs hálózatok; infotáció; Digitális valóság technológiák},
	year = {2024},
	pages = {1-3},
	orcid-numbers = {Sallai, Gyula/0000-0002-8062-3042; Bacsárdi, László/0000-0002-7337-317X; Baranyi, Péter Zoltán/0000-0002-8265-5849; Rab, Árpád/0000-0002-9427-2968}
}

@article{MTMT:35573368,
	title = {A hálózati technológiák jövője: vízió 2040-re},
	url = {https://m2.mtmt.hu/api/publication/35573368},
	author = {Vida, Rolland},
	journal-iso = {HÍRADÁSTECHNIKA},
	journal = {HÍRADÁSTECHNIKA (1962)},
	volume = {79},
	unique-id = {35573368},
	issn = {0018-2028},
	year = {2024},
	pages = {4-6}
}

@inproceedings{MTMT:36086987,
	title = {Navigable Architectures for Complex Communication Networks},
	url = {https://m2.mtmt.hu/api/publication/36086987},
	author = {Majdán, András and Pašić, Lejla and Heszberger, Zalán and Vida, Rolland and Bíró, József},
	booktitle = {2024 IEEE Virtual Conference on Communications, VCC 2024},
	doi = {10.1109/VCC63113.2024.10914373},
	unique-id = {36086987},
	abstract = {The anticipated complexity of future communication networks (e.g. 6G) demands the use of advanced complex network analysis tools and methodologies during their conceptualization, design, and operation. In this paper, we introduce a new family of navigation architectures and algorithms based on a specialized geometric embedding of complex networks. We demonstrate that this approach is scalable with respect to routing table sizes and allows nodes to make navigation decisions without full knowledge of the system's topology. While this method requires additional connections to extend the original network, the cost is minimal, as these extra links represent only a small fraction of the network's size and can be factored into the design. We showcase the effectiveness of this approach within prospective communication architectures. © 2024 IEEE.},
	keywords = {complex networks; Communication systems; Trees (mathematics); Analysis tools; Memory requirements; Memory requirements; Spanning tree; Spanning tree; Navigation algorithms; Design and operations; Memory architecture; complex network analysis; Geometric embedding; Geometric embedding; Communications networks; Communications systems; Navigation architectures},
	year = {2024},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}

@inproceedings{MTMT:36224223,
	title = {Matchmaking optimisation for car sharing services},
	url = {https://m2.mtmt.hu/api/publication/36224223},
	author = {Sardouk, Firas and Vida, Rolland and Cinkler, Tibor},
	booktitle = {Proceedings of the 1st Workshop on Intelligent Infocommunication Networks, Systems and Services (WI2NS2)},
	doi = {10.3311/WINS2023-019},
	unique-id = {36224223},
	year = {2023},
	pages = {105-110}
}

@article{MTMT:32584660,
	title = {Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection},
	url = {https://m2.mtmt.hu/api/publication/32584660},
	author = {Cinkler, Tibor and Nagy, Kristof and Simon, Csaba and Vida, Rolland and Rajab, Husam},
	doi = {10.1109/JSEN.2021.3134817},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {22},
	unique-id = {32584660},
	issn = {1530-437X},
	abstract = {For a wireless sensor network consisting of numerous sensors, spread over a large area with no direct power supply, energy efficiency is of paramount importance. As most power is consumed by the communication module, we should pay special attention to reduce communication needs as much as possible. The more data we send, the larger the power requirement of the sensor module. Preprocessing can help in reducing the amount of data to send. However, it also consumes energy. This paper focuses on this tradeoff between preprocessing, pre-filtering and preselecting of sensor data on one hand, and uploading of unprocessed and unfiltered raw data on the other hand, for the special case of protecting vineyards from starlings. We propose a two-phase decision mechanism based on machine learning: the less complex first phase is executed on the microcontroller of the sensor module, while the more complex, more accurate second phase is performed in the cloud. Individual noise sensors monitor the environment, and try to detect starling songs, using a simple, SVM-based classification. These sensors are grouped into clusters, through a mechanism similar to the well-known LEACH protocol, and signal to the current cluster-head the likelihood of starling presence. If several alerts are received to justify further investigation, the cluster-head asks the node with highest starling detection likelihood to upload a 1 s sound sample to the cloud. There, the more complex and more accurate second phase sound matching is performed, and the actuators deployed in the field are remotely triggered, if needed.},
	year = {2022},
	eissn = {1558-1748},
	pages = {11393-11404}
}

@article{MTMT:33037332,
	title = {Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2020 Conference},
	url = {https://m2.mtmt.hu/api/publication/33037332},
	author = {Krijnen, G. and Vida, Rolland},
	doi = {10.1109/JSEN.2022.3172520},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {22},
	unique-id = {33037332},
	issn = {1530-437X},
	year = {2022},
	eissn = {1558-1748},
	pages = {11222}
}

@article{MTMT:33217363,
	title = {A Primer on Software Defined Radios},
	url = {https://m2.mtmt.hu/api/publication/33217363},
	author = {Popescu, Dimitrie C. and Vida, Rolland},
	doi = {10.36244/ICJ.2022.3.3},
	journal-iso = {INFOCOMMUNICATIONS J},
	journal = {INFOCOMMUNICATIONS JOURNAL},
	volume = {14},
	unique-id = {33217363},
	issn = {2061-2079},
	abstract = {The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that includes a discussion of drivers and technologies that have contributed to their continuous advancement, and presents the theory needed to understand the architecture and operation of current SDRs. We also review the choices for SDR platforms and the programming options that are currently available for SDR research, development, and teaching, and present case studies illustrating SDR use. Our hope is that the paper will be useful as a reference to wireless researchers and developers working in the industry or in academic settings on further advancing and refining the capabilities of wireless systems.},
	year = {2022},
	eissn = {2061-2125},
	pages = {16-27}
}

@inproceedings{MTMT:33558121,
	title = {Connected Hyperbolic Complex Networks},
	url = {https://m2.mtmt.hu/api/publication/33558121},
	author = {Vida, Rolland and Bíró, József},
	booktitle = {2022 International Conference on Computational Science and Computational Intelligence (CSCI)},
	doi = {10.1109/CSCI58124.2022.00084},
	unique-id = {33558121},
	abstract = {Generative models play an important role in analyzing and predicting relevant characteristics of real-world complex networks. The predictive power of these models lies in the possibility that large amount of synthetic networks, similar to real ones at some extent, can be produced, and by analyzing them statistically, significant inferences can be performed. Synthetic networks, based on the hyperbolic geometry, turned out to be good generative models of real-world networks, as they reproduce several macroscopic behaviours of real networks and can help in assessing the scalability of new network functions. The original hyperbolic generative model by Krioukov et al. [1] lacks an important property: it can not guarantee with 100% probability that the resulting synthetic network is connected. In other words for some parameter regions, the networks fall into fragments resulting in disconnected regions of subgraphs. Inevitably, from the viewpoint of modeling real networks, the connectedness is an expected property. In this paper we show that, with a slight extension of the original generation rule of the hyperbolic networks, the connectedness can always be ensured. We also present analytical and numerical results which show that there will be no significant changes in the macroscopic properties of networks, like average degree and degree distribution.},
	year = {2022},
	pages = {432-437},
	orcid-numbers = {Bíró, József/0000-0002-9729-2702}
}