TY - CHAP AU - Majdán, András AU - Pašić, Lejla AU - Ficzere, Dániel AU - Hollósi, Gergely László AU - Heszberger, Zalán AU - Vida, Rolland AU - Bíró, József TI - IoT connectivity management by hyperbolic trees T2 - NOMS 2025-2025 IEEE Network Operations and Management Symposium PB - IEEE CY - Piscataway (NJ) SN - 9798331531645 T3 - IEEE IFIP Network Operations and Management Symposium, ISSN 1542-1201 PY - 2025 PG - 7 DO - 10.1109/NOMS57970.2025.11073575 UR - https://m2.mtmt.hu/api/publication/35998520 ID - 35998520 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Lee, J.B. AU - Vida, Rolland TI - Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2022 Conference JF - IEEE SENSORS JOURNAL J2 - IEEE SENS J VL - 24 PY - 2024 IS - 6 SP - 7234 PG - 1 SN - 1530-437X DO - 10.1109/JSEN.2024.3361559 UR - https://m2.mtmt.hu/api/publication/34763417 ID - 34763417 N1 - Export Date: 2 April 2024 Correspondence Address: Vida, R.; Budapest University of Technology and EconomicsHungary; email: vida@tmit.bme.hu WoS:hiba:001197673400002 2024-09-23 23:32 cím nem egyezik LA - English DB - MTMT ER - TY - JOUR AU - Sallai, Gyula AU - Vida, Rolland AU - Bacsárdi, László AU - Vágujhelyi, Ferenc AU - Gyires-Tóth, Bálint Pál AU - Buttyán, Levente AU - Baranyi, Péter Zoltán AU - Rab, Árpád AU - Bartolits, István TI - HTE 75 Infokommunikációs víziók JF - HÍRADÁSTECHNIKA (1962) J2 - HÍRADÁSTECHNIKA VL - 79 PY - 2024 IS - 2 SP - 1 EP - 3 PG - 28 SN - 0018-2028 UR - https://m2.mtmt.hu/api/publication/35572196 ID - 35572196 N1 - Szerkesztő: Sallai Gyula https://www.hiradastechnika.hu/documents/4743302/4941534/Hiradastechnika_Kulonszam.pdf AB - 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. LA - Hungarian DB - MTMT ER - TY - JOUR AU - Vida, Rolland TI - A hálózati technológiák jövője: vízió 2040-re JF - HÍRADÁSTECHNIKA (1962) J2 - HÍRADÁSTECHNIKA VL - 79 PY - 2024 IS - 2 SP - 4 EP - 6 PG - 3 SN - 0018-2028 UR - https://m2.mtmt.hu/api/publication/35573368 ID - 35573368 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Majdán, András AU - Pašić, Lejla AU - Heszberger, Zalán AU - Vida, Rolland AU - Bíró, József TI - Navigable Architectures for Complex Communication Networks T2 - 2024 IEEE Virtual Conference on Communications, VCC 2024 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway (NJ) SN - 9798331530099 PY - 2024 PG - 5 DO - 10.1109/VCC63113.2024.10914373 UR - https://m2.mtmt.hu/api/publication/36086987 ID - 36086987 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Sardouk, Firas AU - Vida, Rolland AU - Cinkler, Tibor ED - Imre, Sándor ED - Gyimóthy, Szabolcs ED - Varga, Pál TI - Matchmaking optimisation for car sharing services T2 - Proceedings of the 1st Workshop on Intelligent Infocommunication Networks, Systems and Services (WI2NS2) PB - BME Villamosmérnöki és Informatikai Kar CY - Budapest SN - 9789634219026 PY - 2023 SP - 105 EP - 110 PG - 6 DO - 10.3311/WINS2023-019 UR - https://m2.mtmt.hu/api/publication/36224223 ID - 36224223 LA - English DB - MTMT ER - TY - JOUR AU - Cinkler, Tibor AU - Nagy, Kristof AU - Simon, Csaba AU - Vida, Rolland AU - Rajab, Husam TI - Two-Phase Sensor Decision: Machine-Learning for Bird Sound Recognition and Vineyard Protection JF - IEEE SENSORS JOURNAL J2 - IEEE SENS J VL - 22 PY - 2022 IS - 12 SP - 11393 EP - 11404 PG - 10 SN - 1530-437X DO - 10.1109/JSEN.2021.3134817 UR - https://m2.mtmt.hu/api/publication/32584660 ID - 32584660 N1 - Export Date: 19 September 2022 Correspondence Address: Cinkler, T.; Budapest University of Technology and Economics, Hungary; email: cinkler@tmit.bme.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Krijnen, G. AU - Vida, Rolland TI - Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2020 Conference JF - IEEE SENSORS JOURNAL J2 - IEEE SENS J VL - 22 PY - 2022 IS - 12 SP - 11222 SN - 1530-437X DO - 10.1109/JSEN.2022.3172520 UR - https://m2.mtmt.hu/api/publication/33037332 ID - 33037332 N1 - Export Date: 3 August 2022 Correspondence Address: Vida, R.; Budapest University of Technology and Economics, Hungary; email: vida@tmit.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Popescu, Dimitrie C. AU - Vida, Rolland TI - A Primer on Software Defined Radios JF - INFOCOMMUNICATIONS JOURNAL J2 - INFOCOMMUNICATIONS J VL - 14 PY - 2022 IS - 3 SP - 16 EP - 27 PG - 12 SN - 2061-2079 DO - 10.36244/ICJ.2022.3.3 UR - https://m2.mtmt.hu/api/publication/33217363 ID - 33217363 N1 - Export Date: 13 December 2022 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Vida, Rolland AU - Bíró, József ED - Hamid, M. Arabnia TI - Connected Hyperbolic Complex Networks T2 - 2022 International Conference on Computational Science and Computational Intelligence (CSCI) PB - IEEE CY - Piscataway (NJ) SN - 9798350320282 PY - 2022 SP - 432 EP - 437 PG - 6 DO - 10.1109/CSCI58124.2022.00084 UR - https://m2.mtmt.hu/api/publication/33558121 ID - 33558121 N1 - Export Date: 15 December 2023 AB - 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. LA - English DB - MTMT ER -