作者：
Behnam Ojaghi*;Mohammad Mahdi Dehshibi;Angelos Antonopoulos
通讯作者：
Behnam Ojaghi
作者机构：
Centre Tecnologic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, Spain
Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Madrid, Spain
Nearby Computing S.L., Barcelona, Spain
通讯机构：
Centre Tecnologic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, Spain
语种：
英文
关键词：
Wireless sensor networks,Lifetime,IoT,Active learning,Critical nodes
期刊：
JOURNAL OF SUPERCOMPUTING
ISSN：
0920-8542
年：
2024
基金类别：
BODYinTRANSIT project as part of the European Union [101002711, TSI-063000-2021-52, TSI-063000-2021-144, TSI-063000-2021-70/71, TSI-063000-2021-24, TSI-063000-2021-39/40/41, TSI-063000-2021-112/113/114
摘要：
The energy efficiency of wireless sensor networks (WSNs) as a key feature of the Internet of Things (IoT) and fifth-generation (5G) mobile networks is determined by several key characteristics, such as hop count, user’s location, allocated power, and relay. Identifying important nodes, known as critical nodes, in IoT networks that involve a massive number of interconnected devices and sensors significantly affects these characteristics. However, it also requires a significant computational overhead and energy consumption. To address this issue, we introduce a novel supervised active learning method for identifying critical nodes in IoT networks aimed at enhancing the ene...
