A supervised active learning method for identifying critical nodes in Wireless Sensor Network

• URL: http://arxiv.org/abs/2004.08885v4
• Date: Sat, 29 Apr 2023 18:45:43 GMT
• Title: A supervised active learning method for identifying critical nodes in Wireless Sensor Network
• Authors: Behnam Ojaghi and Mohammad Mahdi Dehshibi
• Abstract summary: We propose an active learning approach to address the computational overhead of identifying critical nodes in a wireless sensor network (WSN) The proposed approach can overcome biasing in identifying non-critical nodes and needs much less effort in fine-tuning to adapt to the dynamic nature of WSN. Experiments show that the proposed method has more flexibility, compared to the state-of-the-art, to be employed in large scale WSN environments.
• Score: 0.27920304852537525
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Energy Efficiency of a wireless sensor network (WSN) relies on its main characteristics, including hop-number, user's location, allocated power, and relay. Identifying nodes, which have more impact on these characteristics, is, however, subject to a substantial computational overhead and energy consumption. In this paper, we proposed an active learning approach to address the computational overhead of identifying critical nodes in a WSN. The proposed approach can overcome biasing in identifying non-critical nodes and needs much less effort in fine-tuning to adapt to the dynamic nature of WSN. This method benefits from the cooperation of clustering and classification modules to iteratively decrease the required number of data in a typical supervised learning scenario and to increase the accuracy in the presence of uninformative examples, i.e., non-critical nodes. Experiments show that the proposed method has more flexibility, compared to the state-of-the-art, to be employed in large scale WSN environments, the fifth-generation mobile networks (5G), and massively distributed IoT (i.e., sensor networks), where it can prolong the network lifetime.

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