Analysis of Imbalanced Data Challenges in Wireless Sensor Networks and Existing Solutions

Wireless Sensor Networks (WSNs) play a critical role in modern applications such as environmental monitoring, healthcare, and smart cities, where they collect real-time data from distributed sensors. However, one of the major challenges in WSNs is the issue of imbalanced data, which can severely affect the prediction and classification abilities of machine learning models deployed in these networks. Imbalanced datasets often lead to poor classification performance, especially for rare events such as gas leaks or environmental disasters, which are crucial for system reliability and security. Furthermore, imbalanced data results in a high false negative rate, where rare but important events are overlooked, posing significant risks in safety-critical applications. The limited resources of WSNs, such as energy and bandwidth, make it even more challenging to handle imbalanced data effectively. The scarcity of minority class data, especially in remote or harsh environments, compounds the problem by limiting the network’s ability to detect these rare events. Additionally, network design factors, such as the spatial distribution of sensors and environmental conditions, can further contribute to data imbalance, making it difficult to ensure consistent and accurate data collection. This paper aims to explore the challenges of imbalanced data in WSNs and present various solutions to mitigate these issues. We review several preprocessing techniques, including oversampling methods like SMOTE (Synthetic Minority Over-sampling Technique), undersampling methods, and ADASYN (Adaptive Synthetic Sampling). We also examine the effectiveness of ensemble methods, such as Random Forests and AdaBoost, which have been shown to improve classification performance for imbalanced datasets. Furthermore, cost-sensitive learning approaches and modifications to classification algorithms, such as Support Vector Machines (SVM) and Decision Trees, are discussed as ways to improve detection of minority class events. Additionally, we investigate the role of network design improvements, such as hierarchical and distributed network architectures, in addressing data collection challenges. These strategies help in ensuring better representation of rare events and improving the overall detection accuracy in imbalanced data conditions. By leveraging these solutions, WSNs can maintain efficiency and reliability even in resource-constrained environments.

کلیدواژه ها:

Wireless Sensor Networks ، Imbalanced Data ، Machine Learning ، Data Preprocessing ، Classification Algorithms