BIELM: Integrating Incremental Extreme Learning Machine and Blockchain for Secure Data Gathering in IoT-Based WSNs

The Internet of Things (IoT) is advancing at a rapid pace with wireless sensor networks (WSNs) and is becoming integral for real-time monitoring and data-driven decision-making in different sectors. Emerging real-world applications are greatly reliant on IoT-based WSNs to improve productivity and operational efficiency. However, WSNs encounter significant challenges in handling substantial data amounts and maintaining robust security requirements. These networks are prone to many security risks that may compromise network operation and data integrity. This article proposes a novel technique by integrating blockchain and incremental extreme learning machine (abbreviated as BIELM) to effectively detect such malicious nodes in WSNs and ensure secure data collection. BIELM utilizes an interplanetary file system (IPFS) for efficient data storage and blockchain to record hashes and provide secure data access. This work also introduces an effective feature selection method by modifying the sparrow search optimization and the edited nearest neighbor (ENN) for data balancing with a combination of a standard oversampling technique (SMOTE). The experimental results reveal an accuracy of 99.38%, demonstrating the superior performance of BIELM in detecting malicious nodes, with an average cost saving of approximately 22.72% in blockchain transaction costs across key operations compared to conventional blockchainbased methods, revealing its overall efficiency over the existing methods.