Wireless Sensor Networks based on Mobile Sinks (MS) are becoming more popular because of their benefits in enhancing performance and minimizing unequal energy usage of the nodes. While several MS data gathering methods have been proposed, most of them are less adaptive to changes in network topology and fails to modify the MS path suitably in response to node failures. Also, it is difficult to plan an efficient path for data gathering by MS using a set of Rendezvous Points (RPs) that could reduce the MS travel time while reducing the WSN energy usage. In this paper, we propose an Enhanced Sparrow Search based Adaptive and Robust Data Gathering Scheme for WSNs (ESRP-MP) to create shorter travel route for MS and minimize data gathering latency. For effective utilization of RPs, the best RP locations that maximize the coverage of nodes and minimize the overlap in coverage with adjacent RPs are considered. Also, the algorithm is adaptive to node failures by updating the RPs and refining the planned MS path when necessary. In the first phase, we introduce an enhanced Sparrow Search based algorithm to select a set of RPs that maximizes the coverage of nodes and minimizes the overlap in RP coverage. Then, the shortest tour through the RPs is obtained using Ant Colony Optimization (ACO). The results reveal the effectiveness of ESRP-MP against other related approaches in terms of number of RPs, data gathering time, MS path, energy utilization and network lifetime.

Wireless Sensor Networks (WSNs) often struggle with managing extensive data volumes, given their resource-constrained nature. Deployed in unattended areas, they face significant security risks and attacks. This study introduces the Secure Data Collection Model with Blockchain and Machine Learning Integration for WSNs (SDCBM), designed to identify intrusions and ensure secure data collection and storage for WSN applications. SDCBM employs an Extreme Learning Machine (ELM) model, a fast single-layer feedforward neural network, and integrates techniques for balancing the data distribution and selecting relevant features to enhance real-time detection of malicious attacks. Data is preprocessed and balanced utilizing the Synthetic Minority Oversampling Technique (SMOTE) and Tomek-Links combination method. To enhance the feature selection process, the Harris Hawk Optimization-based method is proposed. The blockchain module manages network node registration, authentication, node revocation, and secure storage of data hashes and node credentials. Simulation results demonstrate the efficacy of the proposed SDCBM method in detecting malicious nodes and enhancing secure data collection, thereby strengthening the security of WSNs.