MUMBAI, India, July 7 -- Intellectual Property India has published a patent application (202621055974 A) filed by Prof. Neha Verma; Dr. Preeti Singh; Mr. Dipak Eknath Chavan; Nancy Sharma; Ramakrishna Manda; Dr P Agilan; Khammampati R Sreejyothi; Velamala Yamuna; Dr. N. Venkatachalam; P. Vidhya; Dr A Udhayakumar; and Subham Pankaj Samantaray on May 02, 2026, for Machine Learning-Based Energy-Efficient Hybrid Optimization For Secure Hierarchical Data Aggregation In Cloud-Assisted Wireless Sensor Networks.
Inventors include Prof. Neha Verma; Dr. Preeti Singh; Mr. Dipak Eknath Chavan; Nancy Sharma; Ramakrishna Manda; Dr P Agilan; Khammampati R Sreejyothi; Velamala Yamuna; Dr. N. Venkatachalam; P. Vidhya; Dr A Udhayakumar; and Subham Pankaj Samantaray.
The application for the patent was published on July 03, 2026, under issue no. 27/2026.
Abstract: The present invention relates to the development of a cloud-assisted WSNs suffer from various limitations associated with energy restrictions and data security problems. In this regard, an effective approach using the machine learning (ML)-based optimization methodology has been developed to enhance energy efficiency in the process of hierarchical data aggregation in a secure manner. Cloud computing can be used to optimize both clustering and route selection processes with the goal of minimizing energy utilization and ensuring data security. The use of ML-based predictive techniques in combination with optimization procedures allows achieving higher security by detecting any possible attacks such as data tampering or eavesdropping. As a result, the proposed approach enables achieving better performance in terms of the network lifetime and security measures than standard solutions. FIG.1
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