Group key management with privacy preserving and trust still remains a precarious and stimulating issue for securing multicast communications in an energy embarrassed large wireless ad-hoc networks (WANETs). To address this, few researchers with the adaption of blockchain technology and practical usage of a privacy-preserving smart contract as group controller made these group key agreements adaptable to WANET. However, proportionate to the increase in the size of the group, the processing load on the smart contract is also increasing, which made the capability of the smart contract could not work beyond a certain group size. Contemporary blockchain schemes suffer from various inherent shortcomings in their latency, scalability, and processing throughput. So, in this direction, we adopted blockchain sharding smart contract-centric processing for making the key agreement adaptable to large WANETs.In this technique, we divide the large network into r sharded subnetworks with G 1 , G 2 , G 3 , … , G r as smart contract instances generated by group controller G, which acts as subgroup controllers to their respective shards using blockchain sharding technique. This protocol is shown secure under the assumptions elliptic curve decision Diffie-Hellman and group-elliptic curve Diffie-Hellman. The performance analysis demonstrates that the proposed protocol is highly proficient than examined protocols for secure communication in large WANETs.
Strength and stiffness characteristics are major concern for selecting any geomaterial. However, laboratory testing of these characteristics is time associated, laborious, and high cost. So, there is a need of intelligence tools to estimate the strength and stiffness of geomaterial. The impact of sawdust ash on the stiffness and strength properties of combined expanding clays is discussed in this research. The combined expansive clays underwent tests for California bearing ratio (CBR), unconfined compressive strength (UCS), optimal moisture content, maximum dry density, plasticity characteristics (liquid limit and plastic limit), and differential free swell (DFSI). According to test results, adding more sawdust to the blended clays improves their performance. This study also investigates the artificial neural network (ANN) model that considers six input variables to forecast the CBR and UCS of blended clays. The findings demonstrate that the ANN model performs more accurately for the CBR and UCS models. This clever method may help manage the under- or overestimation of additive dosage and reduce project costs.
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