Massive Machine Type IoT Communication (mMTIC) has the potential for high impact in anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering Routing Protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight towards the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multi-hop communication among the nodes often causes such an energy hole problem. In this paper, we develop an AI-based CRP (CIRP) framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.

High-efficiency solar cells with Cs₂AgBiBr₆/CsSnCl₃ absorbers achieving 21.75% PCE. Impedance and noise analyses reveal enhanced charge transport.

Reversible data hiding in encrypted images (RDHEI) has gained significant popularity among security and privacy researchers as well as users, because of its features such as reversibility, embedding capacity (EC), and security. To enlarge the EC while ensuring the complete reversibility and security, we propose a bit-plane based RDHEI method based on multi-level blocking with quad-tree. The proposed method uses median edge detector (MED) as well as difference predictor to transform the original input image into a low-magnitude difference matrix. The difference matrix is then encoded by first employing a novel quad-tree based bit-plane representation strategy to exploit the intra-bit plane correlation and subsequently by inter bit-plane redundancy mitigation strategy to exploit inter bit-plane level correlation, for significantly condensing their size. Thus, a bigger room is reserved inside the cover image for embedding, so that a large amount of secret data can be hidden while ensuring the complete reversibility of the image. Experimental results validate the superiority of the proposed method over the state-of-the-art methods.