Energy efficiency and minimization of redundant transmissions are critical challenges in Wireless Sensor Networks (WSNs), especially in heterogeneous IoT environments where sensor nodes (SNs) are resource-constrained and deployed in remote or inaccessible areas. This paper aims to address the dual problem of uneven energy distribution and limited network lifespan by proposing a novel Artificial Protozoa Optimizer-based Cluster Head Selection (APO-CHS) algorithm. The proposed APO-CHS is inspired by the adaptive behavior of Euglena, integrating foraging, dormancy, and reproduction mechanisms to optimize cluster head and relay node selection through a multi-objective fitness function. The function incorporates residual energy, node density, neighbor distance, and energy consumption rate to guide the selection process effectively. Additionally, to tackle communication inefficiency, a lightweight data aggregation scheme is employed. This scheme reduces redundant transmissions by introducing a multi-level aggregation model that eliminates full, partial, and duplicate data in both intra-and inter-cluster communication. The simulation results demonstrate that the proposed framework improves network stability by 29.24%, extends network lifetime by 283.96%, and increases throughput by over 60% compared to baseline methods, thus making it a highly efficient and scalable solution for energy-aware IoT-enabled WSN applications.

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.