Optimization algorithms are crucial for energy-efficient routing in Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) because they help minimize energy consumption, reduce communication overhead, and improve overall network performance. By optimizing the routing paths and scheduling data transmission, these algorithms can prolong network lifetime by efficiently managing the limited energy resources of sensor nodes, ensuring reliable data delivery while conserving energy. In this work, we present Greylag Goose-based Optimized Clustering (GGOC), which aids in selecting the Cluster Head (CH) using the proposed critical fitness parameters. These parameters include residual energy, sensor sensing range, distance of a candidate node from the sink, number of neighboring nodes, and energy consumption rate. Simulation analysis shows that the proposed approach improves various performance metrics, namely network lifetime, stability period, throughput, the network’s remaining energy, and the number of clusters formed.

The rapid expansion of smart technologies and IoT has made Wireless Sensor Networks (WSNs) essential for real-time applications such as industrial automation, environmental monitoring, and healthcare. Despite advances in sensor node technology, energy efficiency remains a key challenge due to the limited battery life of nodes, which often operate in remote environments. Effective clustering, where Cluster Heads (CHs) manage data aggregation and transmission, is crucial for optimizing energy use. Motivated from the above, in this paper, we introduce a novel metaheuristic approach called Hippopotamus Optimization-Based Cluster Head Selection (HO-CHS), designed to enhance CH selection by dynamically considering factors such as residual energy, node location, and network topology. Inspired by natural behaviors, HO-CHS effectively balances energy loads, reduces communication distances, and boosts network scalability and reliability. The proposed scheme achieves a 35% increase in network lifetime and a 40% improvement in stability period in comparison to the other existing schemes in literature. Simulation results demonstrate that HO-CHS significantly reduces energy consumption and enhances data transmission efficiency, making it ideal for IoT-enabled consumer electronics networks requiring consistent performance and energy conservation.

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.

The rapid expansion of Internet of Things (IoT) applications, ranging from environmental monitoring to industrial automation, has placed unprecedented demands on wireless sensor networks (WSNs). Ensuring sustained operation and reliable data delivery under stringent energy constraints and uneven traffic patterns remains a vital research challenge. This paper introduces a cluster head (CH) structuring framework powered by a hybrid Superb Fairy Wren Optimization Algorithm (SFOA) that orchestrates dynamic CH rotation to balance energy usage and alleviate hotspots. A composite fitness function is formulated by integrating residual node energy, distance to sink, neighbor density, transmission latency, and energy consumption rate. Additionally, hierarchical data aggregation is employed at both intra and inter cluster levels to minimize communication overhead. Evaluation results demonstrate a 33.99 % increase in network stability duration and a 30 % extension in overall network lifetime compared to state of the art protocols. To our knowledge, this is the first integration of a Fairy Wren inspired metaheuristic with CH based clustering for WSN energy management, yielding significant improvements in network longevity and reliability.

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.

Abstract This paper proposes a novel reversible data hiding (RDH) technique using sorting and pairwise prediction error expansion (PEE) to improve embedding capacity (EC) while retaining the quality of cover image. The proposed scheme traverses alternate pixels of the cover image in a zig‐zag order to construct two independent sets for sequential embedding. The pixels of each set are sorted in an increasing order of their rhombus mean followed by a two‐pass data embedding by dividing the sets into 1 × 3 size blocks based on some pre‐defined criteria. In pass 1, two prediction errors are calculated for the first and the last pixels using their rhombus means; and pairwise mapping is modified and exploited to embed the secret data in such a way that the value of the first pixel is either increased or remains unchanged, and the value of the last pixel is either decreased or remains unchanged. In pass‐2, the middle pixel is utilised to predict the first and last pixels and the values of the first pixel and the last pixel are either decreased or remains unchanged and either increased or remains unchanged, respectively. In contrast to some existing recovery‐based methods, the proposed pass‐2 guarantees to complement the changes made in pass‐1, thereby boosting the quality along with increased EC. The experimental results show that the proposed method achieves better embedding performance than the state‐of‐the‐art RDH techniques. More specifically, the proposed method gets an increment by an average of 0.46 dB and 1.01 dB for embedding 10,000 bits and 20,000 bits respectively over its closest prior art.

Internet of Things (IoT)-enabled wireless sensor network (WSN) permits the development of various IoT-based applications, ranging from industry to education and military to agriculture. However, the common IoT devices usually have very limited battery power, which is not frequently rechargeable. Thus, an energy-efficient mechanism is required to operate IoT-enabled WSN. To address the limited power shortcoming of IoT-enabled WSN, we propose an optimized genetic algorithm (GA) for cluster head (CH) election (OptGACHE). The CH election using GA incorporates four different criteria, namely: 1) node density; 2) distance; 3) energy; and 4) heterogeneous node’s capability for the development of fitness function. These criteria help in optimizing intracluster distance, systematic utilization of node’s energy in the cluster, reducing hop count, and promoting selection of highly capable nodes for CHs. The proposed movable sink strategy shortens the length of communication distance between sink and CH and also diminishes the hotspot problem. Furthermore, the incorporated dynamic sensing range adjustment minimizes the overlapping of sensing range of CH along with cutting down transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, namely, network’s remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

This article proposes an optimized genetic algorithm (GA)-based sustainable and secure green data collection/transmission method for IoT-enabled WSN in healthcare by optimizing intracluster distance, systematic utilization of node’s energy, and reducing hop count. For secure transmission of data, the communication data is encrypted using stream cipher and a pseudo-randomly generated security key. Additionally, the proposed movable sink and data collection/transmission strategies shorten communication distance between sink and cluster head (CH) which diminishes the hotspot problem. The direct data collection helps in transmitting data directly to the sink, when the sinks are nearer to the sensor nodes with respect to CH. Further, the incorporated dynamic sensing range minimizes overlapping of sensing range with a significant decrement in the transmission energy. The simulation results show that the proposed protocol outperforms the existing protocols on the performance metrics, such as remaining energy, lifetime, stability period, throughput, and the number of clusters per rounds.

Pixel-value-ordering (PVO) is one of the most widely used reversible data hiding (RDH) framework which efficiently utilizes smooth pixels of the cover image to provide high-fidelity stego-image but with limited embedding capacity. This letter proposes an RDH scheme based on local moment driven pixel value ordering (LM-PVO) which further enhances the effectiveness of smooth pixel's utilization by dividing the fixed-size blocks into two groups. Pixels of each group are sub-divided into two sub-groups based on the local moment of the block so that correlation among the pixels of each sub-group is enhanced. Thus doing, the pixels of each sub-group are grouped based on their intensity values instead of their position as in the conventional PVO-based schemes; this enables information hider to embed a higher amount of secret data while also enhancing the stego-image quality. Experimental results also validate the superiority of the proposed scheme over the existing PVO-based RDH schemes.