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.

Software-defined networking (SDN) centralizes and abstracts network control, potentially introducing single points of failure. To enhance scalability and flexibility, a distributed SDN (DSDN) approach is essential. This research introduces MLB-DSDN, an energy-efficient multipath load-balancing protocol for flow control in DSDNs, with a specific focus on an eco-friendly aerial computing environment. MLB-DSDN protocol identifies multiple routes between source and destination nodes, dynamically distributing data packets based on an inverse proportionality mechanism relative to route traversal time. This strategy balances traffic loads across various channels, significantly reducing the total routing time for data packet delivery. Moreover, the proposed framework enhances network performance and resilience in dynamic, high-mobility environments. It achieves this by incorporating unmanned aerial vehicles (UAVs) and satellite nodes as mobile network components. Experimental results demonstrate that MLB-DSDN improves average response time by 14.40% and increases average transactions per second by 14.63%, surpassing state-of-the-art methodologies. The integration of UAVs and satellites contributes to an additional 10% improvement in network throughput and a 12% reduction in latency compared to ground-based solutions alone. These findings highlight the robustness and efficiency of MLB-DSDN in enabling seamless and reliable data dissemination across terrestrial and aerial networks. Thus, the proposed framework enhances scalability, reliability, and flexibility in aerial computing, offering a robust and adaptable solution for resilient modern networks.

Summary Internet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

Recent advancements in the field of the Internet of Medical Things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges, such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA)-based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this article. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

The vision for the sixth-generation (6G) network involves the integration of communication and sensing capabilities in internet of everything (IoE), towards enabling broader interconnection in the devices of distributed wireless sensor networks (WSN). Moreover, the merging of SDN policies in 6G IoE-based WSNs i.e. SDN-enable WSN improves the network’s reliability and scalability via integration of sensing and communication (ISAC). It consists of multiple controllers to deploy the control services closer to the data plane for a speedy response through control messages. However, controller placement and load balancing are the major challenges in SDN-enabled WSNs due to the dynamic nature of data plane devices. To address the controller placement problem, an optimal number of controllers is identified using the articulation point method. Furthermore, a nature-inspired cheetah optimization algorithm is proposed for the efficient placement of controllers by considering the latency and synchronization overhead. Moreover, a load-sharing based control node migration (LS-CNM) method is proposed to address the challenges of controller load balancing dynamically. The LS-CNM identifies the overloaded controller and corresponding assistant controller with low utilization. Then, a suitable control node is chosen for partial migration in accordance with the load of the assistant controller. Subsequently, LS-CNM ensures dynamic load balancing by considering threshold loads, intelligent assistant controller selection, and real-time monitoring for effective partial load migration. The proposed LS-CNM scheme is executed on the open network operating system (ONOS) controller and the whole network is simulated in ns-3 simulator. The simulation results of the proposed LS-CNM outperform the state of the art in terms of frequency of controller overload, load variation of each controller, round trip time, and average delay.

Summary The cloud services demand has increased exponentially in the last decade due to its plethora of services. It becomes a significant platform to compute large and diverse applications over the internet. On the contrary, on‐demand resource allocation to a variety of applications becomes a serious issue due to dynamic workload conditions and uncertainty in the cloud environment. Several existing state of art techniques often fails to allocate the optimal resources to forthcoming demands, leading to an imbalance workload over cloud platform, degrading the performance. This article introduces a secure and self‐adaptive resource allocation framework that addressed the mentioned issues and allocates the most suitable resources to users' applications while ensuring the deadline constraints. Further, the proposed framework is integrated with a metaheuristic algorithm named enhanced spider monkey optimization algorithm that is based on the intelligent foraging behavior of spider monkeys. The proposed algorithm finds an optimal resource for the user's application using the fission‐fusion approach and improves multiple influential parameters like time, cost, degree of load balancing, energy consumption, task rejection ratio and so on. The experimental CloudSim based results verified that the proposed framework performs superior to state of art approaches like PSO, GSA, ABC, and IMMLB.

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.