Multi-objective optimization approach for energy efficient clustering and routing in wireless sensor networks.

Enhancing Energy Efficiency in Wireless Sensor Networks through Genetic Algorithm-Based Routing Approaches

Modern technological systems rely heavily on Wireless Sensor Networks (WSNs), which support many kinds of applications, including but not limited to: (1) environmental monitoring (2) medical monitoring and (3) smart city/infrastructure development. One major problem with extending the overall life of the network is the limited power source of the sensor nodes; therefore, energy-efficient communication has become one of the primary research areas. The direction of this work is a GA-based routing mechanism developed to minimize energy usage within WSNs. The proposed methodology employs evolutionary operators (e.g., selection, crossover, and mutation) that will adaptively develop low-energy routing paths and provide for an even distribution of traffic across all nodes. Also, node clustering, dynamic data aggregation, and multi-objective optimization are all methods used to improve network energy efficiency while not compromising the reliability of the data or the stability of the network. The simulations performed show that the proposed GA-based routing protocol offers improved power consumption, packet delivery rate and overall longevity of the network when compared to traditional methodologies such as LEACH or other energy aware GA methods. Additionally, the implementation analysis indicates a very high level of adaptability to node movement and variable traffic patterns, suggesting that it is robust and scalable. Thus, this research will help promote the development of energy-efficient wireless sensor networks (WSNs) as well as lay a solid foundation for future intelligent routing and effective resource management within future WSNs.

A multi-objective energy-efficient clustering routing protocol for environmental monitoring in industrial IoT.

For Industrial Wireless Sensor Networks (IWSNs) serving industrial environmental monitoring tasks, clustering optimization, the core technology for network performance tuning, is a well-recognized NP-hard problem that directly determines the energy efficiency and communication reliability of the entire system. Such IWSNs are typically deployed in large-scale, unattended industrial fields to collect real-time, high-precision environmental data including air quality, water pollution levels and soil parameters. However, inherent constraints like limited node energy supply and unstable wireless links in these scenarios often lead to incomplete data collection and delayed early warning, which directly undermine the reliability of environmental monitoring. To address this challenge, this paper proposes CCNCSO-CRP, a novel energy-efficient clustering routing protocol based on a multi-objective clustering model that jointly considers four key metrics: total network residual energy, average transmission delay, packet loss rate, and the distance from cluster heads to the base station. The protocol is built on the newly designed Chaotic Clonal Niche Cockroach Swarm Optimization (CCNCSO) algorithm, which integrates chaotic initialization and evolutionary strategies including clonal selection and niche preservation to enhance population diversity and convergence speed. Extensive experimental validations are conducted on the CEC2008 and CEC2020 benchmark test suites, where the CCNCSO algorithm outperforms classical meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Osprey Optimization Algorithm (OFA), Komodo Mlipir Algorithm (KMA), Grey Wolf Optimizer (GWO), and Artificial Bee Colony (ABC). Furthermore, experimental evaluations under various IWSN environmental monitoring scenarios show that CCNCSO-CRP outperforms state-of-the-art protocols including LEACH-C, VSSLS-SIACR, and FOAEAUC-SARP by at least 11.5% in extending network lifetime, reduces average delay by no less than 9.5%, and cuts packet loss rate by a minimum of 40.9%. These results validate the effectiveness and superiority of the proposed protocol in improving clustering efficiency and network stability for environmental monitoring IWSNs, and provide reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.

Optimizing energy efficiency in wireless sensor networks through advanced cluster-based routing protocols

A cluster-based proactive routing protocol designed for three-tier energy-heterogeneous WSNs, aiming to enhance network lifetime and energy efficiency, and modified to address the challenge of long transmission distances for low-power nodes by strategically deploying them in specific network zones.

Multi-Mobile Sink-Based Clustering and Routing in Heterogeneous Wireless Sensor Networks Using the Crow Search Algorithm

Heterogeneous Wireless Sensor Networks (HWSNs) face conspicuous challenges in maximizing network lifetime due to uncurbed power utilization depletion at sensor nodes (SNs) and cluster heads (CHs). This study presents an optimal Multi-Mobile Sink-based Clustering and Routing (MSCR) control strategy using the multi-objective Crow Search Algorithm (CSA) to address resource-constrained node energy-efficiency challenges. The proposed CSA-based MSCR model enhances deployed node’s performance by intelligently coordinating SNs, CHs, and mobile sink (MS) to optimize the data acquisition process while reducing energy consumption. The CSA strategy is deployed for two crucial optimization operations: constructing optimal mobile sink trajectories and selecting energy-efficient cluster heads under limited resources and harsh environmental circumstances. Performance analysis compares the CSA-based model against established traditional metaheuristic methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). Large-scale simulations conducted under various network and harsh environmental conditions demonstrate significant improvements in critical performance metrics. The CSA-based MSCR approach achieves a 36% reduction in power consumption, 49% accelerated cluster formation and cluster head (CH) selection, and 42% improvements in data delivery efficiency compared to conventional approaches. Furthermore, the proposed model extends average network lifetime by 45% while maintaining data accuracy above 97%. The results endorse the usefulness of the CSA optimization strategy in solving composite multi-objective optimization problems in wireless sensor networks. This work contributes a strong and scalable solution for next-generation IoT applications requiring energy-efficient data collection in challenging deployment environments. The outcome highlights the strengths of metaheuristic algorithms like CSA in advancing MSCR control for WSNs, offering a promising alternative to traditional approaches for improving the data delivery and lifetime of the sensor networks.

An Enhanced LEACH-Based Dynamic Routing Framework for Energy-Efficient Heterogeneous Wireless Sensor Networks

The core research goal of this paper is to optimize dynamic routing protocols to improve the performance of the classic LEACH protocol in heterogeneous WSNs through a real-time adaptive scheme, which relies on two core methods: a cluster head selection mechanism based on the residual energy criterion, and a priority hop count strategy.

Optimized Energy Efficiency in Wireless Sensor Networks Using Dynamic Sub-station Placement and Q-learning-Based Cluster Head Selection