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, and provides reliable technical support for high-performance environmental parameter detection and intelligent early warning systems.
Abstract
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.
Wireless Sensor Networks (WSNs) use resource-constrained sensor nodes to continuously monitor ambient conditions and serve as data sinks for various Internet of Things (IoT) applications. However, maximizing Energy Efficiency (EE) while maintaining reliable data delivery remains a significant challenge. Existing clustering and routing algorithms struggle with issues such as uneven energy consumption, premature node failures, and poor network performance. Additionally, many existing metaheuristic schemes are not adaptable to dynamic network environments, which leads to ineffective energy management. To address the above issues and enhance the energy efficiency of IoT-based WSN, this research introduced a novel Energy-Aware Cluster-Optimized Intelligent Routing (EACO-IR) protocol. The EACO-IR protocol performs in three stages: stable cluster formation, Cluster Head (CH) selection, and energy-aware route finding. At first, stable clusters are formed using the Hybrid Fuzzy–Density Adaptive Kronecker Clustering (HF-DAC) algorithm. Subsequently, CHs are optimally selected using the Mutation-Enhanced Armadillo–Devil Optimization (MEADO) based on a multi-objective function for the Base Station (BS). Finally, the Multi-level Energy-Aware Attention Transformer- Based Reinforcement Learning is introduced to create intra and inter-cluster data travel ways to minimize communication overhead from SNs to the BS. Experimental results show that the proposed protocol yields an average throughput of 4 Mbps, an average Packet Delivery Ratio (PDR) of 98.71%, and an end-to-end latency of 0.06 seconds, outperforming current state-of-the-art clustering and routing algorithms. Ultimately, this framework establishes a highly adaptable template for deploying self-optimizing, long-lasting IoT architectures capable of supporting real-time data streaming without premature network degradation.
P. Kumbhar, A. Naik· International Journal of Ele...· 0 citations
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.
V. Barbudhe, Shruti Dixit· International journal of com...· 0 citations
Wireless Sensor Networks (WSNs) are widely used in environmental monitoring, healthcare, industrial automation, and Internet of Things (IoT) applications. However, limited energy resources and hotspot formation near the base station significantly reduce network lifetime and communication efficiency. This study proposes a Hotspot-Aware Load-Balanced Clustering and Routing Protocol (HALBCRP) to improve energy utilization and mitigate hotspot formation in WSNs. The proposed protocol integrates energy-aware cluster formation, hotspot-aware cluster head selection, dynamic load balancing, and fault-tolerant multi-hop routing into a unified framework. Sensor nodes are grouped using an energy-distance-based clustering strategy, while cluster heads are selected based on residual energy, connectivity, traffic load, and proximity to the base station. To prevent energy holes and traffic congestion, overloaded cluster heads dynamically redistribute traffic to neighboring cluster heads. The performance of HALBCRP was evaluated using MATLAB and compared with ITSA-UCHSE, HHDAP, Q-DAEER, and IPSO based on energy consumption, average throughput, number of alive nodes, number of dead nodes, and network lifetime. Simulation results show that HALBCRP achieves lower energy consumption (0.605–1.053 J), maintains a higher number of alive nodes throughout the simulation, and attains an average throughput of 28.62 packets per round. Furthermore, the protocol records superior network lifetime values of FND = 800 rounds, HND = 1150 rounds, and LND = 1340 rounds. These results demonstrate that HALBCRP effectively mitigates hotspot formation, balances network load, and significantly extends network lifetime compared with existing approaches.
Okehie Baslem, I. D. Ikpaya, Onyeanusi Ugochukwu Chimobi et al.· International Journal of Tre...· 0 citations
Energy management is a key challenge in wireless sensor networks (WSNs), primarily due to the limited battery resources of sensors, which shorten the network lifespan. Uneven energy consumption and high routing overhead significantly reduce network longevity. In this paper, we present an energy efficient clustering method called green hybrid-low energy adaptive clustering hierarchy (GH-LEACH). The proposed method selects balanced and energy-efficient cluster heads (CHs) during the setup phase by considering residual energy, node density, node centrality, and distance to the base station (BS). Additionally, an adaptive CH rotation mechanism and multi-hop communication among CHs are incorporated to reduce transmission energy and distribute the network load more effectively. Results demonstrate that GH-LEACH minimizes network overhead, extends the time until the first node dies (FND), improves overall network performance, and enhances throughput compared to existing protocols such as conventional LEACH, LEACH-C, TL-LEACH, and LEACH-F. This approach distributes the energy load among sensors more evenly, especially in denser networks.
M. I. Raheem, Mazin Ismael Raheem· Indonesian Journal of Electr...· 0 citations
The results indicate that E2CMR improves energy efficiency, network stability, and routing performance and is applicable for large-scale energy-constrained WSNs.
S. Priyadarshini, A. T.· International journal of com...· 0 citations
Simulations conducted in MATLAB R2019a validate that the proposed MOPSO outperforms existing algorithms such as LEACH, LEACH-FL, LEACH-FC, KM-PSO, EECHS-ARO, HSWO, and EECHIGWO by mitigating premature convergence and enhancing CH selection accuracy.
Monisha Gupta, Chandrasekar Vadivelraju· Review of Computer Engineeri...· 0 citations
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