HEOCP: Hybrid Energy-Optimized Clustering Protocol for WSNs Using Analytical Modeling and Deep Learning Integration

TL;DR

This paper introduces a new protocol for wireless sensor networks that uses a mix of energy modeling and deep learning to extend network lifetime by up to 60%.

Contribution

A hybrid protocol combining analytical energy modeling and deep learning for efficient cluster head selection in WSNs.

Findings

HEOCP extends network lifetime by up to 60% compared to conventional methods like LEACH and GA-based approaches.

The hybrid GA–ResNet framework shows high scalability and computational efficiency for large-scale IoT deployments.

The protocol effectively delays the first-node death and improves overall energy efficiency in WSNs.

Abstract

What are the main findings?
A derivable and fully solvable energy-consumption model was developed, allowing us to determine both the optimal distance range and the ideal number of clusters for selecting Cluster Heads (CHs).Proposes a hybrid CH selection framework that leverages ResNet-50 to capture the spatial features of the wireless sensor network (WSN) and smooth out the results across multiple rounds of a genetic algorithm (GA). By doing so, it eliminates the need for expensive real-time computations.The proposed Hybrid Energy-Optimized Clustering Protocol significantly extends the network lifetime under various WSNs scales.

A derivable and fully solvable energy-consumption model was developed, allowing us to determine both the optimal distance range and the ideal number of clusters for selecting Cluster Heads (CHs).

Proposes a hybrid CH selection framework that leverages ResNet-50 to capture the spatial features of the wireless sensor network (WSN) and smooth out the results across multiple rounds of a genetic algorithm (GA). By doing so, it eliminates the need for expensive real-time computations.

The proposed Hybrid Energy-Optimized Clustering Protocol significantly extends the network lifetime under various WSNs scales.

What are the implications of the main findings?
An energy optimization theoretical framework is provided that can be widely applied to large-scale or dynamic WSNs.Using deep learning to decrease computational needs makes “intelligent CH selection” possible in real-world IoT systems.The Hybrid AI + Analytical Modeling approach has been shown to be beneficial in extending the lifetime of WSNs.

An energy optimization theoretical framework is provided that can be widely applied to large-scale or dynamic WSNs.

Using deep learning to decrease computational needs makes “intelligent CH selection” possible in real-world IoT systems.

The Hybrid AI + Analytical Modeling approach has been shown to be beneficial in extending the lifetime of WSNs.

Wireless Sensor Networks (WSNs) play a pivotal role in Internet of Things (IoT) applications; however, their lifetime is fundamentally constrained by the limited energy of sensor nodes. This paper introduces a Hybrid Energy-Optimized Clustering Protocol (HEOCP) that combines analytical modeling of radio energy consumption with deep learning–assisted cluster-head (CH) selection. First, an analytical framework is developed to determine the distance-constrained CH eligibility region and the optimal number of clusters, thereby minimizing redundant transmissions and balancing energy consumption. Then, a genetic algorithm (GA) is used to determine the best cluster head configuration. These configurations are then trained by a ResNet-50 deep network and averaged to reduce noise, allowing for real-time cluster head prediction without repeatedly performing expensive heuristic optimization, resulting in more steady performance. Extensive simulations under various network scales demonstrate that HEOCP extends network lifetime by up to 60% compared with conventional LEACH and GA-based approaches, effectively delaying the first-node death and improving overall energy efficiency. Furthermore, the hybrid GA–ResNet framework exhibits high scalability and computational efficiency, making it suitable for large-scale IoT deployments. The results confirm that integrating analytical energy modeling with deep learning provides a powerful and sustainable paradigm for intelligent energy management in future IoT-enabled WSNs.

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