Optimizing Network Lifetime via Deep Q‐Network Driven Intelligent Aggregation in Heterogeneous Sensor Ecosystems

Abstract

The proliferation of heterogeneous sensor nodes in modern wireless sensor networks (WSNs) necessitates a paradigm shift from rigid, static routing regimes toward autonomous, resource‐aware operational management. Conventional data aggregation and clustering heuristics frequently fail to mitigate the nonlinear energy depletion profiles and high data redundancies inherent to dense Internet of things (IoT) ecosystems. This paper proposes a deep Q‐network–driven intelligent aggregation (DDIA) framework designed to maximize network longevity through self‐learning optimization. The DDIA architectural framework incorporates a localized deep Q‐network (DQN) agent. This agent dynamically monitors real‐time network state vectors—specifically tracking node residual energy, local traffic intensity, and spatial data correlation coefficients. Based on these inputs, the model executes optimized cluster‐head selection sequences and multihop routing trajectories. A key innovation of the proposed work is the implementation of bitwise‐rotation entanglement (BRE) for data fusion. In contrast to standard arithmetic or statistical aggregation methods, the proposed BRE technique introduces nonlinear circular rotation primitives. This approach simultaneously compresses and encrypts transient data packets. Consequently, the mechanism maintains a minimal computational footprint, rendering it highly compatible with resource‐constrained edge‐tier hardware. This creates a dynamic entropy model where the aggregation intensity is adaptively tuned by the DQN agent based on information uncertainty and node energy levels. Simulation results involving 500 heterogeneous nodes demonstrate that the DDIA framework extends the first node dead (FND) milestone by 38% and reduces energy consumption per bit (Ebit) by 42% compared with state‐of‐the‐art protocols like HEED and LEACH. The findings validate the framework's efficacy in providing a scalable, secure, and energy‐efficient solution for next‐generation industrial WSNs.