A Framework for Designing WSNs Using Dynamic Programming for Better Performance

Abstract

Wireless Sensor Networks (WSNs) are essential for many applications, including smart cities, industrial automation, and environmental monitoring. However, maximizing the durability and performance of these networks is a major problem because of the limitations on processing power and battery life. To enhance routing effectiveness, energy management, and overall network performance, this research proposes a methodology for constructing WSNs utilizing dynamic programming.

The suggested architecture balances network demand and minimizes energy consumption by optimizing routing decisions using dynamic programming techniques. The model finds the best routes for data transmission by considering energy levels, traffic patterns, and real-time sensor conditions. This ensures dependable communication with little energy overhead. For adaptive sleep-wake scheduling, the framework also incorporates an Artificial Neural Network (ANN), which enables sensor nodes to switch to low-power modes during idle times while preserving network responsiveness.

The framework also includes cluster creation and management algorithms, which group sensor nodes according to proximity and energy levels to improve scalability and efficiency. By further minimizing duplicate transmissions, data aggregation techniques enhance overall energy efficiency. Extensive simulations used to evaluate performance show that, in comparison to conventional methods, the suggested architecture greatly increases the network's operational lifetime while enhancing throughput, latency, and fault tolerance.

According to experimental findings, integrating machine learning methods with dynamic programming improves WSN decision-making, resulting in more efficient resource allocation and energy saving. ANN-based models' capacity to adapt to changing network circumstances is ensured by their integration with routing and sleep scheduling systems. In order to better optimize the framework for large-scale WSN deployments, future research will concentrate on integrating reinforcement learning.