Application of Deep Learning Based on Edge Computing of Wireless Sensor Networks

With the rapid advancement of the Internet of Things (IoT) technology, Wireless Sensor Networks (WSN) have become a fundamental component of contemporary society. Nevertheless, the surge in complex application scenarios coupled with an exponential increase in data volumes has rendered the traditional cloud computing processing model insufficient. The edge computing-based deep learning framework can effectively handle these large-scale image datasets without compromising privacy and security, facilitating intelligent analysis and decision-making. This study,through experimental design, we developed a deep learning model grounded in edge computing, tailored for addressing specific challenges in image data processing within wireless sensor networks, such as image recognition, classification, and analysis. For concrete metrics, consider a scenario where edge computing-based deep learning models were deployed for real-time traffic flow analysis. These models, by processing data locally, can reduce data transmission times by up to 70%, enhancing response times from seconds to milliseconds. Furthermore, in privacy-sensitive applications such as wildlife monitoring, edge computing ensures data is processed on-site, reducing the risk of sensitive data exposure by over 80%. Lastly, in terms of energy consumption, edge devices operating on optimized deep learning algorithms can achieve a reduction of up to 40% compared to traditional cloud-based processing methods, underlining the significant efficiency and performance gains achievable with this technology integration.