Maintaining energy efficiency and privacy becomes more challenging as the use of WSNs grows more widespread in a variety of industries. Through the utilization of edge computing and deep learning techniques (DL) in WSNs, this abstract presents a novel approach to addressing these two difficulties. We present an architecture that incorporates edge computing nodes that are strategically positioned throughout the network to manage resource-intensive processes effectively, thereby reducing energy consumption and latency simultaneously. In addition, these models can identify anomalies and intrusions, as well as process data with privacy protection at the network’s edge. This results in an improvement in both security and privacy, while simultaneously maintaining energy economy. The purpose of this abstract is to investigate the potential benefits of this technique, which include better efficiency in the allocation of resources, increased resilience of the network, and enhanced protection of data privacy. In addition to this, it highlights key challenges, such as the complexity of models, the ability to manage enormous amounts of data, and the execution of algorithms on edge devices that have limited resources. By combining edge computing and deep learning, the purpose of this abstract is to pave the way for the development of WSNs that are secure, energy-efficient, and protect users’ privacy in a variety of applications.