Deep Learning Based Energy Intensive Computation Method for Anomaly Detection in Wireless Sensor Networks

Abstract

Abstract Wireless Sensor Networks (WSN) can be part of a tremendous number of applications. There are countless uses for Wireless Sensor Networks (WSN). A lot of WSN applications need real-time communication, meaning that the sensed data must reach the sink node by a specific date that the application has set. Real-time applications in WSNs are severely hampered by the limited resources of the sensor nodes (such as memory and power) and the lossy wireless communications. Furthermore, a lot of WSN routing algorithms place a heavy emphasis on energy efficiency, with delay not being the main issue. As a result, WSNs urgently require new routing protocols that are suitable for real-time applications, dependable, and energy-efficient. It accomplishes this by selecting potential neighbors who can transport the packet ahead of schedule and are qualified to take part in the routing process. It also calculates the relaying speed for each qualified candidate to reduce the latency of the selected paths. Additionally, it considers the available buffer size, hop count, and link quality of the chosen relays, which reduces end-to-end latency and uses the least amount of energy. By tackling these energy issues, we hope to achieve a compromise between anomaly detection models' sustainability and performance, guaranteeing that these technologies can be effectively used in actual healthcare settings. In order to preserve the efficacy of machine learning methods for identifying network anomalies, the paper's conclusion highlights the significance of optimizing computing resources. The suggested approach, EIC, uses anomaly detection for deep learning algorithms. Now that you are an expert, picture developing a system to identify cardiac arrhythmias. You may detect abnormal heartbeats early and notify doctors of possible dangers, such as heart attacks, by using thousands of EKG readings to train a machine learning model. Early alerts from this type of detection can save lives.