Intrusion Monitoring in Military Surveillance Applications using Wireless Sensor Networks (WSNs) with Deep Learning for Multiple Object Detection and Tracking

Terrestrial Wireless Sensor Networks (WSNs) are used in military environments for region surveillance, healthcare systems for soldiers, and, smart transport, and logistics, etc. In surveillance applications, the sensor nodes are deployed randomly in the field to observe the events of interest, movement of humans, or vehicles. In these sensor networks, the image or video is captured by the camera module. Many times it becomes difficult to correctly detect the intrusion or anomalous activity in the field because the image being captured maybe not clear enough due to prevailing weather conditions, the amount of light, and other reasons. In this paper, in addition to a WSN Surveillance System for military applications, we have used Convolutional Neural Network (CNN) for analyzing and understanding the content of the captured images and videos. CNN is a deep learning neural network that detects and tracks automatically the important features without any human supervision. The distinctive layers of each class are learned by themselves and have the highest accuracy of prediction. The results of the implementation for four test images captured in different conditions show an accuracy of 92%. The results of the video tracking yield the Object Tracking Efficiency of 80.35%.

I. Introduction

A Wireless Sensor Network (WSN) can be defined as a network of devices that are capable of communicating the information collected from a monitored field through wireless links. The gathered data gets forwarded through multiple nodes, with the help of a gateway these nodes are connected to other networks like wireless Ethernet [l]. A sensor node is a microcomputer consisting of an inbuilt-sensor, microcontroller, a wireless module, and an antenna. These nodes are spatially distributed over the region of interest known as a sensor field. The sensor nodes are capable of communicating with each other as well as the centralized Base Station (BS). The Base Station is connected to the internet through which the information passed on by the nodes can be disseminated amongst the users. The different applications of WSN include environmental and physical conditions monitoring, home and office automation, precision agriculture, object tracking, smart healthcare systems, building monitoring, and, smart energy and grid systems, and, industrial applications. The remote applications of WSN include military surveillance, disaster relief operations like flood control, earthquake, landslide detection, and ocean monitoring for detecting tectonic movements.