利用无线传感器网络预测在圆环区域使用无线传感器网络进行入侵探测的 k 障碍器数目的深入学习办法 (A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks)

from arxiv, Singh, A., Amutha, J., Nagar, J., & Sharma, S. (2022). A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks. Expert Systems with Applications, 118588

Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.

翻译：无线传感器网络(WSNS)是一个前景良好的技术,几乎在生命的每一个行走中都应用了极多的入侵探测和监视,WSNS的关键应用之一是对边境地区和国防设施进行入侵探测和监视。边境地区的面积以数百至数千英里的速度拉开,因此不可能对整个边境地区进行巡逻。因此,敌人可能从任何一点进入,没有监视,造成生命损失或摧毁军事设施。WSNS可能是解决边境地区入侵探测和监视问题的可行办法。在边境地区和附近的关键地区,如军事营地等发现敌人是时间敏感的任务,因为拖延几秒钟可能会造成灾难性的后果。因此,必须设计能够发现和探测整个边境地区的系统。在本文中,我们提议了一个深度学习架构,其基础是完全连接的向上方的货币货币级的货币模型(ANNE),用于准确预测用于快速入侵检测和预防的KBerrier的数值,以及军事营地等附近的货币储存库的准确度,我们用ANURL9的模型和传感器的频率,用于向前方的传输区域。