# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction
### State of the Art
This system uses commercially available components and standard Wi-Fi signals for the detection and path prediction of moving objects. Specifically, it employs channel state information that may be affected by human movement [19]. Motion in a picture is estimated using a statistical-based model. Where in location is linked to the variance of moving objects. Object detection uses recursion via a Kalman filter to detect an object in a moving image [20]. Naive Bayes and Logistic Regression have been investigated in this regard. The end-to-end system described here allows mm wave radar to be classified in addition to range detection [21]. The 3D images provided by the (RF) are understandable according to human nature, addressing issues such as geometric and semantic perception tasks or classification and pose estimation addressed by supervised learning. Data gathered via RF are tested with the help of trained optical images taken on the other side of a wall. The algorithm is based on the nearest neighbor or neural network, using a CNN-based algorithm as well as pose estimation in thick material. However, some limitations related to shape classification and the filtering of the unwanted signal from original signals also exist, which can be nullified or addressed [22]. Utilizing deep learning techniques for categorization will steer research in a new path because radar signals carry important information on items hidden behind an obstruction. This study employs a convolutional neural network (CNN) and wall-penetrating radar waves to classify human postures behind walls. The suggested methodology yields amazing and effective outcomes [23]. The initial stage of intelligent video surveillance is the most important process of moving object detection, which entails differentiating the motion of foreground objects from a background model. The objective is to accurately segment and classify the moving objects, distinguishing them from other regions in an image. Enhancements have been achieved through the integration of background subtraction and deep convolutional neural network algorithms for moving object detection and categorizing irregular falling activity. This monitoring process employs rank polling to ensure accuracy and reliability [24]. The incorporation of context awareness in the algorithm leads to notable improvements in energy consumption, execution cost, network utilization, delay, and fairness. These findings confirm the efficacy of leveraging context-aware information for optimizing network performance. Autonomous management is employed to collect contexts in the monitor-analysis-plan-execution loop for all offloading processes. The simulation results relating to this approach demonstrate the superiority of the method compared to local computing and offloading without considering context-aware algorithms with respect to various metrics, including energy consumption, execution cost, network usage, delay, and fairness [25]. The early detection of fire is crucial for preventing loss of life and minimizing financial costs. Smoke and gas sensing plays a vital role in this regard. The main objective in this regard is to design an affordable and user-friendly system capable of operating in three distinct modes: gas and fire leakage detection as well as temperature monitoring. The related study aims to expedite the identification and characterization of fire incidents by equipping each node with multiple sensors for collecting temperature, smoke, and fire-flickering data. In the event of fire detection, an automatic notification is sent to a designated officer via email. By implementing this system, the project enables faster response times and improved fire incident management [26]. The collection of constituent models under consideration encompasses a wide range of time series, providing predictions through linear or nonlinear approaches. Additionally, the evaluation of data points based on the Bayesian Information Criterion (BIC) enables the smoothing of certain observed data points, as necessary.