Intruder Detection in Fog Computing for Smart Surveillance

Project Category

Computer Vision

1.Introduction Problem Statement

Surveillance systems play a crucial role in maintaining security and public safety. However, traditional cloud-based video analytics face challenges such as high latency, bandwidth congestion, and excessive reliance on centralized resources. The delay in transmitting real-time video streams to cloud servers leads to inefficiencies in detecting and responding to security threats, particularly unauthorized intrusions.

This project aims to implement a fog-based smart surveillance system specifically designed for intruder detection using a software-only approach. The system will integrate YOLOv5 and Faster R-CNN to identify unauthorized persons in restricted areas. Additionally, it will optimize task scheduling and resource allocation in a simulated fog computing environment using iFogSim. The study evaluates system performance in terms of latency reduction, bandwidth optimization, and resource efficiency compared to traditional cloud-based approaches.

2.Objectives

·       Develop an intruder detection model for real-time surveillance using YOLOv5 and Faster R- CNN.

·       Implement a fog computing simulation framework for decentralized video processing.

·       Optimize task scheduling and resource allocation using AI-based techniques (MPSO & RL) in a software environment.

·       Evaluate system performance with iFogSim simulation.

·       Compare fog-based intruder detection with traditional cloud-based methods.

3.Functional Requirements

3.1System Overview

The proposed system will be entirely software-based and will be developed using simulated data

instead of physical hardware. Key functionalities include:

3.2Functional Requirements Real-time Intruder Detection

·       Detect unauthorized individuals in restricted areas using YOLOv5/Faster R-CNN.

·       Process simulated video feeds from pre-recorded datasets or real-time streams. Fog Computing Simulation

·       Use iFogSim to simulate a fog computing environment.

·       Optimize bandwidth by sending only security alerts to the cloud.

Task Scheduling & Resource Allocation

·       Implement Modified Particle Swarm Optimization (MPSO) for load balancing.

·       Use Reinforcement Learning (RL) for adaptive scheduling. Performance Evaluation & Simulation

·       Simulate fog-cloud architecture using iFogSim.

·       Measure response time, bandwidth usage, and energy efficiency. Software-Only Approach

·       No dependency on physical hardware such as edge devices, cameras, or sensors.

·       Utilize synthetic or publicly available surveillance datasets.