Toward Energy-Efficient and Cost-Effective Task Offloading in Mobile Edge Computing for Intelligent Surveillance Systems

In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computatio...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on consumer electronics 2024-02, Vol.70 (1), p.4087-4094
Main Authors: Mondal, Manash Kumar, Banerjee, Sourav, Das, Debashis, Ghosh, Uttam, Al-Numay, Mohammed S., Biswas, Utpal
Format: Article
Language:English
Subjects:
Cameras
Cloud computing
Computation offloading
Constraints
Critical infrastructure
Edge computing
Electronic devices
Energy consumption
Intelligent surveillance system
Mobile computing
mobile edge computing
Mobile handsets
Object recognition
Public safety
Servers
Simulation
Surveillance
Surveillance systems
System effectiveness
Task analysis
task offloading
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2024.3362396