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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.
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Collaborative Cloud-Edge-Local Offloading for Multi-Component Applications
With the explosion of intelligent and latency-sensitive applications such as AR/VR, remote health and autonomous driving, mobile edge computing (MEC) has emerged as a promising solution to mitigate the high end-to-end latency of mobile cloud computing (MCC). However, the edge servers have significantly less computing capability compared to the resourceful central cloud. Therefore, a collaborative cloud-edge-local offloading scheme is necessary to accommodate both computationally intensive and latency-sensitive mobile applications. The coexistence of central cloud, edge servers and the mobile device (MD), forming a multi-tiered heterogeneous architecture, makes the optimal application deployment very challenging especially for multi-component applications with component dependencies. This paper addresses the problem of energy and latency efficient application offloading in a collaborative cloud-edgelocal environment. We formulate a multi-objective mixed integer linear program (MILP) with the goal of minimizing the systemwide energy consumption and application end-to-end latency. An approximation algorithm based on LP relaxation and rounding is proposed to address the time complexity. We demonstrate that our approach outperforms existing strategies in terms of application request acceptance ratio, latency and system energy consumption.
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- Award ID(s):
- 2127605
- PAR ID:
- 10381004
- Date Published:
- Journal Name:
- Proceedings of Sixth ACM/IEEE Symposium on Edge Computing - The 2nd Workshop on Edge Computing and Communications(EdgeComm 2021)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
