.png){kind=icon}
.png){kind=icon}
Return
Security-Aware Task Offloading in IoT Edge Networks Using Software-Defined Networking
.png){kind=icon}
PRE
AI
DOI：10.3390/mca31030072.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately 125 +/- 5 ms, a task completion ratio (TCR) of about 92 +/- 2%, and a security success rate (SSR) near 95 +/- 1.5%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions.
Keywords:
IoT edge computing
security-aware offloading
Software-Defined Networking (SDN)
heuristic task allocation
performance and security optimization
Journal
M
IF:
2.1
Papers:
125
Citations:
0