Cuckoo Search-Enabled Task Scheduling and Cache Updating in Vehicular Edge-Fog Computing

With the emergence of computation-intensive vehicle applications, vehicle edge-fog computing (VEFC) is playing an increasingly important role in intelligent transportation systems. In this article, we study the task scheduling and cache updating (TSCU) problem in VEFC, where vehicles can choose thre...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 74; no. 6; pp. 9656 - 9670
Main Authors Shen, Qiaoqiao, Hu, Bin-Jie, Xia, Enjun
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Cache updating
Collaboration
Computational modeling
cuckoo search
Delays
Edge computing
Efficiency
Greedy algorithms
Intelligent transportation systems
Linear programming
Load modeling
Mixed integer
Nonlinear programming
Optimization
Processor scheduling
Proposals
Scheduling
Search algorithms
Servers
Simulation
Task scheduling
V2I offloading
V2V offloading
Vehicle-to-infrastructure
Vehicular ad hoc networks
vehicular edge-fog computing
Online AccessGet full text
ISSN0018-9545
1939-9359
DOI10.1109/TVT.2025.3540639

Cover

More Information
Summary:With the emergence of computation-intensive vehicle applications, vehicle edge-fog computing (VEFC) is playing an increasingly important role in intelligent transportation systems. In this article, we study the task scheduling and cache updating (TSCU) problem in VEFC, where vehicles can choose three modes to process their tasks: local computing, vehicle-to-vehicle (V2V) offloading, and vehicle-to-infrastructure (V2I) offloading. Service providers can dynamically update their caches to improve the system offloading efficiency. To maximize the offloading efficiency, we formulate the TSCU problem as a mixed-integer non-linear programming (MINLP) problem and develop an improved discrete cuckoo search algorithm to solve the optimization problem. Our proposed algorithm explores the global environment via Levy flight. On the other hand, the algorithm convergence rate is accelerated by generating the initial population based on a greedy algorithm and its variants and updating the discard probability. Simulation results show that our proposal has achieved an up to 12% improvement in offloading efficiency compared to the benchmark schemes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3540639