Hybrid algorithms for task offloading in vehicular networks: a comprehensive survey
TL;DR
This survey classifies hybrid algorithms into six basic categories and two extended categories, characterize environmental dynamism along three dimensions—channel dynamics, topology dynamics, and task dynamics—and define four operating regimes from static to highly dynamic.
Abstract
Task offloading in vehicular networks (VNets) is complicated by fluctuating channels, dynamic topologies, and bursty task arrivals. No single algorithmic paradigm performs reliably across all dynamic conditions, making hybridization a compelling approach. Based on patterns observed in the literature, this survey classifies hybrid algorithms into six basic categories and two extended categories. We characterize environmental dynamism along three dimensions—channel dynamics, topology dynamics, and task dynamics—and define four operating regimes from static to highly dynamic. We review studies across these categories and regimes and compare their hybridization mechanisms, validation conditions, and reported limitations. Based on this analysis, we identify key research gaps in online dynamism detection, cross-category benchmarking, and runtime meta-control. Unlike prior surveys that primarily catalog algorithm combinations, this survey provides a structured taxonomy, an environmental characterization framework, and a diagnostic perspective to guide future research.