Divergent Selection Task Offloading Strategy for Connected Vehicles Based on Incentive Mechanism

نویسندگان

چکیده

With the improvements in intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside vehicle, which place extremely high demands on computing power onboard systems (OBSs). However, arithmetic a single CV often cannot meet diverse service in-vehicle system. As new paradigm, task offloading based vehicular edge has significant advantages remedying shortcomings single-CV balancing allocation resources. This paper studied computational high-speed without help roadside servers certain geographic areas. User (UVs) with insufficient offload some their tasks to nearby CVs abundant We explored driving model classification refine process. Additionally, inspired by game theory, we designed divergent selection strategy an incentive mechanism (DSIM), balanced interests both user vehicle vehicles. that contribute resources are rewarded motivate more join. A DSIM algorithm greedy was introduced maximize total rewards all volunteer while respecting will The experimental simulation results showed that, compared several existing studies, our approach always obtain highest reward for lowest latency

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ژورنال

عنوان ژورنال: Electronics

سال: 2023

ISSN: ['2079-9292']

DOI: https://doi.org/10.3390/electronics12092143