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Mobile edge computing (MEC) has become a promising solution to utilizedistributed computing resources for supporting computation-intensive vehicularapplications in dynamic driving environments. To facilitate this paradigm, theonsite resource trading serves as a critical enabler. However, dynamiccommunications and resource conditions could lead unpredictable tradinglatency, trading failure, and unfair pricing to the conventional resourcetrading process. To overcome these challenges, we introduce a novelfutures-based resource trading approach in edge computing-enabled internet ofvehicles (IoV), where a forward contract is used to facilitate resource tradingrelated negotiations between an MEC server (seller) and a vehicle (buyer) in agiven future term. Through estimating the historical statistics of futureresource supply and network condition, we formulate the futures-based resourcetrading as the optimization problem aiming to maximize the seller's and thebuyer's expected utility, while applying risk evaluations to relieve possiblelosses incurred by the uncertainties in the system. To tackle this problem, wepropose an efficient bilateral negotiation approach which facilitates theparticipants reaching a consensus. Extensive simulations demonstrate that theproposed futures-based resource trading brings considerable utilities to bothparticipants, while significantly outperforming the baseline methods oncritical factors, e.g., trading failures and fairness, negotiation latency andcost.

Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach