Radio frequency energy harvesting (RFEH) is a promising technology to chargeunattended Internet of Things (IoT) low-power devices remotely. To enable this,in future IoT system, besides the traditional data access points (DAPs) forcollecting data, energy access points (EAPs) should be deployed to charge IoTdevices to maintain their sustainable operations. Practically, the DAPs andEAPs may be operated by different operators, and the DAPs thus need to provideeffective incentives to motivate the surrounding EAPs to charge theirassociated IoT devices. Different from existing incentive schemes, we considera practical scenario with asymmetric information, where the DAP is not aware ofthe channel conditions and energy costs of the EAPs. We first extend theexisting Stackelberg game-based approach with complete information to theasymmetric information scenario, where the expected utility of the DAP isdefined and maximized. To deal with asymmetric information more efficiently, wethen develop a contract theory-based framework, where the optimal contract isderived to maximize the DAP's expected utility as well as the social welfare.Simulations show that information asymmetry leads to severe performancedegradation for the Stackelberg game-based framework, while the proposedcontract theory-based approach using asymmetric information outperforms theStackelberg game-based method with complete information. This reveals that theperformance of the considered system depends largely on the market structure(i.e., whether the EAPs are allowed to optimize their received power at the IoTdevices with full freedom or not) than on the information availability (i.e.,the complete or asymmetric information).
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