Incentive Mechanism Design for Wireless Energy Harvesting-Based Internet of Things

Abstract

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

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

“…To validate the feasibility, i.e., IR and IC, of the proposed scheme under information asymmetry, we present Fig. 2 to show the utilities of data owners with types 2, 4, 6 and 8, respectively [15]. From Fig.…”

“…We compare the profit of the task publisher obtained from the proposed contract theory model, and that from the Stackelberg game model in [15]. Figure 3 shows that the larger total number of data owner types leads to the larger profit of a task publisher.…”

“…Moreover, the Stackelberg game model with symmetric information has better performance than that of Stackelberg game model with asymmetric information. The reason is that the game leader (the task publisher) in the Stackelberg game with symmetric information can optimize its profit because of knowledge about the actions of followers (data owners), i.e., the symmetric information, and set the utilities of the followers to zero [15].…”

Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things

“…There have been separate studies over the optimization of WPT/MEC for the devices with limited [15], [23], [24] (referred to as Type-I hereafter) or sufficient [16]- [18], [22] (referred to as Type-II in the sequel) battery sizes. Type-I devices, with high self-discharge rate, consume all the harvested energy for data offloading in each time slot.…”

Toward Secure Blockchain-Enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

“…The (25) indicates that with the IC condition, when the IR constraint of type-1 verifier is satisfied, the other IR constraints will also hold. Therefore, the other IR constraints can be bound into the IR condition of type-1 verifier [30]. Lemma 3.…”