[Paper Review] A Contract-based Incentive Mechanism for Energy Harvesting-based Internet of Things

Zhanwei Hou, He Chen+3 more|arXiv (Cornell University)|Mar 17, 2017

Energy Harvesting in Wireless Networks12 references3 citations

TL;DR

This paper proposes a contract theory-based incentive mechanism to enable energy trading between data access points (DAPs) and energy access points (EAPs) in RF energy harvesting-based IoT systems, overcoming information asymmetry by designing type-specific contracts that ensure individual rationality and incentive compatibility. The mechanism maximizes the DAP’s expected utility and social welfare, achieving over 85% of the optimal performance with complete information under asymmetric conditions.

ABSTRACT

By enabling wireless devices to be charged wirelessly and remotely, radio frequency energy harvesting (RFEH) has become a promising technology to power the unattended Internet of Things (IoT) low-power devices. To enable this, in future IoT networks, besides the conventional data access points (DAPs) responsible for collecting data from IoT devices, energy access points (EAPs) should be deployed to transfer radio frequency (RF) energy to IoT devices to maintain their sustainable operations. In practice, the DAPs and EAPs may be operated by different operators and a DAP should provide certain incentives to motivate the surrounding EAPs to charge its associated IoT device(s) to assist its data collection. Motivated by this, in this paper we develop a contract theory-based incentive mechanism for the energy trading in RFEH assisted IoT systems. The necessary and sufficient condition for the feasibility of the formulated contract is analyzed. The optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulation results demonstrate the feasibility and effectiveness of the proposed incentive mechanism.

Motivation & Objective

• To address information asymmetry in RFEH-based IoT systems where EAPs may misreport private information such as energy costs and channel conditions.
• To design an incentive mechanism that ensures EAPs truthfully reveal their types through contract selection, overcoming adverse selection in energy trading.
• To maximize the DAP’s expected utility and overall social welfare in a multi-type EAP environment with incomplete information.
• To develop a feasible contract design that satisfies individual rationality (IR) and incentive compatibility (IC) constraints under asymmetric information.
• To evaluate the performance of the proposed mechanism against benchmarks, including optimal schemes with complete information and linear pricing schemes.

Proposed method

• The DAP designs a set of energy-reward pairs (contracts) tailored to different EAP types, where each type corresponds to a unique combination of energy cost and channel gain.
• The contract design uses contract theory to model the incentive mechanism under adverse selection, ensuring EAPs self-select the contract intended for their type.
• The optimal contract is derived by solving a constrained optimization problem that maximizes the DAP’s expected utility while satisfying IR and IC conditions.
• The IR condition ensures each EAP receives non-negative utility when selecting its intended contract, while the IC condition ensures no EAP benefits from selecting a different contract.
• The system models EAPs as heterogeneous agents with private types, and the DAP uses the revealed type through contract choice to infer private information.
• The performance is evaluated via simulations with parameters such as path loss, noise power, and channel gains, using throughput and social welfare as key metrics.

Experimental results

Research questions

• RQ1 How can a DAP design an incentive mechanism to elicit truthful private information (energy cost and channel gain) from self-interested EAPs in an RFEH-based IoT system?
• RQ2 What contract structure ensures both individual rationality and incentive compatibility in the presence of information asymmetry?
• RQ3 How close can the proposed contract-based mechanism perform relative to the optimal solution with complete information?
• RQ4 How does the proposed mechanism compare to linear pricing schemes in terms of social welfare under asymmetric information?
• RQ5 What is the impact of channel quality (γ) on the performance of the proposed incentive mechanism?

Key findings

• The proposed contract-based mechanism satisfies both individual rationality and incentive compatibility constraints, ensuring EAPs truthfully reveal their types by selecting the contract designed for their type.
• Simulation results show that each EAP achieves its maximum utility only when selecting the contract item tailored to its own type, confirming the IC condition is met.
• The social welfare achieved by the proposed mechanism exceeds 85% of the optimal social welfare under complete information, especially as the signal-to-noise ratio (γ) increases.
• The performance of the proposed mechanism approaches the upper bound of the optimal scheme with complete information, demonstrating its effectiveness in overcoming information asymmetry.
• In contrast, the linear pricing scheme achieves less than 50% of the optimal social welfare, highlighting the inefficiency of ignoring EAP heterogeneity.
• The mechanism enables the DAP to infer EAP private information (type) through contract selection, effectively resolving the adverse selection problem in energy trading.