Auction Incentive Mechanism Research Articles

Blockchain Enabled Credible Energy Trading at the Edge of the Internet of Things

In order to promote the value circulation of energy resources and improve energy efficiency, credible energy sharing between Internet of Things Devices (IoTDs) came into being. However, sometimes IoTDs do not obtain the required energy in the required time period, resulting in less active participation in energy sharing. To address these challenges, this paper first proposes a credible energy transaction model based on the distributed ledger blockchain at the Edge of the Internet of Things, where the Edge Cloud Server (ECS) can collect a large number of surplus energy resources of IoTDs in a secure and credible energy sharing environment and share them with other IoTDs in urgent need of charging. Meanwhile, in order to attract IoTDs to participate in energy sharing for a long time and meet the energy demand of ECS to the maximum extent, a smart contract-based Expected Social Welfare Maximized double auction incentive mechanism of Single ECS to Multi-IoTDs (ESWM-StM) is proposed to enable dynamic and adaptive energy sharing from multiple IoTDs to a single ECS. In addition, this paper compares the proposed algorithm with the benchmark method in terms of energy-sharing cost and long-term utility. The simulation results show that the proposed incentive mechanism can enable IoTDs to provide more surplus energy per unit cost to meet the energy demand of ECSs, and can sustainably attract more energy trading participants to enhance the expected social welfare in the long term.

Crowdsensing From the Perspective of Behavioral Economics: An Incentive Mechanism Based on Mental Accounting

Crowdsensing is a new paradigm of applications that takes advantage of mobile devices to collect sensing data. Tasks in crowdsensing will consume users’ resources so that incentive mechanisms are necessary to encourage users’ participation. Existing incentive mechanisms are based on traditional economics, which have two common problems: 1) the utility of different tasks is fungible and 2) users’ behavioral preferences are consistent. Mental accounting (MA) theory in behavioral economics proves that the utility of tasks obtained in different ways is nonfungible and people’s preferences of behavior are inconsistent. Reference dependence, loss aversion, and sensitivity decline are the three characteristics of MA. Reference dependence means people evaluate outcomes relative to a reference point, and then classify gains and losses. Loss aversion refers to people’s tendency to prefer avoiding losses to acquiring equivalent gains. Sensitivity decline means that the marginal utility of MA about gains and losses is diminishing. Thus, this paper proposes an incentive mechanism called the MA auction incentive mechanism (MAAIM). Based on reference dependence, coupled with sensitivity decline, we establish an external reference environment and an internal reference point to motivate users. Based on loss aversion, we design a payment mechanism to encourage users to improve their data quality. The extensive simulation results show that MAAIM improves the number of users participating in sensing tasks, the utility of the sensing platform, and the quality of data collected by users.

Incentive Mechanisms for Crowdblocking Rumors in Mobile Social Networks

Mobile social networks (MSNs) have become an indispensable way for people to access information, express emotions, and communicate with each other. However, the advent and extensive use of MSNs has also created fertile soil for the breeding and rapid spread of rumors. Therefore, blocking the spread of rumors in MSNs has always been a hot topic in this field. With the idea of crowdsourcing, we propose a novel rumor control framework, called Crowdblocking , in which users can implement control schemes in a collaborative and distributed way, so that the rumors can be controlled more effectively. With the proposed framework, the main problem that arises is how to motivate more users to actively participate in rumor blocking activities. To this end, we design two effective incentive mechanisms in this paper. First, we propose an incentive mechanism based on the Stackelberg game for homogeneous control tasks. We theoretically analyze the Stackelberg equilibrium to maximize the utility of the network manager and users involved in blocking rumor tasks, and ensure that no user can improve its own utility by unilaterally changing the current strategy. Second, for heterogeneous control tasks, we design a real-time reverse auction incentive mechanism, which allows users to have more autonomy and freely customize their own plans to participate in control tasks. Also, we prove that the mechanism possesses the desired properties of task timeliness, computational efficiency, user rationality, manager profitability, and price truthfulness. Finally, we validate the efficiency of the proposed mechanisms through extensive simulation experiments on the real datasets.

Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing.

Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.