The location of participants in Location-based CrowdSensing (LCS) represents important information for task completion. Tasks in Areas with High Concentration of Participants (AHCP) can be completed quickly, whereas task completion is difficult in Areas with Sparse Participants (ASP). Incentive mechanisms are necessary to motivate participants to move toward ASP. Previous studies have faced two main problems. First, most incentive mechanisms assume that participant motivation is not affected by external factors. Second, when participants fail to complete tasks, only the cost of the participant is considered the loss. However, reference effect from behavioral economics proves that participants are influenced by both internal and external factors. Furthermore, loss aversion studies have shown that participant evaluations of loss are more severe than simple costs. Therefore we propose an Incentive mechanism based on Behavioral Economics (IBE) consisting of two schemes for Participant Selection (IBE-PS) and Payment Decisions (IBE-PD). Based on reference effect, IBE-PS is proposed to control the task selection and pricing of participants. Based on loss aversion, IBE-PD is proposed to encourage participants to complete tasks in ASP many times. Theoretical analysis and simulation results demonstrate that IBE can improve the task completion rate, the participant utility, and the platform welfare.

In mobile crowdsensing, many mobile devices are collectively used to complete complex sensing tasks. Most tasks require users to consume resources to ensure continuous performance over multiple periods of time. Therefore, it is important to incentivize enough users to continuously participate in the tasks. However, there are two issues with current incentive mechanisms. First, most studies are designed for maximizing the revenue of a single round of tasks rather than long-term incentives. Second, although some studies use historical data to design mechanisms for long-term operation, the law of diminishing marginal utility is not considered; thus, the actual performance is lower than expected. In this study, the concepts of capital deposit and intertemporal choice from behavioral economics are introduced to explain the principle of addiction, which is a representative long-term incentive. Consequently, an Addiction Incentive Mechanism (AIM) is proposed. It influences the utility and demand functions of users by accelerating the accumulation of capital deposits and promoting users to become addicted to cooperative behavior. It also mitigates the effect of diminishing marginal utility through intertemporal choice theory to maintain user engagement. Simulations demonstrate that AIM improves participation and repetition rates compared with the state-of-the-art mechanisms.

The Internet of Vehicles (IoV) is composed of road side units (RSUs) nodes and vehicle nodes. Because the limited bandwidth of RSUs can not satisfy massive requests from vehicle nodes, offloading technology is proposed. Generally, spare vehicle nodes (SVs) with redundant resources are motivated to cache data from RSUs. However, due to the selfishness, the participation of SVs is generally low. Hence, an effective incentive mechanism to motivate SVs to offload is important. There are two problems with current incentive mechanisms: 1) all of them ignore loss aversion, which is the act of preferring avoidance of loss over acquiring equivalent gains. This act can lead to deviation in the final decision and 2) no attention is paid to the critical effects that initial allocation of property have on the final resource allocation. However, researches on behavior economics have found that loss aversion exists and the final resource allocation is affected by the initial configuration. Therefore, we propose an incentive mechanism called reverse auction based on endowment effect (RABEE). To the first problem, we introduce the endowment effect from behavior economics, and propose a term called endowment compensation for increasing the participation rate of SVs. For the second problem, by changing the initial allocation of endowment compensation, we illustrate through theoretical analysis and simulations the significance the initial configuration has in terms of the final resource allocation. Simulations show that RABEE increases the average utility of SVs by 6.8% compared with the conventional scheme, and it also improves participation rate by 2%.

The body sensor network (BSN), consisting of wearable or implantable devices, is a monitoring system applied to a healthcare environment based on the IoT technology. In BSN, prolonging the service cycle of the network is a major challenge due to the limited battery capacity and energy supply for sensors. To this end, improving energy efficiency and harvesting energy are the keys for the network to maintain sustainability. In this paper, we propose a transmission scheduling and energy harvesting strategy to manage energy supply and consumption, and build several dynamic models to capture the stochastic processes in BSN. Besides, a system utility maximization problem is formulated. Since this problem is a multi-objective mixed-integer optimization problem (MMOP) which is difficult to solve directly, we provide a solution framework where MMOP is decomposed into several subproblems by the Lyapunov optimization method. Based on this framework, we propose an online energy sustainability optimization algorithm to solve these subproblems, such as the matching problem and convex optimization problem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the appropriate sizes of the data buffer and battery capacity are derived, which can give a guidance to determine the sizes of these components. Simulation results show the impact of the system parameter on the utility and data and energy queues, and verify that the proposed strategy and methods can maintain the sustainable operation of BSN effectively.

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; 2) users’ behavioural preferences are consistent. Mental Accounting (MA) theory in behavioural economics proves that the utility of tasks obtained in different ways is non-fungibility and people’s preferences of behaviour 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 MAAIM (the Mental Accounting Auction Incentive Mechanism). Based on reference dependence, which couples 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. 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.

Location Dependent Mobile Crowdsensing (LDMC) often needs to collect data at different time points in various regions to ensure the coverage of sensing data. An incentive mechanism is needed to encourage participants to move to sparse areas and improve coverage. However, there are two problems: 1) most incentive mechanisms assume that the participants can get accurate information about tasks; 2) those mechanisms encourage participants through absolute utility so that the platform can obtain an improvement of incentive effect by increasing the reward. However, nodes usually get inaccurate information in reality. Moreover, behavioral economics finds that decision-making is often affected by relative utility rather than absolute utility. Path-dependence means that choices made on the basis of transitory conditions can persist long after those conditions change, which can solve the above problems. This study uses cognitive bias and the reference effect to explain the principle of path-dependence, and proposes a mechanism called Task Coverage promotion based on Path-dependence (TCPD). TCPD cultivates the cognitive bias of participants, causing an overestimation of expected utility. Then, it sets dynamic reference points to prevent participants from quitting early. The simulation results show that TCPD can improve the coverage and effectiveness of the platform.