An Incentive Mechanism Combined With Anchoring Effect and Loss Aversion To Stimulate Data Offloading in IoT


With the rapid growth of mobile traffic, Internet of Things requires a large number of access points (APs) to provide data offloading capabilities. Owing to their selfishness, most APs refuse to participate. Therefore, an effective incentive mechanism is necessary. There are two common problems with current incentive mechanisms: 1) they generally assume that APs make decision by calculating expected utility and 2) they also do not consider the time restrictions of the mechanisms themselves. Thus, this paper proposes an incentive mechanism comprising the anchoring effect and loss aversion on offloading (AELAO). The creative of AELAO is its use of anchoring effect (i.e., the influence of referencing a user's decision) and loss aversion (i.e., consequences become more intolerable when facing the same losses and benefits). In order to solve the first problem, this paper proposes the reference factor and price-break discounts factor based on the anchoring effect. The reference factor is used as the anchor value (i.e., reference point) to determine the number of APs participating in data offloading. The design of the price-break discounts factor is based on the reference factor. For the second problem, this paper presents two new concepts: 1) time pressure and 2) regret value. Based on APs' loss aversion, time pressure can encourage them to participate in data offloading as soon as possible within the given time limit. Theoretical analysis and simulation results show that AELAO can increase the amount of data offloading while improving the offloading value, the average utility, and the participation rate of APs.


Computer Science

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IEEE Internet of Things Journal