## 2 Related Work
(2018) proposed a utility-based data computing scheme to provide sensing services in Internet of Things. In their scheme, a roadside unit (RSU) collects sensing data from vehicles. An RSU can collect sensing data from vehicles directly or request a roadside buffer (RSB) to collect sensing data from vehicles. Utilities for an RSU and RSBs are calculated based on the power and time cost needed for collecting sensing data. The interactions between the RSU and RSBs are modeled by a bargaining game, and both the RSU and RSBs try to maximize their utilities. In the bargaining game, RSBs tender proposals to the RSU. Then the RSU decides which RSBs or vehicles to obtain sensing data from. Vehicles do not participate in the bargaining game even though the bargaining game is similar to RevAuc. Even though their scheme can contribute to reducing cellular traffic between the RSU and vehicles through the maximization of utilities, the authors of ([PERSON] et al., 2018) did not mention how to apply their scheme to our scenario, where vehicles participate in RevAuc. [PERSON] et al. (2019) proposed a block-chain-based collaborative crowdsensing (BCC) scheme in an autonomous vehicular network. In their scheme, an edge computing device (ECD) on roadside collects sensing data from vehicles. Their scheme comprises (1) transaction architecture to protect the privacy of vehicles and ensure the security and nonrepudiation of a transaction, (2) an incentive mechanism to encourage the participation of vehicles, and (3) an algorithm to optimally select vehicles that execute tasks collaboratively with the target of minimizing a task execution cost (TED). Their scheme does not make any assumptions about the communication network between ECDs and vehicles. Even though their scheme may be applicable to reducing the cellular traffic between ECDs and vehicles with an appropriate design of the communication network and TED ([PERSON] et al., 2019), it does not mention how to apply their scheme to our scenario, where vehicles participate in RevAuc. [PERSON] et al. (2019) proposed a smart live video adaptive streaming technique over a multi-hop ad hoc network for road traffic monitoring. In (2019), a camera node transmits video data to a traffic monitoring server via a multi-hop D2D communication network. Using the multi-hop D2D network, their method can eliminate cellular traffic between the camera node and the traffic monitoring server while sharing video data between the camera node and the traffic monitoring server. The authors of (2019) did not mention how to apply their method to pull-type RevAuc. [PERSON] et al. (2019) proposed a method enabling a sensor node to compute psycho-acoustic parameters in real time using convolutional neural networks. Their method allows wireless acoustic sensor nodes to compute psycho-acoustic parameters without sending raw audio streams over a wireless acoustic sensor network. Their method can be used to reduce traffic in the wireless acoustic sensor network. The authors of (2019) did not mention how to apply their method to a pull-type RevAuc. In summary, existing studies on MCS have described how to reduce cellular traffic between a server and mobile hosts using D2D communication. However, as far as we know, no existing study can be applied to reducing the cellular traffic of pull-type RevAuc as it is currently employed.