## 2 Literature Review
### Demand Management Considering Congestion  
Another relevant stream of the literature is demand management methods considering congestion in mobile Internet networks. As the demand for data service grew dramatically, demand management has become a new challenge for service providers. Research on demand management is conducted mainly considering two concepts, time-dependent pricing and the traffic offloading mechanism, which aim to relieve network congestion by  
Figure 1: Two part pricing, three part pricing, and connection-speed-restriction pricing. giving users incentives to shift their mobile data demand to less-congested time periods or to supplementary networks (such as Wi-Fi) [18]. Time-dependent pricing has many variants. The most basic version is time-of-day pricing, which charges users a higher price for data usage during certain \"peak\" hours of the day than at other times of the day, so that network congestion in these time periods can be relieved [19; 20; 21]. In contrast to time-of-day pricing, day-ahead pricing computes new prices for different times of the next day in advance, based on predicted congestion levels. Reference [22] showed that day-ahead pricing benefits both service providers and customers, flattening the temporal fluctuation of data demand while allowing users to save money by choosing the time and volume of their usage. Besides, day-ahead pricing is also used in the electricity pricing context. Reference [23] considered a model for a single smart home and for a community (multiple homes) with different priorities. The priority was assigned to each appliance by electricity consumers. In their scheme, day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) were utilized to calculate electricity cost. Time-dependent pricing typically requires information about user demand [24; 25]. To obtain a reliable forecast of user demand, [PERSON] [26] built a multiple equation time-series model. For day-ahead forecasting, the mean absolute percentage error (MAPE) returned by the model over a period of 11 years was an impressive 1.36%, which is superior to all benchmarks that the authors chose. In addition to time-dependent pricing, service providers can manage demand by encouraging users to shift some of their data traffic to supplementary networks such as Wi-Fi [27]. [PERSON] [28] noted that the success of such a \"traffic offloading\" strategy largely relies on the economic incentives provided to users. [PERSON] [29] proposed a novel congestion-optimal Wi-Fi offloading (COWO) algorithm based on the subgradient method, which aims to obtain the optimal offloading ratio for each access point to maximize the throughput and minimize network congestion. [PERSON] [30] proposed a downloading mechanism in different vehicular networks that comprises an ad hoc network and a cellular network. In this mechanism, roadside units act as traffic managers to collect data from the Internet and then distribute them to vehicles in an approximately optimal manner. [PERSON] [31] constructed an intelligent offloading method for vehicular networks. They jointly utilized licensed cellular spectrum and unlicensed channels and used the real data of the traces of taxies to illustrate the effectiveness of the solution. In general, [PERSON] [18] stated that the realizations of time-dependent pricing and traffic offloading can help create a financial win-win solution for service providers and their users. Although time-dependent pricing and traffic offloading mechanisms have been proven to be helpful in demand management, they have some drawbacks and limitations [18; 32]. Time-dependent pricing makes the price change too frequently, which leads to extra costs, including operational costs and costs to help consumers in understanding and making a selection from a complex menu of choices [33]. Traffic offloading seems to be the direction in which many service providers are going today, but it requires investment in expanding wired and wireless network capacities.