# Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload
## 2 Related Work  
In 2011, Radisys Corporation published a white paper regarding the offloading of Internet data for mobile operators [4]. The report suggested that, to increase data growth and to provide low-cost services, data can be offloaded to a lower-cost network. In the case of WiFi offload, the report classified it into three categories: hard, optimized and integrated WiFi offload. Hard offload is driven by the user, and the device is configured to work with a private WiFi network. Optimized offload uses SIM-based authentication and should securely connect with public WiFi providers. Integrated offloading is part of the mobile network and supports integration in core networking. All of these offloading mechanisms require credible authentication, execution and pricing mechanisms. Researchers in [5] discussed two approaches to Internet Protocol (IP) flow mobility being developed, the Internet Engineering Task Force (IETF) and the 3 rd Generation Partnership Project (3 GPP). The researchers suggested that network-based flow mobility is encouraging technology. It can help telecommunication operators to extend their network capability and can help in providing low-cost services. Researchers in [6] discussed different data offloading mechanisms such as Local IP Access (LIPA), Selected IP Traffic Offload (SIPTO), and IP Flow Mobility. The researchers highlighted various issues to be addressed, such as network management, traffic handling, network deployment for data offloading, energy efficiency, etc. It is also suggested to work on providing desirable Quality of Service (QoS) for offloaded traffic and selecting an optimal offloading point. The coupling between WiFi and a cellular network during offloading in Radio Access Networks (RAN) can be classified into three different classes: loose, tight and very tight coupling [7]. After studying these three couplings, the researcher found that continuity of session during a vertical handover is a major cause of concern for network architectures. The researchers in one study, [8], analysed smartphone users' behavioural patterns during the usage of an offloading service. A total of 298 different users offloaded data from their smartphones to WiFi networks, and the correlation between the preferences of users and mobile data volume was studied. The authors presented a coefficient value-based mechanism to categorize the different types of users into various offloading behaviours. The authors emphasized the use of the offloading technique for cellular network users to save money and access better service. They proposed a WiFi-aware mobile data offloading technique through WiFi networks with deadline constraints. The proposed technique can finish file transmission before the deadline and can find optimal ways to reduce the computation required to decide the offloading schedule [9]. The authors in [10] proposed a Satisfaction-based Dynamic Bandwidth Reallocation (SDBR) scheme to improve network efficiency and user satisfaction. The analysis of their results shows the effectiveness of the methodology in increasing the overall revenue and average user experience. An NP-hard Target Set Selection (TSS) problem and heuristic optimization are used to propose a data offloading scheme. This scheme works on polynomial time complexity and can reduce traffic by 20% compared with the heuristic-only scheme [11]. Efficient offloading for users who are commuting is another problem which needs attention. Researchers in [12] proposed unsupervised learning-based techniques for improved offloading performance for commuting users. In [13], researchers proposed a dynamic game-theoretic model for crowd-sourced WiFi offloading, which can help mobile operators to solve their pricing problems. This technique is developed to support mobile operators to expand crowd-sourced WiFi networks and solve users' ever-increasing demand. Exponential learning-based minority game theory is also used in one study [14] to develop a distributed data offloading scheme.