**1. Introduction**

The application of Internet-of-Things (IoT) technology to almost every sphere of life has resulted in immense growth in the number of IoT devices, the acceptance of IoT, IoT applications, and the volume of data uploaded to cloud systems. The international data corporation (IDC) forecasted that about 41 billion IoT-connected devices will be active in 2025, producing data surpassing 79 ZB [1]. Current cloud systems are not large enough to process and store this increase in IoT data traffic to meet real-time demands [2], which affects all IoT systems. Too many networks towards a distant cloud can cause high latency for sensitive IoT applications, such as healthcare [3], multimedia [4], and vehicular/drone applications [5, 6]. In addition, the centralization of the cloud may lead to a reduction of privacy in IoT data uploaded [7].

Fog nodes (FN) are used between IoT devices and cloud computing in fog computing (FC) architecture to reduce the distance data travels for processing in the cloud. Therefore, enabling cloud computing services from core network infrastructures to customer premises by using fog nodes, namely; switches, private servers, cloudlets, routers, etc. The closeness of fog nodes to edge devices results in a large

reduction in latency, energy-efficient, and optimal use of the network bandwidth for applications within agriculture, smart cities, etc. [8]. Also, fog nodes eliminate data duplication and empower applications using fog with local and near realtime intelligence. Regarding processing and storage abilities, fog servers are much smaller than cloud [9]. Still, fog servers' larger number and geo-distribution allow fog to alleviate cloud network congestion by servicing many IoT applications [8]. Indeed, an IoT application can be fully serviced by local fog servers without propagating IoT data into the cloud core network. Fog computing enables computational workload offloading through fog nodes which can further reduce the transmission latency and ease traffic congestions on the Internet. It also introduces many new services and applications that cannot fit the traditional cloud computing architecture well. For example, large-scale environmental monitoring systems can deploy computationally intensive applications at the sensors and utilize the fog computing architecture to achieve instantaneous response [9, 10].

This paper presents a survey on fog resources monetization, a payment system implemented for the services delivered by fog resources [11]. The wide use of IoT devices has made FC a paramount technology necessary to achieve real-time computation of IoT devices. The basic unit of FC, which is the FN, is defined in this literature, also highlighting the characteristics of FC. The available deployment and revenue models are divided into four and discussed briefly. Fog resource monetization was divided into centralized and decentralized architectures. The centralized architecture has a central authority, which determines the pricing model and quality of service (QoS), while the decentralized system has no central authority and no fixed pricing model.
