# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
###### Abstract  
Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence. However, the WSN network still has some limitations in computing power, storage resources, and battery life, which make the network is restricted for data transformation. It is due to less supportive battery power, and limited memory of nodes. The integration of WSN and cloud offers an open, adaptable, and more reconfigurable stage for different security checks and regulating requirements. In this paper, we discovered how WSN and cloud computing (CC) are integrated and help to accomplish different goals. Additionally, a comprehensive study about procedures and issues for an effective combination of WSN-CC is presented. This work also presents the work proposed by the research community for WSN-CC. Besides, we explored the integration of WSN/IoT with Fog computing (FC). Based on investigations, WSN integration with Fog computing (FC) has many benefits with respect to latency, energy consumption, data processing, and real-time data streaming. FC is not a substitute for distributed computing, so far it is utilized to improve the productivity of the sensor.  
fog computing; cloud computing; WSN to fog; WSN to cloud +
Footnote †: journal: 27 October 2021  
integration of WSNs and clouds can also be used in a large number of applications such as transportation, war zones, health, and agriculture [1; 2]. Disaster surveillance is another region, in which sensor nodes can be used to recognize the tragedy by exact investigated points, to decrease the causality and damage of property. Cloud computing has a slightly positive impact on WSN in the following ways: integration of sensors, ease of storage of data on the cloud, ease of data processing, easy accessibility of tools in different WSN environments, load balancing of a network by the CC, and CC need for WSN to develop other similar computing models due to its low cost. All of the WSN network limitations can be overcome by the CC placement. The WSN with cloud integration is shown in Figure 1. The emergence of WSN and cloud computing services has introduced significant sensor-cloud integration opportunities that will make it easier for users not only to track their objects of concern via sensors but also to employ cloud services to evaluate future directions [3].  
Integration of WSN with the cloud can also be achieved through FC. WSN to fog integration is shown in Figure 2. The figure explains the concept of fog working in the WSN [4]. Different clients can use the Fog as different services for servers to perform their activities. Fog computing provides the smart data processing of WSN network sensors. For example, the goal is to reduce sending direct sensor information toward the cloud thus improving the ratio for both user data and noise [5]. Some basic information processing algorithms are introduced at the sensor stage [6; 7]. Fog is a three-tier structure which is shown in Figure 3. Fog maximizes throughput and minimizes the latency for energy saving [8; 9; 10]. Fog is a very flexible structure for providing services to cloud and sensing nodes [11; 12; 13].  
This integration is based on a two-tier structure, and Figure 3 elaborates this clearly. In this paper, we clearly define the WSNs integration with cloud computing and with Fog computing. The significance of the study is to illustrate the key benefits, issues, and introduced frameworks, techniques in this combination. This study discusses the Fog and cloud requirements as well as the integration of WSN with the Fog and cloud. With, We discuss this in detail next in Section 2.  
Figure 1: WSN to cloud integration.