# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
## 5 Comparison of Integration WSN with Cloud and Fog
\begin{table}
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**Ref. No** &
\begin{tabular}{c} **Problem** \\ **Addressed** \\ \end{tabular} & **Techniques** & **Strength** & **Weakness** & **Tools** & **Domain** \\ \hline
[1] & Integration get benefits and efficiency & Zigbee is used to route the packets and store the data & Customers can send and receive information to the server & Computation cost is high & Zigbee & WSN in CC \\ \hline
[2] & Minimize the battery power to maximize the lifetime of the node in WSN. & HEF (High Energy First) algorithm. Reduce the number of transmissions. & HEF algorithm. libitime can be bounded and by using a device cloud application. & These disorganized cluster heads could not maximize energy efficiency. & Leach & WSN in CC \\ \hline
[3] & Identification of the threatening and trustworthiness of WSN. & TPSS scheme & TPSS incorporates TSDT & TPSS incorporates TPSDT and PSS to improve sensory data and the relationship of WSN. & Computational and energy cost is high & WSN-MCC \\ \hline
[4] & Data shift from WSN to the cloud scientifically and economically. & Data of WSN moves through a gateway to the DPU. The DPU sends the data to DR according to storage format. & Data Processing Unit (DPU), Pub/Sub provide any mechanism to store and process the data. & Data Processing Unit (DPU), Pub/Sub broker, Request & \\ \hline
[5] & Make sensor data available across the globe and reduce maintenance costs. & Authentication and access control are provided through Identity and Access Management Unit (IAMU). & Data accessed through a secured Identity and Access & No simulation results showed the accuracy of the framework. & Identity and Access Management Unit (IAM�). & WSN in CC \\ \hline
[6] & Minimize the storage requirements for sensor nodes and network gateway. & Data transfer to mobile users in a rapid, reliable, and even more secure manner. & Minimizes the traffic overhead and basewidth requirement and also framework predict the data trend with security. & Deploying more sensors to the area that mobile users are interested in & WSN-MCC \\ \hline
[7] & Bandwidth quality suffers due to the collection of multimedia data. & Several proposed frameworks for integrating WSN-MCC are reviewed. & The optimal usage of WSNs can be managed and their attacks can be checked. & Due to compromising the possibility of WSNs, the data transferred through WSNs are not the data needed by mobile users. & Due to compromising the possibility of WSNs, the data transferred through WSNs are not the data needed by mobile users. & \\ \hline
[8] & Facilitate connecting all components of the network. & Event matching algorithm called Statistical Group Index Matching (SGIM) which targets range predicate case. & Deliver published sensor data or events to appropriate users of cloud applications. & Need the computational CC model than the traditional HPC approaches & Need the computational CC model than the traditional HPC approaches & WSN-MCC \\ \hline
[9] & limited resources of a sensor are the main challenge for deploying and operating WSNs. & Shortened the average end-to-end pattern length of packet transmission & the efficiency of sending operation is improved. & The purposed architecture is needed to be carefully managed. & \\ \hline \hline \end{tabular}
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Table 5: Framework comparison of integration of WSN with cloud and Fog. \begin{table}
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**Ref. No** & **Problem Addressed** & **Techniques** & **Strength** & **Weakness** & **Tools** & **Domain** \\ \hline \multirow{9}{*}{[10]} & Tracking of multiple targets using the sensor-cloud infrastructure. & Social-choice-based Algorithm (S-DMA) & The S-DMA ensures the best possible sensor-cloud sensor-cloud sensor-cloud environment. & If two adjacent sensor nodes are heterogeneous concerning their sensing types as allocation of sensors to targets.