# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 2 Related Work  
Research in this field of fog computing is relatively new but is increasing very quickly [22]. [PERSON] et al. [18] evaluate the capability of the traditional cloud computing integrated with the fog computing to support the increasing demands of the latency-sensitive IoT applications. In their work, a comparative analysis of both paradigms--fog computing and conventional cloud--in the context of IoT was performed. They illustrate the various network devices and networking links within the fog computing paradigm and demonstrate the traffic exchange pattern. Additionally, the fog computing performance metrics in terms of the power consumption, service latency, the corresponding costs incurred, and co2 emission for different renewable and non-renewable energy resources was mathematically characterized. Results of performance evaluation indicate the enhanced performance of fog computing both in terms of the provisioned quality of service (QoS) and eco-friendliness. They justify the fog paradigm as an improved, eco-friendly computing platform that can support IoT better compared to the existing cloud computing paradigm. In 2016, [PERSON] et al. [11] presented an optimal deployment and dimensioning (ODD) study of fog computing to support a vehicular network. Authors in the paper formulate the problem as an integer linear programming (ILP) in order to minimize the cost of deployment. Two different models based on coupling and decoupling fog devices with Roadside Units (RSUs) are developed. [PERSON] et al. [16] present a resource allocation prediction and pricing model. They introduced a service-oriented resource management model for IoT devices, through fog computing in order to provide efficient, effective, and fair management of resources. Their model demonstrates the issues of resource prediction, customer type-based resource estimation and reservation, advance reservation, and pricing for new and existing IoT customers, on the basis of their characteristics. Java and CloudSim toolkit were used for implementation. Results indicate the validity and performance of their system. [PERSON] et al. [17] propose a fog-cloud architecture for information-centric IoT applications with job classification and resource scheduling functions. Their proposal supports the quality of service (QoS) by classifying IoT applications and scheduling computing resources to optimize the dispatch of cloud and fog resources at minimum cost. In their work, one cloud, three fogs, four types of IoT application and 2500 IoT tasks are designed in the cloud-fog simulation environment. Results indicate that the proposed architecture outperforms the computing without scheduling by decreasing the load of the cloud computing by approximately 20.47%. [PERSON] et al. [12] present a distributed hierarchical fog computing paradigm for analyzing big data in smart cities. In this work, intelligence at the edge of a layered fog computing network was distributed as the geo-distribution characteristic in big data generated by massive sensors. Latency-sensitive applications will be performed by the computing nodes of each layer. The computing nodes also support quick control loop to guarantee the safety of infrastructure components. As a use case, the authors implemented a smart pipeline monitoring. They implemented a prototypical four-layer fog-based computing paradigm to illustrate the system's effectiveness and the feasibility. [PERSON] et al. [2] propose a multi-tier fog computing framework for smart city applications. Large scale analytics services can be run over their system with the support of opportunistic and dedicated fog and they call them ad-hoc fog and dedicated fog. They also develop a discrete-event driven system-level simulator to evaluate the performance. Findings demonstrate the efficiency of analytics services over multi-tier fogs and the effectiveness of the proposed schemes. Fogs can largely improve the performance of smart city analytics services compared to the cloud model in terms of job blocking probability and service utility.