The rapid development of Internet of Things (IoT) devices in various smart city-based applications such as healthcare, traffic management systems, environment sensing systems, and public safety systems produce large volumes of data. To process these data, it requires substantial computing and storage resources for smooth implementation and execution. While centralized cloud computing offers scalability, flexibility, and resource sharing, it faces significant limitations in IoT-based applications, especially in terms of latency, bandwidth, security, and cost. The fog computing paradigm complements the existing cloud computing services at the edge of the network to facilitate the various services without sending the data to a centralized cloud server. By processing the data in fog computing, it satisfies the delay requirement of various time-sensitive services of IoT applications. However, many resource-intensive IoT systems exist that require substantial computing resources for their processing. In such scenarios, finding the optimal computing node for processing and executing the service is a challenge. The optimal placement of various IoT applications services in heterogeneous fog computing environments is a well-known NP-complete problem. To solve this problem, various authors proposed different algorithms like the randomized algorithm, heuristic algorithm, meta heuristic algorithm, machine learning algorithm, and graph-based algorithm for finding the optimal placement. In the present survey, we first describe the fundamental and mathematical aspects of the three-layer IoT–fog–cloud computing model. Then, we classify the IoT application model based on different attributes that help to find the optimal computing node. Furthermore, we discuss the complexity analysis of the service placement problem in detail. Finally, we provide a comprehensive evaluation of both single-objective and multi-objective IoT service placement strategies in fog computing. Additionally, we highlight new challenges and identify promising directions for future research, specifically in the context of multi-objective IoT service optimization.

Due to the advent of Internet of Things(IoT) plethora of services has been emerged and to perpetuate all these services using cloud computing paradigm is really tiresome. A new promising service provider called as Fog computing came into the picture where the distance between Iot and edge device is small to provide the services efficiently to the end users. There are different considerable factors like service response time, and expected QoS must met without violating other resource constraints. In this paper we try to layout an architecture which is based on the combination of cloud and fog by introducing a middleware called as cloud fog control middleware, which manages the service request according to some constraints. By using this architecture we can maximize the advantages of next generation computer system, however the architecture require new strategies to manage the mapping of services to resources. Despite several Heuristic and Meta Heuristic techniques has been proposed by different authors to solve the service placement in fog computing with considering different parameters like quality of service(QoS), Latency, etc. In this paper we are trying to minimize the energy consumption in fog computing paradigm by formulating the service placement plan in order to utilize the resources efficiently by considering the Active and Idle state of machine. First we calculate the energy consumption by the application(number of tasks) requesting for a particular service. Undoubtedly the service placement problem is a combinatorial optimization problem. The optimal solution obtained distribute the load to some other fog node by appropriate placement of service which leads to reduce the over heat generated by a particular fog node. In this way we can achieve energy minimization in fog computing.