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.

Smart object interconnected between each other and producing a large volume of information in Internet of Mobile Things (IoMT) is a huge challenge. The things that we expect to connect to the Internet will consist of sensors, actuators with information processing and communication capabilities that will make themselves intelligent. Sensors are collecting continuously environmental data (e.g. body area network, smart city, cars, smartphones, etc.). We used Arduino, XBee, Raspberry Pi 3 and open source software for framework design. This architecture process real-time data and maximize the delivery of raw data to the cloud for post processing. It also ensure efficient event-driven data collection given the buffer space and communication bandwidth limitations of each sensor. An intelligent IoMT gateway and middleware used to support different message format of sensor data and efficient decision-making system. We also propose an Android data collection system where data collectors are smartphones. This paper defines a data collection architecture supporting the physical dimension of sensors to the storage of data in a cloud-based service. This class of architecture must tackle several challenges, e.g., buffer management, sensor message ordering, data alignment for multiple temporal data streams, data storage, sensor heterogeneity, high throughput and avoiding processing bottlenecks. For the issues of an extended advancement cycle, poor transportability and high-chance in the improvement procedure of ZigBee remote system, presents the Unified Modeling Language (UML) to institutionalize the advancement procedure, enhancing improvement productivity and guaranteeing item quality.