Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Fog computing is one of the efficient technologies used for overcoming the issues related to handling large quantities of Heterogeneous Internet of Things (HIoT) data like distributed network and health monitoring by dispensing the applications which gets applied nearer to the network edge. In fog based network model, distributed fog server closeness to the terminal devices allows currently available data to be transmitted effectually. But the increasing size of fog enabled HIoT environments leads to the design of effective routing and path selection to accomplish minimum delay and power consumption. With this motivation, this paper presents Optimization based Data Aggregation and fault Tolerance with Energy Management (ODFTEM) technique for fog enabled HIoT. This idea is segmented into three categories; they are a quasi-oppositional chimp optimization based data aggregation for routing, fault tolerance amongst the fog nodes and a Markov-chain-based probabilistic model for energy management. Initially, data aggregation is employed to gather the data and accumulate it for removing repetitive data and conserving energy. The goal of data aggregation is to reduce quantity of data dissemination and extend lifetime. A quasi-oppositional chimp optimization based data aggregation in routing is performed to choose an optimal collection of routes and perform data aggregation and it follows two stages like relay node selection and data aggregation. Secondly, to boost the network performance the best cost path is chosen where it selects the best nearest neighbor so that the packet transmission time can get reduced. Finally to forecast the energy consumption a Markov chain based probabilistic approach is used and this method can provide a comfortable energy saving model to the fog network. A detailed experimental validation of the ODFTEM technique reported the better performance in terms of energy consumption, throughput and transmission delay of the devices in the Fog enabled HIoT environment which is compared with the baseline methods like IHCFC, DAIFC and ODIFC.