Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system

Abstract

The rising craze of sensor enabled mobile devices promotes its usage in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) and other distributed computing environments derived from the cloud computing environment. As the computing paradigm shifts from centralized to distributed computing and mobile devices are getting smarter and resource rich, it facilitate the user to do computation to its proximity. Hence, it is quite useful to incorporate the Wireless Sensor Networks (WSN) with distributed computing environment to better cater to the user needs. The proposed work enhances the MCC and MEC by incorporating sensor enabled computing along with the application of energy optimization techniques such as coyote optimization, Fuzzy Logic (FL), data redundancy and data compression. A new framework called Sensor Enabled-Scalable Key Parameter Yield of Resources (SE-SKYR) framework is proposed in this research work by integrating SKYR framework with cluster-based sensing mechanism. The proposed work uses SKYR framework which is a cloudlet based MCC framework and works well for MEC as well. Cloudlet is used as the main computing component available at the local level which suits both MEC and MCC. The existing system uses the concept of relay node to transmit data packets in transmission path from sensor nodes to server via edge cloud and hence causes delay in transmission of data. In the proposed work, we have introduced a Scalable Energy Optimization of Resource (SEOR) algorithm to optimize the energy consumption by various resources. SE-SKYR framework along with SEOR algorithm addresses the problems faced by the existing system. The complexity of the proposed SEOR algorithm is less as compared to its existing counterparts and is also comprehended from the results.