Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system
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Date
2025
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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 pack
ets 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.