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An Optimal Novel Approach for Dynamic Energy-Efficient Task Offloading in Mobile Edge-Cloud Computing Networks

Authors:
Avijit Mondal at KIIT University
Avijit Mondal
Pinaki Sankar Chatterjee at KIIT University
Pinaki Sankar Chatterjee
Niranjan K. Ray
Niranjan K. Ray
Niranjan K. Ray
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Abstract and Figures

The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to resource limitations and security concerns inherent to these devices. In response, this paper introduces a Dynamic Energy-Efficient Offloading Algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption, and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and comparative analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user experience.
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Vol.:(0123456789)
SN Computer Science (2024) 5:655
https://doi.org/10.1007/s42979-024-02992-1
SN Computer Science
ORIGINAL RESEARCH
An Optimal Novel Approach forDynamic Energy‑Efficient Task
Offloading inMobile Edge‑Cloud Computing Networks
AvijitMondal1 · PinakiSankarChatterjee1 · NiranjanK.Ray1
Received: 10 September 2023 / Accepted: 23 May 2024
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024
Abstract
The rapid evolution of mobile devices has greatly advanced secure medical image transmission, yet challenges persist due to
resource limitations and security concerns inherent to these devices. In response, this paper introduces a Dynamic Energy-
Efficient Offloading Algorithm (DEEO), seamlessly integrated into the Mobile Edge-Cloud Computing (MECC) environ-
ment. DEEO empowers mobile devices to efficiently offload computationally intensive secure image transmission tasks to
the nearest edge server or fog access point. This integration optimizes resource utilization, minimizes energy consumption,
and ensures the confidentiality and integrity of sensitive medical image data. Through rigorous evaluations and compara-
tive analysis, our approach demonstrates clear superiority over existing solutions. This integrated framework is poised to
significantly enhance healthcare applications, offering heightened efficiency, elevated security, and an overall improved user
experience.
Keywords Mobile edge computing· Mobile cloud computing· Task offloading· Mobile energy consumption· Dynamic
decision-making· Energy efficient offloading· Workload analysis· Dynamic optimization algorithm· Genetic algorithm·
PSO· Hill climbing· Scalability analysis· Statistical analysis
Introduction
Edge computing offers a distributed approach to computing
that complements traditional cloud computing. It doesn’t
replace cloud computing, but rather works alongside it to
offload processing to the edge when immediate analysis and
response are required [1]. Cloud Computing offers numer-
ous advantages, but it also presents significant limitations,
some of which have become more pronounced with recent
developments in communication technologies and applica-
tion demands. While these limitations have existed since
the inception of cloud computing, they have gained more
attention recently.
Background andContext
The rise of new communication technologies, applications,
and services has led to a surge in data volume and increased
demands for low latency communication. This has revealed
challenges in providing cloud resources from centralized
locations distant from users. Such an arrangement can result
in considerable delays that are particularly problematic
for critical applications like healthcare control. Transmit-
ting data from end devices to the cloud introduces several
challenges. Firstly, the substantial data volume can create
congestion and hotspots within the internet infrastructure,
contributing to communication delays. Secondly, navigat-
ing different networks and administrative domains between
the cloud and front-end devices can result in inconsistent
and unstable network connections. Additionally, data often
needs to traverse backhaul networks like cellular or satellite
links before reaching the cloud, leading to cost and data
loss issues. While this discussion focuses primarily on net-
working and data processing limitations, it’s worth noting
that security concerns also arise. The increased surface area
for data transmission from end devices to the cloud opens
up possibilities for security threats during transit. These
* Avijit Mondal
avijit.ism@gmail.com
Pinaki Sankar Chatterjee
pinaki.sankar.chatterjee@gmail.com
Niranjan K. Ray
rayniranjan@gmail.com
1 School ofComputer Engineering, KIIT University,
Bhubaneswar, Odisha, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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