Home > Research > Publications & Outputs > EFFORT

Electronic data

  • SPE-19-0327.R1

    Accepted author manuscript, 858 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


Text available via DOI:

View graph of relations

EFFORT: Energy Efficient Framework for Offload Communication in Mobile Cloud Computing

Research output: Contribution to journalJournal articlepeer-review

  • Saif U.R.Malik
  • Hina Akram
  • Sukhpal Singh Gill
  • Haris Pervaiz
  • hassan malik
<mark>Journal publication date</mark>30/09/2021
<mark>Journal</mark>Software: Practice and Experience
Issue number9
Number of pages14
Pages (from-to)1896-1909
Publication StatusPublished
Early online date31/05/20
<mark>Original language</mark>English


There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this paper is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This paper proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication-intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%.