Energy-Efficient Computation Offloading for Wearable Devices and Smartphones in Mobile Cloud Computing

Ragona, Claudio; Granelli, Fabrizio; Fiandrino, Claudio; Kliazovich, Dzmitry; Bouvry, Pascal

doi:10.1109/glocom.2014.7417039

Cited by 18 publications

(22 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Offloading stands for the capability of moving the load from one device to another [6] and can refer to both traffic offloading [7] or computation offloading [8]. In the second case, the tasks offloaded are executed remotely in the cloud or in the devices in the vicinity [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

Sciarrone

Fiandrino

Bisio

et al. 2016

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

Self Cite

View full text Add to dashboard Cite

Abstract-Indoor navigation and localization are becoming fundamental services nowadays. WiFi-based solutions such as FingerPrinting (FP) are the most widely adopted techniques for positioning and provide better results if compared to other approaches. They require to compare WiFi Received Signal Strength (RSS) with an pre-computed radio map called fingerprint. The recently proposed Smart Probabilistic FingerPrinting (P-FP) algorithm reduces the computational complexity of the traditional FP approach without any accuracy detriment. On the other hand, fog computing has emerged as a new promising paradigm in the recent years, which extends traditional mobile cloud computing capabilities towards the edge of the network and enables location-aware services. In this paper we propose to offload Smart P-FP computation over a fog platform exploiting a novel distributed algorithm. Performance evaluation validates the effectiveness of the proposed approach with the analysis of: i) the amount of power saved and ii) the efficiency of candidate selection process for offloading. Having 2 or more devices in the vicinity contributing to the computation makes offloading beneficial from a power standpoint. The offloading effectiveness increases with the number of devices willing to contribute and the amount of data to be transferred. Power savings can be as high as 80% if compared with local computation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Offloading reduces processing and energy consumption of the mobile devices and is a valid strategy not only for energy saving, but can also for expanding data storage and extending computing capacity. This is especially useful for wearable devices [8]. Recently, fog computing has emerged as a new promising paradigm [10].…”

Section: Introductionmentioning

confidence: 99%

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

Sciarrone

Fiandrino

Bisio

et al. 2016

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mobile Cloud Computing", [6] Researchers are investigating task offloading performed. It has been performed by devices that are wearable in case of mobile based cloud computing.…”

Section: E Claudio R Et Al (2015) "Energy-efficient Computation Ofmentioning

confidence: 99%

Implementing Multi Criteria Analysis of Advanced Mobile Cloud Computing: A Review

Kumar¹

2018

IJRASET

View full text Add to dashboard Cite

“…Also, in [19], only a TCP/IP mobile connection has been considered. Another example is [20], where LTE and WiFi technologies have only been used for the transmission of computing tasks from wearable devices and smartphones in the cloud infrastructure. Indeed, the mobile clients could be surrounded by multiple network connections (Wi-Fi, 4G, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Bangui

Bühnová

et al. 2017

JSAN

View full text Add to dashboard Cite

Abstract:Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers' computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process.

show abstract

Energy-Efficient Computation Offloading for Wearable Devices and Smartphones in Mobile Cloud Computing

Cited by 18 publications

References 16 publications

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

Implementing Multi Criteria Analysis of Advanced Mobile Cloud Computing: A Review

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Contact Info

Product

Resources

About