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.2015.7417039

Cited by 39 publications

(5 citation statements)

References 17 publications

(14 reference statements)

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“…Where x i is the specification of the mobile device like storage, processor speed, and battery power, and 𝜇 mc is the membership function of x i with values in the interval [0,1]. The network configuration (nc) fuzzy set is indicated in Equation (3).…”

Section: Web Service-based Iot Frameworkmentioning

confidence: 99%

“…Ad hoc cloud is another alternative solution that provides cloud computing in an ad hoc environment by combining the computing and storage of the proximate heterogeneous user devices. [2][3][4] However, deciding the appropriate proximate device for task offloading, and establishing the synchronization among user equipment, security, and privacy are the key issues that are to be addressed in this ad hoc cloud environment. Cisco introduced the fog computing model to provide proximate services to the huge number of edge devices on the web.…”

Section: Introductionmentioning

confidence: 99%

“…A larger number of intermediary hops between the mobile device and the remote cloud increases latency, network traffic, bandwidth requirement, and security risks. Ad hoc cloud is another alternative solution that provides cloud computing in an ad hoc environment by combining the computing and storage of the proximate heterogeneous user devices 2–4 . However, deciding the appropriate proximate device for task offloading, and establishing the synchronization among user equipment, security, and privacy are the key issues that are to be addressed in this ad hoc cloud environment.…”

Section: Introductionmentioning

confidence: 99%

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A generic resource augmentation architecture for efficient mobile communication

Anitha

Padma²,

Vallimayil

2023

Concurrency and Computation

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SummaryMobile devices play a vital role in people's day‐to‐day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource‐constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource‐consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource‐intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro‐fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource‐consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service‐based IoT framework, and (c) a neuro‐fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource‐efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augmentation frameworks is analyzed through the simulation results and it illustrates that the neuro‐fuzzy hybrid framework outperforms the other two. The outcome of the proposed research contributions improves the effectiveness of mobile communication by providing seamless services to mobile users regardless of their resource‐limited devices. Besides the devised edge computing architecture supports mobile application developers in the design and deployment of resource‐rich mobile applications in low‐specification mobile devices. Also, there is a strong scope for implementing the proposed generic architecture as an industrial smart product with adequate hardware and software configurations.

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Section: Web Service-based Iot Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A generic resource augmentation architecture for efficient mobile communication

Anitha

Padma²,

Vallimayil

2023

Concurrency and Computation

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“…To address the issues on the preservation of battery power through non-intermittent connectivity, Ragona et al proposed a centralized framework (ECO-CLOUD) using mobile cloud computing through Near Field Communication (NFC) interfaces for a multi-client application [22].A distributed multisite computation offloading using genetic algorithm has been proposed by Zeinab et al for a multi-user environment. The authors in [23] targets to optimize the overall energy consumption and response time by performing efficient offloading.…”

Section: Related Workmentioning

confidence: 99%

Multimedia Computational Offloading for 5G Mobile Edge Computing

D¹,

Raja²

2021

Preprint

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Due to the tremendous growth of smartphone users with their massive usage of faster response, delay sensitive applications lead to high traffic demands, still not yet been met by current researchers. Thus, the ever-increasing computational demands are managed through computational offloading, which offload intensive workloads to the small cells having functionality similar to the resource-rich providers namely edge server. With the use of edge server, the heavily loaded computations get executed outside of the mobile device which results in minimization of mobile energy consumption and latency. Mobile edge computing is an emerging paradigm of commercial infrastructure for computational offloading, which enhances the power of smart mobile devices. Generally, edge provides cloud services and resources to the nearest proximity of users with radio access in fifth-generation (5G) networks for low latency, prompt response, and filtering. But, the frequent network failure automatically degrades the performance. Hence in this paper, we integrate the functionalities of Edge Server with Pico cells (ESP) for efficient traffic management and also propose a distributed Multi hop Mesh Middle layer (MMM) architecture for seamless communication with high resilience, minimal latency, and reduction of energy consumption. Generally, a pico cell is a distributed antenna system, an alternative to a repeater used to extend wireless services to 100 users. We developed an Ant Social based Vector (ASV) optimization algorithm for an intelligent offload decision, which paves the way for backhaul routing conflicts. Simulation results of Cloud Sim show that offloading tiny devices integrated with significant additional computational capabilities would satisfy all the demands from each network within a 15ms delay.

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“…In [23] , computation offloading is empirically studied for a wearable device, Google Glass. In this paper, computation offloading from wearable to smartphone or from wearable to cloud via smartphone is considered.…”

Section: Introductionmentioning

confidence: 99%