Mobile Cloud Computing Energy-aware Task Offloading (MCC: ETO)

Ahmed, Awais; Abdul, Arif Mohammad; Kaiwartya, Omprakash; Usman, Muhammad; Syed, O; Aanchal,

doi:10.1201/9781315364094-65

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…Mobile edge computing (MEC) is a promising paradigm for addressing resource limitations in various aspects [ 15 ]. To mitigate computation, communication, and storage resource shortages, a variety of approaches have been proposed, including computation offloading and content caching strategies [ 16 , 17 , 18 , 19 ].…”

Section: Related Workmentioning

confidence: 99%

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Sun

Chen

2023

Sensors

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An optimal method for resource allocation based on contract theory is proposed to improve energy utilization. In heterogeneous networks (HetNets), distributed heterogeneous network architectures are designed to balance different computing capacities, and MEC server gains are designed based on the amount of allocated computing tasks. An optimal function based on contract theory is developed to optimize the revenue gain of MEC servers while considering constraints such as service caching, computation offloading, and the number of resources allocated. As the objective function is a complex problem, it is solved utilizing equivalent transformations and variations of the reduced constraints. A greedy algorithm is applied to solve the optimal function. A comparative experiment on resource allocation is conducted, and energy utilization parameters are calculated to compare the effectiveness of the proposed algorithm and the main algorithm. The results show that the proposed incentive mechanism has a significant advantage in improving the utility of the MEC server.

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Section: Related Workmentioning

confidence: 99%

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Sun

Chen

2023

Sensors

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“…Approaches like [22], [23] and [24] focused on offloading computing to extend the battery life of the mobile devices. In [22], the authors proposed FMOCO, a framework which takes into account the offloading requests submitted by users during a specific period of time and proposed the best possible offloading strategy based on each individual user's computing power, bandwidth, as well as available cloud resources.…”

Section: Related Workmentioning

confidence: 99%

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

Draa

Niar

Strugeon

et al. 2019

Journal of Systems Architecture

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Optimizing energy consumption in modern mobile handled devices plays a crucial role as lowering the power consumption impacts battery life and system reliability. Recent mobile platforms have an increasing number of sensors and processing components. Added to the popularity of power-hungry applications, battery life in mobile devices is an important issue. However, the utilization pattern of large amount of data from the various sensors can be beneficial to detect the changing device context, the user needs and the running application requirements in terms of resources. When these information are used properly, an efficient control of power knobs can be implemented to reduce the energy consumption. This paper presents a framework for ENergy Optimization for MObile platform using User needS (ENOsMOUS). This framework is able to identify user contexts and to understand user habits, preferences and needs to improve the operating system power scheme. Machine Learning (ML) algorithms have been used to obtain an efficient trade-off between power consumption reduction opportunities and user satisfaction requirements. ENOrMOUS is a generic solution that manages the power knobs. When applied to the CPU frequency, the sound level, the screen brightness and the Wi-Fi, ENOrMOUS can lower the power consumption by up to 35% compared the out-of-the-box operating system power manager schemes with a negligible overhead.

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“…The network connection, computation and storage capabilities of various devices need to be integrated in such a way that every device has its own video compression standard considering sender and receiver devices capabilities. Further, for large video data, 5G network technology can be employed for automatic synchronization and offloading of the video data to cloud datacenter [153]. This would minimize the need for large memory space and high processor capability for resource storage and computations.…”

Section:  Transmission Range Oriented Traffic Video Deliverymentioning

confidence: 99%

Towards video streaming in IoT Environments: Vehicular communication perspective

Aliyu

Abdullah

Kaiwartya

et al. 2018

Computer Communications

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Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-tovehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, the significance of video streaming in vehicular IoT environments is highlighted focusing on the integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues.

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Mobile Cloud Computing Energy-aware Task Offloading (MCC: ETO)

Cited by 7 publications

References 2 publications

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

ENOrMOUS: ENergy Optimization for MObile plateform using User needS

Towards video streaming in IoT Environments: Vehicular communication perspective

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