Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

Nawrocki, Piotr; Śnieżyński, Bartłomiej; Kołodziej, Joanna; Szynkiewicz, Paweł

doi:10.1109/ccgrid49817.2020.00-19

Cited by 13 publications

(7 citation statements)

References 34 publications

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“…In order to make offloading decisions that maximize benefits, it is essential to consider contextual information. Studies have been conducted to make service selection or offloading decision by considering different contextual factors, such as user location [11], wireless medium [14], application type [15], and network bandwidth [16], etc.. These schemes adopted such as supervised learning [11] or a multi-criteria decision-making [14] to improve cloud computing service performance or optimize the energy consumption of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

TConNS: A Novel Time-varying Context-aware Offloading Strategy for Mobile Edge Computing

Zheng,

Li,

et al. 2023

Preprint

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Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set (TConNS). Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single valued neutrosophic set (SVNS) with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49\% and the average energy consumption by about 46\%, and also reduces the number of task failures.

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Section: Related Workmentioning

confidence: 99%

TConNS: A Novel Time-varying Context-aware Offloading Strategy for Mobile Edge Computing

Zheng,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Adaptiveness in applications for optimization of energy consumption is addressed in the literature. Some of these applications use machine learning techniques to improve adaptive task scheduling [27] and perform resource allocation [28].…”

Section: B Adaptation and Context Awareness In Iot/ml Applicationsmentioning

confidence: 99%

Context Aware Adaptive ML Inference in Mobile-Cloud Applications

Dolui

Michiels

Hughes

et al. 2022

2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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With the emergence of mobile devices having enough resources to execute real-time ML inference, deployment opportunities arise on mobile devices while keeping privacy-sensitive data close to the source and reducing server load. Moreover, offloading inference to a cloud server facilitates deployment of neural network-based applications on resource-constrained devices. Depending on the application goals and execution context of the application, the optimal deployment on either cloud server or mobile device varies during the lifetime of an application. In this paper, we propose a context-aware middleware that enables optimization of deployed application software to satisfy the application's functional goals in accordance with changing execution context and environmental conditions. We facilitate system design through the abstraction of deployed software components as states and make use of finite state machines and contextual triggers to model the reconfiguration of the system. We evaluate our framework using a real-world nutritional monitoring application via food image recognition deployed in a two-tier mobile and cloud architecture. We compare the proposed solution with various static deployments of the application and show that our approach can react to changing application goals at run-time in order to reduce server load and thereby increase scalability.

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“…Decisions are made taking the context into account, such as network connection type, the health status of a patient, potential time, and cost of executing the application or service. Adaptive systems using mobile devices allow the system to be more aware of the context through ubiquitous computing techniques (Nawrocki et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A computational model for adaptive recording of vital signs through context histories

Aranda

Bavaresco

Carvalho

et al. 2021

J Ambient Intell Human Comput

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Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

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Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects

Cited by 13 publications

References 34 publications

TConNS: A Novel Time-varying Context-aware Offloading Strategy for Mobile Edge Computing

TConNS: A Novel Time-varying Context-aware Offloading Strategy for Mobile Edge Computing

Context Aware Adaptive ML Inference in Mobile-Cloud Applications

A computational model for adaptive recording of vital signs through context histories

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