Efficient User Incentives in Extreme Edge Sensing: A New Learning-Based Scheme
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Authors
Naserallah, Omar
Date
2024-05-14
Type
thesis
Language
eng
Keyword
Extreme Edge sensing , Quality of data , Mobility , Incentive , Prediction
Alternative Title
Abstract
Extreme Edge Sensing (EES) systems utilize the built-in sensors of users' smart devices to collect data from the surrounding environment and employ their processors to carry out edge computing tasks. Emerging as a solution to remote sensing challenges, EES systems are noted for their efficient time and cost management, scalability, and the ability to gather real-time data. Efforts to enhance these systems have focused on improving quality of data (QoD) and coverage, as well as on developing incentive schemes to optimize performance. In this thesis, we assess the impact of users' mobility and availability on the spatiotemporal coverage and QoD of EES systems, considering the heterogeneity of users. We propose a distribution-aware and learning-based dynamic incentive scheme. Specifically, we consider the randomness of users' mobility and velocity using a 2-dimensional random waypoint (RWP) model and support the learning-based incentive scheme with a long short-term memory (LSTM) model. The LSTM model utilizes the users' historical data to predict their availability to perform the sensing tasks. The proposed scheme is used to enhance system performance and effectively manage the trade-off between quality and cost, by recruiting users based on the required quality and cost constraints to meet the minimum quality requirement within a constrained budget. Our findings demonstrate that the proposed scheme enhances the spatiotemporal coverage and QoD in EES systems.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution-NonCommercial-NoDerivatives 4.0 International