Omar Naserallah;Sherif B. Azmy;Nizar Zorba;Hossam S. Hassanein
{"title":"Novel Distribution-Aware and Learning-Based Dynamic Scheme for Efficient User Incentivization in Edge Sensing Systems","authors":"Omar Naserallah;Sherif B. Azmy;Nizar Zorba;Hossam S. Hassanein","doi":"10.1109/OJCOMS.2024.3449691","DOIUrl":null,"url":null,"abstract":"Edge sensing (ES) systems employ users’ owned smart devices with built-in sensors to gather data from users’ surrounding environments and use their processors to carry out edge computing tasks. Therefore, ES is emerging as a potential solution for remote sensing challenges. Additionally, ES systems are recognized for their favorable characteristics, including efficient time and cost management, scalability, and the ability to gather real-time data. To improve the performance of ES systems, enormous efforts have been made to enhance the quality of data (QoD) and the systems’ spatiotemporal coverage. Moreover, the research community has focused on developing better incentive schemes, as user incentivization is essential for enhancing system performance. In this study, we assess the impact of users’ mobility and availability on the spatiotemporal coverage and QoD of ES systems, taking into account 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 learning-based incentive scheme is further 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 incentivization budget.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648608","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10648608/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Edge sensing (ES) systems employ users’ owned smart devices with built-in sensors to gather data from users’ surrounding environments and use their processors to carry out edge computing tasks. Therefore, ES is emerging as a potential solution for remote sensing challenges. Additionally, ES systems are recognized for their favorable characteristics, including efficient time and cost management, scalability, and the ability to gather real-time data. To improve the performance of ES systems, enormous efforts have been made to enhance the quality of data (QoD) and the systems’ spatiotemporal coverage. Moreover, the research community has focused on developing better incentive schemes, as user incentivization is essential for enhancing system performance. In this study, we assess the impact of users’ mobility and availability on the spatiotemporal coverage and QoD of ES systems, taking into account 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 learning-based incentive scheme is further 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 incentivization budget.
边缘传感(ES)系统利用用户拥有的内置传感器的智能设备从用户周围环境中收集数据,并利用其处理器执行边缘计算任务。因此,ES 正在成为应对遥感挑战的潜在解决方案。此外,ES 系统还因其高效的时间和成本管理、可扩展性以及收集实时数据的能力等有利特性而备受认可。为了提高 ES 系统的性能,人们在提高数据质量(QoD)和系统的时空覆盖范围方面做出了巨大努力。此外,研究界还致力于开发更好的激励方案,因为用户激励对于提高系统性能至关重要。在本研究中,我们评估了用户的移动性和可用性对 ES 系统时空覆盖范围和 QoD 的影响,同时考虑到了用户的异质性。我们提出了一种基于分布感知和学习的动态激励方案。具体来说,我们使用二维随机航点(RWP)模型考虑了用户移动性和速度的随机性,并使用长短期记忆(LSTM)模型支持基于学习的激励方案。LSTM 模型利用用户的历史数据来预测他们是否可以执行传感任务。基于学习的激励方案进一步用于提高系统性能和有效管理质量与成本之间的权衡,根据所需的质量和成本约束条件招募用户,在受限的激励预算内满足最低质量要求。
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.