移动群智感知中动态匹配博弈的能耗感知双侧学习
Energy-Aware Two-Sided Learning for Dynamic Matching Games in Mobile Crowdsensing
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中文总结 AI 辅助
针对移动群智感知中能量受限下的动态匹配问题，提出能耗感知双侧学习框架，实现MU任务提议与MCSP任务分配的联合优化，显著提升双方收益与能效。
中文摘要 AI 辅助
移动群智感知（MCS）是下一代网络（NGNs）中感知即服务（SaaS）的有前景的使能技术，其中感知、通信和计算被联合视为按需服务。在MCS中，移动单元（MUs）通过移动群智感知平台（MCSP）收集并传递感知数据给数据请求者（DRs），以换取货币激励。在感知任务公布后，MUs策略性地选择任务以最大化其长期效用，同时考虑能量和时间成本，而MCSP分配任务以最大化其自身的服务收入和数质量。一个根本性的挑战源于缺乏对MUs感知质量和任务努力程度的先验知识，以及电池供电设备的能量限制，这直接影响NGNs中SaaS的服务可用性和可靠性。为解决这些挑战，我们将MUs与MCSP之间的交互建模为不完全信息下的动态双边匹配博弈，并明确纳入能量约束。我们提出了能耗感知双侧学习（ETSL），一个完全分布式且轻量级的学习框架，其中MUs本地学习任务提议策略，而MCSP学习参与MUs的数质量以制定任务分配策略。ETSL联合实现MUs的能耗感知任务提议和MCSP的自适应任务分配，考虑各自的偏好以最大化其净收益。仿真结果表明，ETSL显著提高了MU和MCSP的利润以及整体能效，突显其作为NGNs可扩展且可持续的SaaS解决方案的有效性。
英文摘要
Mobile crowdsensing (MCS) is a promising enabler of Sensing-as-a-Service (SaaS) for next generation networks (NGNs), where sensing, communication, and computing are jointly considered as on-demand services. In MCS, mobile units (MUs) collect and deliver sensing data to data requesters (DRs) via a mobile crowdsensing platform (MCSP) in exchange for monetary incentives. After sensing tasks are announced, MUs strategically select tasks to maximize their long-term utility while accounting for energy and time costs, whereas the MCSP assigns tasks to maximize its own service revenue and data quality. A fundamental challenge arises from the lack of prior knowledge of MUs' sensing qualities and task efforts, as well as the energy limitations of battery-powered devices, which directly impacts service availability and reliability in SaaS for NGNs. To address these challenges, we formulate the interaction between MUs and the MCSP as a dynamic two-sided matching game under incomplete information, explicitly incorporating energy constraints. We propose Energy-aware Two-Sided Learning (ETSL), a fully decentralized and lightweight learning framework in which MUs locally learn task proposal strategies, while the MCSP learns the data quality of participating MUs to devise task assignment strategy. ETSL jointly enables MUs' energy-aware task proposals and MCSP's adaptive task assignment, considering their individual preferences to maximize their net revenues. Simulation results demonstrate that ETSL significantly improves MU and MCSP profits and overall energy efficiency, highlighting its effectiveness as a scalable and sustainable SaaS solution for NGN.
发表机构
- Technical University of Darmstadt（达姆施塔特工业大学）
- Vienna University of Technology（维也纳工业大学）
机构由 AI 辅助整理，请以论文原文为准。
