Energy aware learning improves mobile crowdsensing task matching
Energy-Aware Two-Sided Learning for Dynamic Matching Games in Mobile Crowdsensing
Networking and Internet Architecture
Summary
Mobile devices can collect data for services by completing sensing tasks, but they have limited battery power and don’t always know how well they perform. The authors study how devices and a coordinating platform can learn over time to choose and assign tasks smarter while considering energy costs. They propose a system where devices decide locally which tasks to offer to do, and the platform adapts its assignments based on data quality. Simulations show this approach helps both devices and the platform earn more and use energy more efficiently.
What this means in practice
- •For mobile app developers: Create apps that better allocate crowdsensing tasks by learning user device energy and data quality to improve service reliability.
- •For network operators: Optimize scheduling of sensing tasks over mobile networks considering device energy limits to sustain sensing services longer.
Authors
Sumedh J. Dongare, Anja Klein, Andrea Ortiz
Abstract
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.