Accelerating the Delivery of Data Services Over Uncertain Mobile Crowdsensing Networks
Efficient exchange and processing of big data in wireless mobile crowdsensing (MCS) networks require responsive data service provisioning. Traditional onsite spot trading of resources, which relies on real-time network conditions, can facilitate data sharing but often suffers from prohibitive delays and trading failures due to the need for timely analysis of dynamic network environments. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which is a stage-wise data sharing protocol designed for uncertain MCS ecosystems. In iFAST, sellers (i.e., mobile devices) can offer long-term or temporary data services to buyers (i.e., sensing tasks). Specifically, iFAST enables the signing of long-term contracts ahead of future transactions through a forward trading mode, leveraging historical network and market statistics. It also promotes the notion of overbooking. Additionally, it allows buyers with unsatisfactory data quality to recruit temporary sellers through a spot trading mode based on current network/market conditions. We analyze the crucial components of iFAST and provide a case study to demonstrate its performance. We also summarize insights for next-generation wireless sensing and communication.
Authors
- Zhipeng Cheng (ORCID: https://orcid.org/0000-0002-4507-9037)
- Minghui Liwang (ORCID: https://orcid.org/0000-0002-1289-0130)
- Huaiyu Dai (ORCID: https://orcid.org/0000-0002-0078-4891)
- Seyyedali Hosseinalipour (ORCID: https://orcid.org/0000-0003-4266-4000)
- Li Li (ORCID: https://orcid.org/0000-0001-5097-9972)
- Xianbin Wang (ORCID: https://orcid.org/0000-0003-4890-0748)
- Wei Gong (ORCID: https://orcid.org/0000-0003-4030-4968)
- Zhenzhen Jiao (ORCID: https://orcid.org/0009-0006-5940-6445)
- Yuhan Su
Institutions
- Tongji University (CN)
- Western University (CA)
- North Carolina State University (US)
- Xiamen University (CN)
- Soochow University (CN)
- OriginWater (China) (CN)
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- IEEE Wireless Communications
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1109/mwc.005.2400126
- Primary Topic
- Mobile Crowdsensing and Crowdsourcing
- Type
- article
- Field-Weighted Citation Impact
- 0.00