Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916196693049344 |
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| author | Liwang, Minghui Cheng, Zhipeng Gong, Wei Li, Li Su, Yuhan Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Dai, Huaiyu |
| author_facet | Liwang, Minghui Cheng, Zhipeng Gong, Wei Li, Li Su, Yuhan Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Dai, Huaiyu |
| contents | The challenge of exchanging and processing of big data over mobile crowdsensing (MCS) networks calls for designing seamless data service provisioning mechanisms to enable utilization of resources of mobile devices/users for crowdsensing tasks. Although conventional onsite spot trading of resources based on real-time network conditions can facilitate data sharing, it often suffers from prohibitively long service provisioning delays and unavoidable trading failures due to requiring timely analysis of dynamic network environment. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which entails a novel hybrid data trading protocol with time efficiency, over uncertain MCS ecosystems. In iFAST, the sellers (i.e., mobile devices who can contribute data) can provide long-term or temporary sensing services to the buyers (i.e., sensing tasks). Specifically, it enables signing long-term contracts in advance of future transactions through a forward trading mode, via analyzing historical statistics of the network/market, for which the notion of overbooking is introduced and promoted. iFAST further encourages the buyers with unsatisfying service quality to recruit temporary sellers through a spot trading mode, considering the current network/market conditions. We analyze the fundamental blocks of iFAST and provide a case study to demonstrate its performance. Inspirations for future research directions of next-generation sensing and communication are summarized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_04410 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks Liwang, Minghui Cheng, Zhipeng Gong, Wei Li, Li Su, Yuhan Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin Dai, Huaiyu Distributed, Parallel, and Cluster Computing The challenge of exchanging and processing of big data over mobile crowdsensing (MCS) networks calls for designing seamless data service provisioning mechanisms to enable utilization of resources of mobile devices/users for crowdsensing tasks. Although conventional onsite spot trading of resources based on real-time network conditions can facilitate data sharing, it often suffers from prohibitively long service provisioning delays and unavoidable trading failures due to requiring timely analysis of dynamic network environment. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which entails a novel hybrid data trading protocol with time efficiency, over uncertain MCS ecosystems. In iFAST, the sellers (i.e., mobile devices who can contribute data) can provide long-term or temporary sensing services to the buyers (i.e., sensing tasks). Specifically, it enables signing long-term contracts in advance of future transactions through a forward trading mode, via analyzing historical statistics of the network/market, for which the notion of overbooking is introduced and promoted. iFAST further encourages the buyers with unsatisfying service quality to recruit temporary sellers through a spot trading mode, considering the current network/market conditions. We analyze the fundamental blocks of iFAST and provide a case study to demonstrate its performance. Inspirations for future research directions of next-generation sensing and communication are summarized. |
| title | Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2210.04410 |