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, Volume 21, Issue 3, pp 783–802 | Cite as

Multi-layer-based opportunistic data collection in mobile crowdsourcing networks

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Part of the following topical collections:
  1. Special Issue on Mobile Crowdsourcing

Abstract

Along with the explosive popularity of wireless mobile devices and availability of high data rates, new crowdsourcing paradigms have emerged to leverage the power of problem-solving by crowds. A crucial challenge in crowdsourcing is data collection. With the increasing number of mobile users, device to device communication with opportunistic connections has become a real possibility, reducing the load on infrastructure based networks. Crowdsourcing over such opportunistic links presents with unique challenges. This paper proposes to exploit opportunistic transmission to collect data in crowdsourced networks, by using multiple layers of social graphs along with weight training for energy efficient data collection. We design two types of multi-layer-based opportunistic data collection methods by using different dimensions of data. Simulation experiments show that using these techniques, delivery ratio can be increased while reducing the load and energy consumption of the mobile network.

Keywords

Crowdsourcing Data collection Multi-layer Opportunistic networks 

Notes

Acknowledgments

The work of Fan Li is partially supported by the National Natural Science Foundation of China under Grant No. 61772077, 61370192 and 61432015. The work of Yang Liu is partially supported by China Postdoctoral Science Foundation 2015M580051, 2016T90039, and the National Natural Science Foundation of China under Grant No. 61602038. The work of YuWang is partially supported by the US National Science Foundation under Grant No. CNS-1319915 and CNS-1343355, and the National Natural Science Foundation of China under Grant No. 61428203 and 61572347.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer ScienceBeijing Institute of TechnologyBeijingChina
  2. 2.School of AutomationBeijing Institute of TechnologyBeijingChina
  3. 3.Department of Computer ScienceUniversity of North Carolina at CharlotteCharlotteUSA

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