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

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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.

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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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Correspondence to Fan Li.

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This article belongs to the Topical Collection: Special Issue on Mobile Crowdsourcing

Guest Editors: Bin Guo, Xing Xie, Raghu K. Ganti, Daqing Zhang, and Zhu Wang

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Li, F., Li, Z., Sharif, K. et al. Multi-layer-based opportunistic data collection in mobile crowdsourcing networks. World Wide Web 21, 783–802 (2018). https://doi.org/10.1007/s11280-017-0482-9

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