| Title | Mobility Prediction based Scheduling for Large Scale Mobile Crowdsourcing Data Collection |
|---|---|
| ID_Doc | 44056 |
| Authors | Thejaswini, M; Choi, BJ |
| Title | Mobility Prediction based Scheduling for Large Scale Mobile Crowdsourcing Data Collection |
| Year | 2019 |
| Published | |
| Abstract | Mobile crowdsourcing is one of the promising Internet of Things applications supported by cloud services providing real-time data gathering. Smartphones are one of the most promising platforms for mobile crowdsourcing for its heterogeneous communicating technologies, rich collection of embedded sensors, and relatively high processing and storage power in comparison with wireless sensors. One of the major challenges of mobile crowdsourcing is to guarantee successful transmissions from a large number of smartphones that are affected by dynamic user mobility. Therefore, an efficient scheduling algorithm that incorporates human mobility is needed to successfully collect mobile crowdsourcing data from a large scale of smartphones. In this paper, a mobility prediction-based scheduling framework is proposed to support a large-scale crowdsourcing data collection from smartphones under IEEE 802.11ah which supports the concept of the Internet of Things by providing long-range communication and low energy consumption. The predicted future locations of mobile nodes are used to prioritize mobile nodes and to find mobile nodes that are potentially moving away from the base station during the scheduling process. The performance evaluation under a realistic human mobility model shows that mobility patterns significantly affect the performance of mobile crowdsourcing and the proposed scheduling approach can achieve higher throughput and lower packet loss rate in comparison with other scheduling approaches. |
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