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Adaptive Flow Control Using Movement Information in Mobile-Assisted Sensor Data Collection

Internet of Things (IoT) is gaining great momentum for remote data collection in various smart applications. Recently, the proliferation of wireless sensors and the explosive increase in the number of mobile devices enable Mobile-Assisted Sensing (MAS) in which the mobile gateways collect data from the distributed sensors. Therefore, MAS collects data without additional overheads for building a static network infrastructure. Meanwhile, the connection between a sensor and a mobile gateway is generally considered unreliable due to the arbitrary mobility of mobile gateways. That is, the connection is unexpectedly terminated when the mobile gateway leaves the communication range of the sensor. Therefore, the existing studies have used frequent control messages for suppressing invalid data transmission after the end of the connection. Thus, the conservative flow control degrades data throughput because the control messages occupy a large part of the connection. However, the connection can be reliable according to the condition of the mobile gateway. In this paper, an adaptive flow control (AFC) scheme is proposed for enhancing data throughput using the movement information of mobile gateways. AFC exploits the movement information for estimating the reliability of the connection. Estimating that the connection is reliable, the control messages are hardly emitted during data transmission thereby the connection contains more data. Consequently, AFC enhances the data throughput by reducing control overheads without degrading reliability.