Abstract
Wi-Fi uploading is considered an effective method for offloading the traffic of cellular networks generated by the data uploading process of mobile crowd sensing applications. However, previously proposed Wi-Fi uploading schemes mainly focus on optimizing one performance objective: the offloaded cellular traffic or the reduced uploading cost. In this paper, we propose an Intelligent Data Uploading Selection Mechanism (IDUSM) to realize a trade-off between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The mechanism first helps the source participant choose an appropriate data uploading manner based on the proposed probability prediction model, and then optimizes its performance objective for the chosen data uploading manner. In IDUSM, our proposed probability prediction model precisely predicts a participant’s mobility from spatial and temporal aspects, and we decrease data redundancy produced in the Wi-Fi offloading process to reduce waste of participants’ limited resources (e.g., storage, battery). Simulation results show that the offloading efficiency of our proposed IDUSM is , and the value is the highest among the other three Wi-Fi offloading mechanisms. Meanwhile, the offloading ratio and uploading cost of IDUSM are respectively 52.1% and . Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.
Highlights
With the proliferation of smart devices with various sensors, Mobile Crowd Sensing (MCS) has become an appealing paradigm by empowering smart devices as participants to contribute data sensed or generated from them, and aggregating and fusing these data in the cloud platform for crowd intelligence extraction and human-centric service delivery [1]
According to Cisco forecast, cellular networks are very likely to become overloaded due to the dramatically increased traffic [6], even though it upgrades to the 5th Generation (5G) mobile networks with higher capacity. 5G networks are expected to handle a 1000-fold increase in capacity [7,8]
We propose the distributed Intelligent Data Uploading Selection Mechanism (IDUSM) to select the appropriate data uploading manner based on the proposed probability prediction model realizing a trade-off between the offloaded traffic and the uploading cost, and reducing unnecessary data copies in opportunistic offloading process to reduce the waste of participants’ limited resources
Summary
With the proliferation of smart devices with various sensors (e.g., smart phones, iPads, intelligent vehicles etc.), Mobile Crowd Sensing (MCS) has become an appealing paradigm by empowering smart devices as participants to contribute data sensed or generated from them, and aggregating and fusing these data in the cloud platform for crowd intelligence extraction and human-centric service delivery [1]. In this paper we study how to design an appropriate data uploading mechanism to make a balance between the offloaded traffic of cellular networks and participants’ uploading cost considering the differences among participants’ data plans and direct and indirect opportunistic transmissions. The proposed IDUSM realized a trade-off between the offloaded cellular networks traffic and participants’ uploading cost considering the differences among participants’ data plans and probabilities of opportunistic transmissions. The model precisely predicts a participant’s probability of successfully transferring data to a Wi-Fi AP considering direct and indirect opportunistic transmission and time variation of opportunistic contact patterns It increases the accuracy of data uploading decision.
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