An intelligent data uploading selection mechanism for offloading uplink traffic of cellular networks

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 (56.54 × 10−7), 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 (6.79 × 103). Compared with other three Wi-Fi offloading mechanisms, it realized a trade-off between the offloading ratio and the uploading cost.This research was funded by National Key Research and Development Project of China (2019YFB2102303), National Natural Science Foundation of China (61971014, 11675199, 61202076) and Beijing Natural Science Foundation (4192007)

Exploiting Mobile Social Networks from Temporal Perspective:A Survey

With the popularity of smart mobile devices, information exchange between users has become more and more frequent, and Mobile Social Networks (MSNs) have attracted significant attention in many research areas. Nowadays, discovering social relationships among people, as well as detecting the evolution of community have become hotly discussed topics in MSNs. One of the major features of MSNs is that the network topology changes over time. Therefore, it is not accurate to depict the social relationships of people based on a static network. In this paper, we present a survey of this emerging field from a temporal perspective. The state-of-the-art research of MSNs is reviewed with focus on four aspects: social property, time-varying graph, temporal social property, and temporal social properties-based applications. Some important open issues with respect to MSNs are discussed