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)

A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network

Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved

Improving Sensor Network Performance by Deploying Mobile Sensors

Sensor networks hold the promise of facilitating largescale, real-time data processing in complex environments. In most existing research, sensors are assumed to be static nodes. In this paper, we propose a novel idea: to improve sensor network performance by deploying a small number of mobile sensors in a sensor network where most nodes are static sensors. Specifically, we want to increase the network coverage, provide better routing performance and better connectivity for sensor networks by using mobile sensors. We propose several effective schemes about where to move the mobile sensors, which can provide the most cost-effective approach to improve network performance. These schemes are evaluated by simulation experiments. Our simulations show that a small number of mobile sensors can significantly improve network coverage, increase throughput, reduce delay and energy consumption, and a sensor network with a small portion of mobile sensors performs better than a sensor network with more static sensors. 1