With the popularity of mobile devices and the emergence of various audio-editing tools, it becomes easier to produce and forge audio files. Many criminals will forge false audio information as evidence. Therefore, audio forensics technology becomes particularly important. Audio recording device identification technology, which can verify the authenticity and uniqueness of the evidence obtained, is one of the promising branches of audio forensics technology. In this paper, a novel neural-network-based framework using the device noise feature is proposed to identify the sources of the recording according to the device traces generated by the device during the recording. We also propose a new neural network model RARS (Recognition of Audio Recording Sources based on residual neural network). The proposed framework achieves state-of-the-art performance on the only publicly available dataset MOBIPHONE in this field. Moreover, we build a new dataset based on the latest mobile phones and tablet devices. Our method achieves good performance on both the two different datasets, which proves that our model has a certain degree of reusability and robustness.

In low-duty-cycle wireless networks with unreliable and correlated links, Opportunistic Routing (OR) is extremely costly because of the unaligned working schedules of nodes within a common candidate forwarder set. In this work, we propose a novel polynomial-time node scheduling scheme considering link correlation for OR in low-duty-cycle wireless networks (LDC-COR), which significantly improves the performance by assigning nodes with low correlation to a common group and scheduling the nodes within this group to wake up simultaneously for forwarding packets in a common cycle. By taking account of both link correlation and link quality, the performance of the expected transmission count (ETX) is improved by adopting the LDC-COR protocol. As a result, the energy consumption of low-duty-cycle OR is significantly reduced. LDC-COR only requires the information of one-hop neighboring nodes which introduces minimal communication overhead. The proposed LDC-COR bridges the gap between the nodes’ limited energy resource and the application lifetime requirements. We evaluate the performance of LDC-COR with extensive simulations and a physical wireless testbed consisting of 20 TelosB nodes. The evaluation results show that both transmission efficiency and energy consumption of low-duty-cycle OR are significantly improved with only a slight increase of end-to-end delay.

Many data center applications have deadline requirements, which pose a requirement of deadline-awareness in network transport. Completing within deadlines is a necessary requirement for flows to be completed. Transport protocols in current data centers try to share the network resources fairly and are deadline-agnostic. Recently several works try to address the problem by making as many flows meet deadlines as possible. However, for many data center applications, a task cannot be completed until the last flow finishes, which indicates the bandwidths consumed by completed flows are wasted if some flows in the task cannot meet deadlines. In this paper we design a task-level deadline-aware preemptive flow scheduling(TAPS), which aims to make more tasks meet deadlines. We leverage software defined networking (SDN) technology and generalize SDN from flow-level awareness to task-level awareness. The scheduling algorithm runs on the SDN controller, which decides whether a flow should be accepted or discarded, pre-allocates the transmission time slices and computes the routing paths for accepted flows. Extensive flow-level simulations demonstrate TAPS outperforms Varys, Bara at, PDQ (Preemptive Distributed Quick flow scheduling), D3 (Deadline-Driven Delivery control protocol) and Fair Sharing transport protocols in deadline sensitive data center environment. A simple implementation on real systems also proves that TAPS makes high effective utilization of the network bandwidth in data centers.