The 21st International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc 2020)
Sl-EDGE: Network Slicing at the Edge
Salvatore D’Oro (Institute for the Wireless Internet of Things, Northeastern University), Leonardo Bonati (Institute for the Wireless Internet of Things, Northeastern University), Francesco Restuccia (Institute for the Wireless Internet of Things, Northeastern University), Michele Polese (University of Padova, Italy), Michele Zorzi (University of Padova, Italy), Tommaso Melodia (Institute for the Wireless Internet of Things, Northeastern University)
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Ang Li (Duke University), Chunpeng Wu (Duke University), Yiran Chen (Duke University), Bin Ni (Quantil Inc.)
Fair Multi-resource Allocation in Mobile Edge Computing with Multiple Access Points
Erfan Meskar, Ben Liang (University of Toronto)
Robust Resource Provisioning in Time-Varying Edge Networks
Ruozhou Yu (North Carolina State University), Guoliang Xue, Yinxin Wan (Arizona State University), Jian Tang (Syracuse University), Dejun Yang (Colorado School of Mines), Yusheng Ji (National Institute of Informatics, Japan (NII))
Bin Li (University of Rhode Island)
Real-Time Wireless Networking
Fresher Content or Smoother Playback? A Brownian-Approximation Framework for Scheduling Real-Time Wireless Video Streams
Ping-Chun Hsieh (National Chiao Tung University), Xi Liu (Texas A&M University), I-Hong Hou (Texas A&M University)
In real-time video streaming, one major challenge is to tackle the natural tension between the two most critical QoE metrics: playback latency and video interruption.
To study this trade-off, we first propose an analytical model that precisely captures all aspects of the playback process of a real-time video stream, including playback latency, video interruptions, and packet dropping.
Built on this model, we show that the playback process of a real-time video can be approximated by a two-sided reflected Brownian motion.
Through such Brownian approximation, we are able to study the fundamental limits of the two QoE metrics and characterize a necessary and sufficient condition for a set of QoE performance requirements to be feasible.
We propose a scheduling policy that satisfies any feasible set of QoE performance requirements and then obtain simple rules on the trade-off between playback latency and the video interrupt rates, in both heavy-traffic and under-loaded regimes.
Finally, simulation results verify the accuracy of the proposed approximation and show that the proposed policy outperforms other popular baseline policies.
Optimizing Information Freshness using Low-Power Status Updates via Sleep-Wake Scheduling
Ahmed M. Bedewy (The Ohio State University), Yin Sun (Auburn University), Rahul Singh, Ness Shroff (The Ohio State University)
Online Control of Random Access with Splitting
Waqas Tariq Toor (Khwaja Fareed University of Engineering and Information Technology), Jun-Bae Seo (Hanyang University), Hu Jin (Hanyang University)
Xiaowen Gong (Auburn University)
De-anonymizability of Social Network: Through the Lens of Symmetry
Benjie Miao, Shuaiqi Wang, Luoyi Fu (Shanghai Jiao Tong University), Xiaojun Lin (Purdue University)
Towards Compression-Resistant Privacy-preserving Photo Sharing on Social Networks
Zhibo Wang, Hengchang Guo (Wuhan University), Zhifei Zhang (Adobe Research), Mengkai Song, Siyan Zheng, Qian WangI (Wuhan University), Ben Niu (Institute of Information Engineering, Chinese Academy of Sciences)
To the best of our knowledge, this paper gives the first attempt to investigate a generic compression-resistant scheme to protect photo privacy against DNNs in the social network scenario. We propose the Compression-Resistant Adversarial framework ComReAdv that can achieve adversarial examples robust to an unknown compression method. To this end, we design an encoding-decoding based compression approximation model (ComModel) to approximate the unknown compression method by learning the transformation from the original-compressed pairs of images queried through the social network. In addition, we involve the pretrained differentiable ComModel into the optimization process of adversarial example generation and adapt existing attack algorithms to generate compression-resistant adversarial examples. Extensive experimental results on different social networks demonstrate the effectiveness and superior resistance of the proposed ComReAdv to unknown compression as compared to the state-of-the-art methods.
Truthful Mobile Crowd Sensing with Interdependent Valuations
Meng Zhang (Northwestern University) Brian Swenson (Princeton University) Jianwei Huang (The Chinese University of Hong Kong, Shenzhen) H. Vincent Poor (Princeton University)
Lei Jiao (University of Oregon)