Building Reliable Network of Unreliable Things

NSF CNS-1652503

Principle Investigator

  • Lu Su, Assistant Professor

Students

  • Chenglin Miao, PhD student
  • Wenjun Jiang, PhD student
  • Yaqing Wang, PhD student
  • Shiyang Wang, PhD student

Award Information

This website is based upon work supported by the National Science Foundation under grant CNS-1652503. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Project Background and Goals

Recent years have witnessed the rise of Internet of Things (IoT), a newly emergent networking paradigm that connects humans and the physical world through ubiquitous sensing, computing, and communicating devices. With the ultimate goal of building reliable, robust, and secure IoT systems that are usually composed of multitudes of unreliable wireless devices - sometimes even carried by malicious users - this project develops TRIP, a 3-in-1 integrated framework of TRuth discovery, Incentive, and Privacy preserving mechanisms for IoT systems. This framework consists of 1) a truth discovery mechanism that can distill true information from the deluge of sensory data generated by the ubiquitous IoT devices, 2) a security and privacy mechanism that can not only protect user privacy but also defend against malicious attack, and 3) an incentive mechanism that can select reliable participants in order to maximize the quality of collected information.

Project Impact

In order to realize the proposed TRIP system, this project addresses a series of important research challenges. First, to infer truth information from the noisy and sparse sensory data collected by human-carried IoT devices, this project delivers a novel truth discovery method that can capture the variety in the reliability of different users as well as the correlations among the objects. Second, this project investigates not only privacy preserving mechanisms that allow the server to conduct truth discovery operations without knowing the genuine values of user observations, but also security solutions that defend against malicious users who try to inject false information for the purpose of sabotage or financial rewards. Third, an effective incentive mechanism is developed to motivate reliable users to contribute to the IoT tasks. Finally, the above mechanisms are seamlessly integrated into networked IoT systems in a distributed and parallel manner, and evaluated on real testbeds in order to validate the effectiveness, efficiency, and the practicality of the proposed research.

Publications

  • Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, and Aidong Zhang, "Towards Confidence Interval Estimation in Truth Discovery," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.31, No.3, pp.575-588, March 2019.[pdf]
  • Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren, "Privacy-Preserving Truth Discovery in Crowd Sensing Systems," ACM Transactions on Sensor Networks (TOSN), Vol.15, Issue 1, No.9, February 2019.[pdf]
  • Hongfei Xue, Wenjun Jiang, Chenglin Miao, Ye Yuan, Fenglong Ma, Xin Ma, Yijiang Wang, Shuochao Yao, Wenyao Xu, Aidong Zhang, Lu Su, "DeepFusion: A Deep Learning Framework for the Fusion of Heterogeneous Sensory Data," the 20th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2019), Catania, Italy, July 2019.
  • Haiming Jin, Hongpeng Guo, Lu Su, Klara Nahrstedt, Xinbing Wang, "Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium," the 38th IEEE International Conference on Computer Communications (INFOCOM 2019), Paris, France, April, 2019.
  • Haiming Jin, Lu Su, Houping Xiao, Klara Nahrstedt, "Incentive Mechanism for Privacy-Aware Data Aggregation in Mobile Crowd Sensing Systems," IEEE/ACM Transactions on Networking (TON), Vol.26, No.5, pp.2019-2032, October 2018.[pdf]
  • Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, Wenyao Xu, Lu Su, "Towards Environment Independent Device Free Human Activity Recognition," the 24th ACM International Conference on Mobile Computing and Networking (MobiCom 2018), New Delhi, India, October 2018.[pdf][video]
  • Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, Lu Su, "TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data," the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018.[pdf]
  • Yaliang Li (co-first author), Chenglin Miao (co-first author), Lu Su, Jing Gao, Qi Li, Bolin Ding, Zhan Qin, and Kui Ren, "An Efficient Two-Layer Mechanism for Privacy-Preserving Truth Discovery," the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018.[pdf]
  • Hengtong Zhang, Fenglong Ma, Yaliang Li, Chao Zhang, Tianqi Wang, Yaqing Wang, Jing Gao, and Lu Su, "Leveraging the Power of Informative Users for Local Event Detection," the IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM 2018), Barcelona, Spain, August 2018.[pdf]
  • Wenjun Jiang, Chenglin Miao, Lu Su, Qi Li, Shaohan Hu, Shiguang Wang, Jing Gao, Hengchang Liu, Tarek F. Abdelzaher, Jiawei Han, Xue Liu, Yan Gao, Lance Kaplan, "Towards Quality Aware Information Integration in Distributed Sensing Systems," IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol.29, No.1, pp.198-211, January 2018.[pdf]
  • Wenjun Jiang, Qi Li, Lu Su, Chenglin Miao, Quanquan Gu, and Wenyao Xu, "Towards Personalized Learning in Mobile Sensing Systems," the 38th International Conference on Distributed Computing Systems (ICDCS 2018), Vienna, Austria, July 2018.[pdf]
  • Chenglin Miao, Qi Li, Houping Xiao, Wenjun Jiang, Mengdi Huai, and Lu Su, "Towards Data Poisoning Attacks in Crowd Sensing Systems," the 19th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2018), Los Angeles, CA, June 2018.[pdf] (Best Paper Award Nominee)
  • Liuyi Yao, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao, Aidong Zhang, "Online Truth Discovery on Time Series Data," the 18th SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, May 2018.[pdf]
  • Chenglin Miao, Qi Li, Lu Su, Mengdi Huai, Wenjun Jiang and Jing Gao, "Attack under Disguise: An Intelligent Data Poisoning Attack Mechanism in Crowdsourcing," the 27th World Wide Web Conference (WWW 2018), Lyon, France, April, 2018.[pdf]
  • Yaqing Wang, Fenglong Ma, Lu Su, Jing Gao, "Discovering Truths from Distributed Data," the 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, LA, November 2017.[pdf]
  • Haiming Jin, Lu Su, Klara Nahrstedt, "Theseus: Incentivizing Truth Discovery in Mobile Crowd Sensing Systems," the 18th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2017), Chennai, India, July 2017.[pdf]

Courses

  • CSE 489/589: Modern Network Concepts
  • CSE 728: Selected Topics on Internet of Things
  • CSE 726: Selected Topics in Crowd Sensing Systems
  • CSE 721: Selected Topics in Mobile Sensing