Building Quality-of-Information Aware Distributed Sensing Systems

NSF CNS-1566374

Principle Investigator

  • Lu Su, Assistant Professor

Students

  • Fenglong Ma, PhD student
  • Chuishi Meng, PhD student
  • Enshu Wang, PhD student

Award Information

This website is based upon work supported by the National Science Foundation under Grant No. CNS-1566374. 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

The proliferation of increasingly capable and affordable sensing devices that pervade every corner of the world has given rise to distributed sensing systems that have fundamentally changed people's ways of interacting with the physical world. Despite their tremendous benefits, distributed sensing systems pose great new research challenges, of which one important facet stems from the conflicts between the Quality of Information (QoI) provided by the sensor nodes and the consumption of system and network resources. On one hand, individual sensors are not reliable, due to various reasons such as incomplete observations, background noise, and poor sensor quality. To address this problem, a possible solution is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors. On the other hand, distributed sensing systems usually have limited resources (e.g., bandwidth, energy, storage, etc). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on this challenge, this project seeks to develop a resource-efficient information integration framework that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources.

Project Impact

This project will lead to method development, analysis, and system prototypes for quality-of-information aware distributed sensing systems, which have the following broader impacts: 1) The development of the QoI aware information integration framework in this project will help address growing research challenges for the collection, transmission and analysis of massive sensory data. 2) The proposed QoI aware resource allocation mechanisms will advance the state-of-the-art in both physical and crowd sensing system research by addressing novel challenges brought by constrained system resources. 3) Successful completion of the proposed research will benefit a whole spectrum of applications that have tremendous natural and societal impact, including environment monitoring, military surveillance, smart transportation, urban sensing, health care, spectrum sensing, and many others. We expect the outputs of this project can inspire new research ideas in not only computer science but also many other disciplines such as transportation engineering, industrial engineering, animal and environmental science, and social science. 4) The research results will be integrated into course materials and K-12 outreach activities, and thus can benefit a large group of students, especially the female and minority students.

Publications

  • Chuishi Meng, Houping Xiao, Lu Su, Yun Cheng, "Tackling the Redundancy and Sparsity in Crowd Sensing Applications," the 14th ACM Conference on Embedded Networked Sensor Systems (SenSys 2016), Stanford, CA, November 2016.[pdf]
  • Haiming Jin, Lu Su, Bolin Ding, Klara Nahrstedt, Nikita Borisov, "Enabling Privacy-Preserving Incentives for Mobile Crowd Sensing Systems," the 36th International Conference on Distributed Computing Systems (ICDCS 2016), Nara, Japan, June 2016.[pdf]
  • Haiming Jin, Lu Su, Houping Xiao, Klara Nahrstedt, "INCEPTION: Incentivizing Privacy-Preserving Data Aggregation for Mobile Crowd Sensing Systems," the 17th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2016), Paderborn, Germany, July 2016.[pdf]
  • Hengtong Zhang, Qi Li, Fenglong Ma, Houping Xiao, Yaliang Li, Jing Gao, Lu Su, "Influence-Aware Truth Discovery," the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, October 2016.[pdf]
  • Houping Xiao, Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su, Han Liu, "A Truth Discovery Approach with Theoretical Guarantee," the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, August 2016.[pdf]
  • Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, Aidong Zhang, "Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach," the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, August 2016.[pdf]
  • Hu Ding, Lu Su, Jinhui Xu, "Towards Distributed Ensemble Clustering for Networked Sensing Systems: A Novel Geometric Approach," the 17th ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2016), Paderborn, Germany, July 2016.[pdf]
  • Qian Wang, Kui Ren, Man Zhou, Tao Lei, Dimitrios Koutsonikolas, and Lu Su, "Messages Behind the Sound: Real-Time Hidden Acoustic Signal Capture with Smartphones," the 22nd ACM International Conference on Mobile Computing and Networking (MobiCom 2016), New York, NY, October 2016.[pdf]
  • Yaliang Li, Qi Li, Jing Gao, Lu Su, Bo Zhao, Wei Fan, Jiawei Han, "Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth Discovery," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.28, No.8, pp.1986-1999, August 2016.[pdf]
  • Yaliang Li, Qi Li, Jing Gao, Patrick Lee, Wei Fan, Lu Su, Caifeng He, Cheng He, Fan Yang, "A Weighted Crowdsourcing Approach for Network Quality Measurement in Cellular Data Networks," IEEE Transactions on Mobile Computing (TMC), Vol.16, No.2, pp.300-313, February, 2017.[pdf]

Courses

  • CSE 489/589: Modern Network Concepts
  • CSE 524: Realtime & Embedded Systems
  • CSE 726: Selected Topics in Crowd Sensing Systems
  • CSE 721: Selected Topics in Mobile Sensing