Tony T. Luo

Openings: PhD Positions | Postdoc Positions
• [Aug 2020] I was awarded a National Science Foundation (NSF) grant on Robust Federated Learning for Internet of Things from 2020 to 2023. Interested PhD students and postdocs, consider submitting your application to join this super exciting project.
• [Aug 2020] Our paper on mobile edge computing was accepted to Globecom 2020.
• [Jun 2020] Our paper on man-in-the-middle attacks in IoT was accepted to KDD 2020 Workshops (3rd AIoT).
• [Apr 2020] Our demo on contextual-bandit anomaly detection for IoT was accepted to ICDCS 2020.
• [Feb 2020] Our white paper on scalable distributed machine learning was accepted to National Science Foundation (NSF) Large Scale Networking Workshop on Huge Data.
• [Feb 2020] We are celebrating our university's 150th anniversary!
• [Dec 2019] Our paper on data privacy was accepted to ACM Transactions on Privacy and Security (TOPS).
• [Dec 2019] Our paper on IoT anomaly detection was accepted to AAAI 2020 Workshops (2nd AIoT).
• [Nov 2019] Two papers on trust prediction and mechanism design, respectively, were accepted to AAAI 2020.

Research Interests:

• Internet of Things

• Machine learning

• Security and privacy

• Wireless networks: sensor / ad hoc / cognitive radio networks
• Software-defined networking (SDN)
• Smart grid

Biosketch:

Teaching

• CS5001: Internet of Things with Applied Data Science (Spring 2020)
  Auditing students: sign up (no more slots now)
• CS6001: Internet of Things with Data Science (Fall 2020)
  No auditing option for this semester.

Selected Publications (see full list with illustrative pictures and acceptance rates)

• [Globecom'20] Coordinated Container Migration and Base Station Handover in Mobile Edge Computing
  M.V. Ngo, T. Luo, H.T. Hoang, and T. Quek
  IEEE Global Communications Conference (GLOBECOM), December 2020.
• [KDD'20] Man-in-the-Middle Attacks on MQTT-based IoT Using BERT based Adversarial Message Generation
  H. Wong and T. Luo
  26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 3rd AIoT Workshop, August 2020.
• [ICDCS'20] Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing
  M.V. Ngo, T. Luo, H. Chaouchi, and T. Quek
  IEEE International Conference on Distributed Computing Systems (ICDCS), Demo, December 2020.
• [Huge'20] Scalable Distributed Machine Learning with Huge Data for IoT and Scientific Discovery
  T. Luo and S. K. Das
  National Science Foundation (NSF) Large Scale Networking Workshop on Huge Data, Chicago, IL, April 13-14, 2020.
• [AAAI'20] COBRA: Context-aware Bernoulli neural networks for reputation assessment
  L. Zeynalvand, T. Luo, and J. Zhang
  34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, Feb 7-12, 2020, pp. 7317-7324.
• [AAAI'20] Mechanism design with predicted task revenue for bike sharing systems
  H. Lv, C. Zhang, Z. Zheng, T. Luo, F. Wu and G. Chen
  34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, Feb 7-12, 2020, pp. 2144-2151.
• [AAAI'20] Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing
  M. V. Ngo, H. Chaouchi, T. Luo, and T. Quek
  34th AAAI Conference on Artificial Intelligence (AAAI), 2nd AIoT Workshop, New York, NY, Feb 2020.
• [TOPS'20] CrowdPrivacy: Publish More Useful Data with Less Privacy Exposure in Crowdsourced Location-based Services [DOI: 10.1145/3375752]
  F-J. Wu and T. Luo
  ACM Transactions on Privacy and Security (TOPS), Feb 2020.
• [IoT-J'19] Improving IoT data quality in mobile crowd sensing: A cross validation approach [DOI: 10.1109/JIOT.2019.2904704]
  T. Luo, J. Huang, S. S. Kanhere, J. Zhang, and S. K. Das
  IEEE Internet of Things Journal (IoT-J), vol. 6, no. 3, pp. 5651-5664, June 2019.
  
  We propose a cross validation (CV) approach which seeks a validating crowd to ratify the contributing crowd in terms of the quality of sensor data contributed by the latter. Using a WRoS technique and a PATOP² algorithm which makes an exploration-exploitation tradeoff, the CV approach offers a unified solution to two typical yet disparate needs: reinforce obscure truth and discover hidden truth. (See illustration.)
• [AAIM'18] Achieving location truthfulness in rebalancing supply-demand distribution for bike sharing [DOI: 10.1007/978-3-030-04618-7_21]
  H. Lv, F. Wu, T. Luo, X. Gao, and G. Chen
  12th International Conference on Algorithmic Aspects in Information and Management (AAIM), pp. 256-267, December 2018.
  Best Student Paper Award


• [ICC'18] Distributed anomaly detection using autoencoder neural networks in WSN for IoT [slides] [DOI: 10.1109/ICC.2018.8422402]
  T. Luo and S. Nagarajan
  IEEE International Conference on Communications (ICC), May 2018.
  
  This paper is the first that introduces autoencoder neural networks (ANN), a deep learning model, into wireless sensor networks (WSN) to detect anomalies. It contradicts the general belief that "deep learning is not suitable for WSN", by (1) making deep learning (extremely) shallow and (2) allocates computation load to sensors and IoT cloud using a two-part algorithm, DADA-S and DADA-C. (See illustration.)
• [ComMag'17] Sustainable incentives for mobile crowdsensing: Auctions, lotteries, and trust and reputation systems [DOI: 10.1109/MCOM.2017.1600746CM]
  T. Luo, S. S. Kanhere, J. Huang, S. K. Das, and F. Wu
  IEEE Communications Magazine, vol. 55, no. 3, pp. 68-74, March 2017.
  
  This survey paper provides a technical overview and analysis of six incentive mechanism design frameworks: auction, lottery, trust and reputation system, bargaining game, contract theory, and market-driven mechanism.
• [TIST'16] Incentive mechanism design for crowdsourcing: an all-pay auction approach [pdf] [DOI: 10.1145/2837029]
  T. Luo, S. K. Das, H-P. Tan, and L. Xia
  ACM Transactions on Intelligent Systems and Technology (TIST), vol. 7, no. 3, pp. 35:1-26, February 2016.
  
  The most common auctions used in incentive mechanism design are winner-pay auctions, where only winners (i.e., highest bidders who will receive reward) need to pay for their bids (by money or effort). In contrast, all-pay auctions require every bidder to pay regardless of who wins, which sounds rather unreasonable. However, we show that applying all-pay auctions to crowdsourcing makes perfect sense and gains several advantages over winner-pay auctions. (This paper builds on our INFOCOM'14 paper.)
• [TMC'16] Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests [DOI: 10.1109/TMC.2015.2485978]
  T. Luo, S. S. Kanhere, S. K. Das, and H-P. Tan
  IEEE Transactions on Mobile Computing (TMC), vol. 15, no. 9, pp. 2234-2246, September 2016.
  
  Despite that crowdworkers are typically heterogeneous (in terms of "types" such as abilities, costs, etc.), the daunting challenge of modeling and analyzing it has restricted researchers to homogeneous models in which all the worker types follow a single, common distribution (Bayesian belief). This paper addresses this challenge using an asymmetric all-pay auction model with a prize tuple. In addition, a most interesting Strategy Autonomy (SA) phenomenon is discovered.
• [INFOCOM'15] Crowdsourcing with Tullock contests: A new perspective [DOI: 10.1109/INFOCOM.2015.7218641]
  T. Luo, S. S. Kanhere, H-P. Tan, F. Wu, and H. Wu
  The 34th IEEE International Conference on Computer Communications (INFOCOM), April 2015, pp. 2515-2523.
  Best Paper Award nominee
  
  What is a Tullock contest? Think it as a lucky draw! While auctions have dominated the realm of mechanism design for decades, this paper suggests that Tullock contests is a better alternative in that it is more "friendly" to ordinary participants ("grass roots"): You always have a chance to win, no matter how 'weak' you are.
• [TMC'15] Quality of contributed service and market equilibrium for participatory sensing [DOI: 10.1109/TMC.2014.2330302]
  C-K. Tham and T. Luo
  IEEE Transactions on Mobile Computing (TMC), vol. 14, no. 4, pp. 829-842, April 2015.
  
  In order to characterize QoS for crowdsensing, this work proposes a metric called Quality of Contributed Service (QCS) which aggregates individual quality of contributions and takes into account information quality and time sensitivity. QCS is then analyzed using a market based supply-and-demand model.
• [INFOCOM'14] Profit-maximizing incentive for participatory sensing [DOI: 10.1109/INFOCOM.2014.6847932] [Much enhanced version: ACM TIST'16]
  T. Luo, H-P. Tan, and L. Xia
  The 33rd IEEE International Conference on Computer Communications (INFOCOM), April 2014, pp. 127-135.
  
  All-pay auction + Adaptive (variable) prize (reward). (See illustration.)
• [ICTC'12] Enhancing Responsiveness and Scalability for OpenFlow Networks via Control-Message Quenching [DOI: 10.1109/ICTC.2012.6386857]
  T. Luo, H-P. Tan, P. C. Quan, Y-W. Law, and J. Jin
  International Conference on ICT Convergence (ICTC), October 2012, pp. 348-353.
  Best Paper Award
  

(see full list with illustrative pictures and acceptance rates)

Systems Work

Besides theory, I am also keen in developing real systems. I worked with team to have developed the following mobile crowdsourcing / crowdsensing applications, which are freely downloadable at Apple Store and Google Play. The latest software update was in 2018.
Related Publications:

• [SECON'14] T. Luo, S. Kanhere, and H-P. Tan, "SEW-ing a Simple Endorsement Web to incentivize trustworthy participatory sensing," IEEE International Conference on Sensing, Communication, and Networking (SECON), July 2014.
  
  SEW is the foundation of the incentive engine implemented by FoodPriceSG & imReporter. It introduces an endorsement relationship to connect participants into an socio-economic network to incentivize trustworthy crowdsensing.
• [MASS'14] F-J. Wu and T. Luo, "WiFiScout: A crowdsensing WiFi advisory system with gamification-based incentive," IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), October 2014.
  
  WiFi-Scout is a crowdsensing application that provides WiFi locations, speed, and security modes by using crowdsensed WiFi signals and user inputs.

Professional Services

• Journal Editorial Board:
  Ad Hoc Networks (Elsevier) [SCI, IF: 3.64 (2019)]: Area Editor, 2019-present.
  Wireless Communications and Mobile Computing (Wiley & Hindawi) [SCI]: Editor, 2018-present.
  Telecom (MDPI): Editor, 2018-present.
  Mobile Information Systems [SCI]: Guest Editor (Big Data Management and Analytics for Mobile Crowd Sensing), 2015-2016.
  Journal of Sensor and Actuator Networks (MDPI): Guest Editor (MAC Protocols in Wireless Sensor Networks), 2015-2016.
• Journal Advisory Board:
  Sci (MDPI): 2018-present.
• Conference TPC Co-Chair:
  IEEE Percom CASPer 2016
  ACM ComNet-IoT 2016
  IEEE ISSNIP 2014 Symposium on Participatory Sensing & Crowdsourcing
• Conference Session Chair:
  IEEE INFOCOM 2020
• Conference TPC Member:
  2021: INFOCOM | AAAI | WoWMoM | ICC | WCNC
  2020: INFOCOM | WoWMoM | ICC | WCNC | ComNet-IoT | PST (Privacy, Security and Trust)
  2019: INFOCOM | WoWMoM | ICC | WCNC | MASS | MSWiM | Percom CASPer | ComNet-IoT | IWCMC-ML (machine learning) | PST (Privacy, Security and Trust)
  2018: INFOCOM | WoWMoM | MASS | Globecom | ICCCN | Percom CASPer | ComNet-IoT | PST (Privacy, Security and Trust)
  2017: DCOSS | Percom CASPer | UIC (Ubiquitous Intelligence and Computing)
  2016: WCNC | MobiSPC | AAMAS Trust | PST (Privacy, Security and Trust) | BIH (Brain Informatics and Health)
  2015: WCNC | MobiSPC | SenseApp | ICCVE | CCBD (Cloud Computing and Big Data) | IBDC (Big Data in Crowdsensing)
  2014: WCNC | SenseApp | ICCVE | IOV (Internet of Vehicles)
  2013: WCNC | SenseApp | ICCVE | AMI (Ambient Intelligence) | IoT-SC (IoT for Smart Cities)
  2012: ICCVE | KICSS (Knowledge, Information and Creativity Support Systems)
• Conference Organizing Committee:
  IEEE ISSNIP 2015
  IEEE ISSNIP 2014 (International Conference on Intelligent Sensors, Sensor Networks, and Information Processing)
• Journal Reviewer (Top 1% Reviewer in Computer Science, 2017-2018):
  IEEE/ACM Transactions on Networking (ToN) (2010--present)
  IEEE Transactions on Mobile Computing (TMC) (2009--present)
  IEEE Journal on Selected Areas in Communications (JSAC) (2008--present)
  IEEE Transactions on Knowledge and Data Engineering (TKDE) (2018--present)
  IEEE Transactions on Wireless Communications (TWC) (2013--present)
  IEEE Transactions on Vehicular Technology (TVT) (2010--present)
  IEEE Transactions on Cognitive Communications and Networking (TCCN) (2016--present)
  IEEE Transactions on Network and Service Management (TNSM) (2017--present)
  IEEE Internet of Things Journal (IOT-J) (2019--present)
  IEEE Computer (2018--present)
  IEEE Network (2015--present)
  IEEE Pervasive Computing (2017--present)
  ACM Transactions on Internet Technology (TOIT) (2018--present)
  ACM Mobile Computing and Communications Review (MC2R) (2009--present)
  Elsevier - Pervasive and Mobile Computing (PMC) (2013--present) [Outstanding Reviewer 2016]
  Elsevier - Computer Networks (COMNET) (2008--present)
  Elsevier - Computer Communications (COMCOM) (2017--present)
  Elsevier - Ad Hoc Networks (ADHOC) (2013--present)
  Elsevier - Journal of Parallel and Distributed Computing (JPDC) (2018--present)
  Elsevier - Information Systems (IS) (2018--present)
  Elsevier - Future Generation Computer Systems (FGCS) (2018--present)
  Elsevier - Digital Communications and Networks (DCAN) (2018--present)
  Springer Nature - Peer-to-Peer Networking and Applications (PPNA) [IF 2.397] (2020--present)
  MDPI - Sensors [IF 3.031] (2016--present)
  Wiley - Wireless Communications and Mobile Computing (WCMC) (2018--present)
  Wiley - International Journal of Network Management (NEM) (2020--present)
  Hindawi - Discrete Dynamics in Nature and Society (2019--present)
  IET Intelligent Transport Systems (ITS) (2018--present)
  IEICE Transactions on Fundamentals (2014--present)
• Conference Reviewer (apart from TPC):
  MSWiM 2018, PIMRC 2018, Globecom 2017, ICDM 2016, ACM KDD 2015, MASS 2014, ICC 2013, Globecom 2012, DySPAN 2010, INFOCOM 2009, SECON 2009, Globecom 2009, ICC 2009, INFOCOM 2008, SECON 2008, MASS 2008, ICDCS 2008, Globecom 2008, MobiCom 2007, MSWiM 2007, MSWiM 2006... (full list)

Miscellaneous

• Advice on Research and Writing (primarily for computer scientists)
• Conference statistics (acceptance rate etc.): Networking | Artificial Intelligence | Computer Security | Theoretical Computer Science]
• Top conferences: Machine Learning & Artificial Intelligence | Computer Networks & Communications
• Top journals (with impact factor): Machine Learning & Artificial Intelligence | Computer Networks & Communications
• Conference and journal combined ranking: Computer Networks | Network and Information Security | AI and Pattern Recognition
• My DBLP (BibTex downloadable from the 2nd icon there)
• My Google Scholar citations