- [Apr 2020] Our demo on Contextual-Bandit Anomaly Detection for IoT was accepted to ICDCS 2020.
- [Mar 2020] Opening Positions for PhD/Master intake of Fall 2020.
- [Feb 2020] Our white paper on Scalable Distributed Machine Learning was accepted to NSF Large Scale Networking (LSN) Workshop on Huge Data.
- [Feb 2020] We are celebrating our university's 150th anniversary!
- [Dec 2019] Our paper was accepted to ACM Transactions on Privacy and Security (TOPS).
- [Nov/Dec 2019] Three papers accepted to AAAI 2020: two in main conference and one in workshop (AIoT).
- Prospective students: If you are (i) seeking a PhD/Master degree or a visiting scholar position, (ii) passionate about IoT, machine learning, cybersecurity, and (iii) good at mathematics or programing, consider dropping me an email.
- Internet of Things
- Machine learning
- Security, trust, and privacy
- Wireless networks: sensor / ad hoc / cognitive radio
- Software-defined networking (SDN)
- Smart grid
Brief Bio: I conduct research on security mechanisms for Internet of Things (IoT) systems and machine learning algorithms, as well as develop novel IoT systems empowered by artificial intelligence (primarily via machine learning). My objectives are to safeguard IoT systems from their vulnerabilities, make machine-learning algorithms robust to adversarial and unreliable behaviors, and develop AIoT applications that are of high economic and societal value. Prior to joining Missouri S&T, I was a Program Lead and Research Scientist at A*STAR Singapore. I earned my Ph.D. in electrical and computer engineering from the National University of Singapore.
Selected Publications (see complete list with illustrations)
- [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 Track, July 2020.
Acceptance rate: 30% (13/43)
- [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 (LSN) Workshop on Huge Data, Chicago, IL, April 13-14, 2020.
- [AAAI'20] Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [pdf]
M. V. Ngo, H. Chaouchi, T. Luo, and T. Quek
AAAI Workshop on Artificial Intelligence of Things (AIoT), New York, NY, Feb 2020.
- [AAAI'20] COBRA: Context-aware Bernoulli neural networks for reputation assessment [pdf]
L. Zeynalvand, T. Luo, and J. Zhang
34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, Feb 7-12, 2020.
- [AAAI'20] Mechanism design with predicted task revenue for bike sharing systems [pdf]
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.
- [TOPS'20] CrowdPrivacy: Publish More Useful Data with Less Privacy Exposure in Crowdsourced Location-based Services
F-J. Wu and T. Luo
ACM Transactions on Privacy and Security (TOPS), 2020. To appear.
- [IoT-J'19] Improving IoT data quality in mobile crowd sensing: A cross validation approach [pdf] [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 weighted random oversamping (WRoS) technique and a PATOP2 algorithm which makes an exploration-exploitation tradeoff, our proposed CV approach offers a unified solution to two typical yet disparate needs: reinforce obscure truth and discover hidden truth.
- [AAIM'18] Achieving location truthfulness in rebalancing supply-demand distribution for bike sharing [pdf] [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 [pdf] [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 work 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.
- [ComMag'17] Sustainable incentives for mobile crowdsensing: Auctions, lotteries, and trust and reputation systems [pdf] [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 [ACM Lib] [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 commonly used auctions for 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, applying all-pay auctions to crowdsourcing, as this paper does, gains several advantages over winner-pay auctions, and reaps much higher profit with an adaptive prize.
- [TMC'16] Incentive mechanism design for heterogeneous crowdsourcing using all-pay contests [pdf] [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 heterogeneous in their "types" (abilities, costs, etc.) as we all know, the hardness to model and analyze it has restricted researchers to adopt a homogeneous model where all the workers' types are assumed to follow a (single) common Bayesian belief. This paper proposes an asymmetric all-pay auction model to characterize the heterogeneity, and uses a prize tuple to achieve an interesting and counter-intuitive property called Strategy Autonomy (SA).
- [INFOCOM'15] Crowdsourcing with Tullock contests: A new perspective [pdf] [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.
Acceptance rate: 19%
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 Tullock contests as an alternative mechanism that is more appealing to "ordinary" participants. Tullock contests distinguish themselves from auctions in its imperfectly discriminating property: "You always have a chance to win, no matter how 'weak' you are." This feature is particularly desirable for, e.g., micro-task crowdsourcing (such as crowd sensing).
- [TMC'15] Quality of contributed service and market equilibrium for participatory sensing [pdf] [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 [pdf] [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.
Acceptance rate: 19% (319 out of 1650)
- Enhancing Responsiveness and Scalability for OpenFlow Networks via Control-Message Quenching [pdf]
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 Complete List with instructive illustrations]
Besides theory, I am also keen in developing real systems. My team has developed the following three mobile crowdsourcing/crowdsensing applications, which can be downloaded for free at Apple Store and Google Play. The latest version update was in 2018:
- [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. [pdf]
Acceptance rate: 28.6%
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. [pdf]
WiFi-Scout is a crowdsensing application that provides WiFi locations, speed, and security modes by using crowdsensed WiFi signals and user inputs.
Tutorial & Invited Talks
- Shanghai Jiao Tong University, "Smart Factories: Augmenting intelligence for Industrial IoT with machine learning", May 2018.
- IEEE ICC 2016 Tutorial, "Mobile crowdsourcing: Incentives, Trust, and Privacy", May 2016. [slides]
- Sun Yat-sen University, "Building Internet of Things and smart cities via mobile crowd sensing", December 2016.
- Xiamen University, "Empowering smart cities and the Internet of Things: A mobile crowdsensing perspective", December 2016.
- Chinese University of Hong Kong, "Incentive mechanism design and trust systems for crowdsourcing", May 2015.
- Singapore University of Technology and Design, "Incentives and trustworthiness in crowdsourcing", December 2014.
- University of Electronic Science and Technology of China, "Incentivizing trustworthy human-centric systems", April 2014.
- University of Melbourne, Australia, "Incentives and QoS in participatory sensing", March 2012.
IEEE Senior Member
- Journal Editorial Board:
Ad Hoc Networks (Elsevier) [SCI, 2018 IF: 3.49]: Area Editor, 2019-present.
Wireless Communications and Mobile Computing (Wiley) [SCI, IF: 1.9]: Editor, 2018-present.
Telecommunications (MDPI): Editor, 2018-present.
Mobile Information Systems [SCI]: Guest Editor, 2015-2016.
Journal of Sensor and Actuator Networks (MDPI): Guest Editor, 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 TPC Member:
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 for 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 (Data Mining), KDD 2015 (Knowledge Discovery and Data Mining), 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)