I am a Research Scientist from the Infrastructure, Energy and Environment (IEE) Department, and the Programme Lead of the "Sensor & Network Analytics" programme, both at the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore. I received my PhD degree in Electrical and Computer Engineering from the National University of Singapore. Prior to joining I2R A*STAR, I did my postdoc and worked in the industry during different stints. I am an IEEE Senior Member.
- Internet of Things: IoT analytics with machine learning; security, privacy, and trust in IoT
- Mobile crowdsourcing and crowdsensing: incentives and trust
- Mobile edge computing
- Wireless networks: sensor / ad hoc / cognitive radio
- Software-defined networking (SDN)
- Smart grid
Selected Publications (see complete list with )
- [IoT-J'19] Improving IoT Data Quality in Mobile Crowd Sensing: A Cross Validation Approach [early preprint] [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), 2019.
- [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 [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 that introduces a deep learning model, autoencoder neural networks (ANN), into wireless sensor networks (WSN) to detect anomalies. It contradicts the general belief that "deep learning is not suitable for WSN" by "making deep learning (extremely) shallow" via constructing a neural network with only a single hidden layer. In addition, it allocates computation load through a two-part algorithm that has a complexity of only O(M2) on each sensor.
- [Globecom'17] Reshaping mobile crowd sensing using cross validation to improve data credibility [pdf] [slides] [DOI: 10.1109/GLOCOM.2017.8255050]
T. Luo and L. Zeynalvand
IEEE Global Communications Conference (GLOBECOM), December 2017.
This paper proposes a cross-validation approach which seeks a validating crowd to validate the contributing crowd on the quality of data contributed by the latter. A specific cross-validation mechanism is designed to reshape the original, noisy crowdsensed data into a more credible posterior belief of the ground truth. It achieves two "incompatible" goals simultaneously: (1) reinforce obscure truth and (2) discover hidden truth.
- [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, namely auction, lottery, trust and reputation system, as well as 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 (highest bidders) need to pay (money or effort) for their bids (and be rewarded). On the contrary, all-pay auctions require every worker to pay regardless of who will win, while still rewarding the winners only. This appears to be rather unreasonable, but we show in this paper that all-pay auctions not only have several advantages over winner-pay auctions, but can also generate higher profit by using 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 as we all know, its hardness in modeling and analysis restricts prior work to have adopted a homogeneous model which imposes a (single) common Bayesian belief on all the workers. This paper addresses this challenge by modeling the heterogeneity (each worker is associated with a different probability distribution) using an asymmetric all-pay auction with a prize tuple.
- [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 being imperfectly discriminating: "You always have a chance to win, no matter how 'weak' you are." This feature is particularly desirable for, e.g., large-scale crowdsensing.
- [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.
- [COMNET'14] Fairness and social welfare in service allocation schemes for participatory sensing [pdf] [DOI: 10.1016/j.comnet.2014.07.013]
C-K. Tham and T. Luo
Computer Networks, Elsevier, vol. 73, pp. 58-71, November 2014.
Instead of using monetary or reputation-based reward, this paper proposes a resource-allocation approach to incentivize crowdsensing. Specifically, it allocates each user some "service quota" based on the amount of contribution the user makes. A typical applicable scenario is a platform that uses crowdsensed data to provide some information service (transport, air quality, healthcare, etc.).
- [MASS'14] Optimal prizes for all-pay contests in heterogeneous crowdsourcing [pdf] [DOI: 10.1109/MASS.2014.66] [Extended work: TMC'16]
T. Luo, S. Kanhere, S. Das, and H-P. Tan
The 11th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), October 2014, pp. 136-144.
Acceptance rate: 26.5% (48 out of 181)
We all know that crowdworkers are heterogeneous, but it is hard to model and analyze. Therefore, existing work usually assumes the same distribution (e.g., uniform or normal) for all the workers, without considering the "deeper" heterogeneity where each individual worker has her own distribution (associated with her "type"). This paper deals with this model and designs an incentive mechanism based on asymmetric all-pay auctions.
- [SECON'14] SEW-ing a Simple Endorsement Web to incentivize trustworthy participatory sensing [pdf] [DOI: 10.1109/SAHCN.2014.6990404]
T. Luo, S. Kanhere, and H-P. Tan
The 11th IEEE International Conference on Sensing, Communication, and Networking (SECON), July 2014, pp. 636-644.
Acceptance rate: 28.6% (67 out of 234)
This work proposes a notion of "nepotism" which strikes a trade-off between egoism (game-theoretical) and altruism (philanthropic) to capture human nature in a more realistic way. It introduces an endorsement relationship to connect participants into an socio-economic network to incentivize trustworthy crowdsensing.
[SEW has been implemented in two software systems, FoodPriceSG and imReporter. See below.]
- [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 ]
Besides theory, I am also keen in developing real systems. The below are three mobile crowdsourcing/crowdsening software systems developed by my team, free downloadable at Apple Store and Google Play:
- [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] (This paper is the foundation of the incentive engine implemented by FoodPriceSG & imReporter. See above in publications.)
- [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] (This short paper summarizes a crowdsensing application called WiFi-Scout which provides WiFi mapping using crowdsensed 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:
Wireless Communications and Mobile Computing (Wiley) [SCI-indexed, 2016/17 IF: 1.9, 2017 acceptance rate: 20%]: Editor, 2018-present.
Telecommunications (MDPI): Editor, 2018-present.
Mobile Information Systems [SCI-indexed, 2013/14 IF: 1.8]: 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:
2019: INFOCOM | WoWMoM | MASS | ICC | WCNC | 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 Member:
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--)
IEEE Transactions on Mobile Computing (TMC) (2009--)
IEEE Journal on Selected Areas in Communications (JSAC) (2008--)
IEEE Transactions on Knowledge and Data Engineering (TKDE) (2018--)
IEEE Transactions on Wireless Communications (TWC) (2013--)
IEEE Transactions on Vehicular Technology (TVT) (2010--)
IEEE Transactions on Cognitive Communications and Networking (TCCN) (2016--)
IEEE Transactions on Network and Service Management (TNSM) (2017--)
IEEE Internet of Things Journal (IOT-J) (2019--)
IEEE Computer (2018--)
IEEE Network (2015--)
IEEE Pervasive Computing (2017--)
ACM Transactions on Internet Technology (TOIT) (2018--)
ACM Mobile Computing and Communications Review (MC2R) (2009--)
Elsevier - Pervasive and Mobile Computing (PMC) (2013--) [Outstanding Reviewer 2016]
Elsevier - Computer Networks (COMNET) (2008--)
Elsevier - Computer Communications (COMCOM) (2017--)
Elsevier - Ad Hoc Networks (ADHOC) (2013--)
Elsevier - Journal of Parallel and Distributed Computing (JPDC) (2018--)
Elsevier - Information Systems (IS) (2018--)
Elsevier - Future Generation Computer Systems (FGCS) (2018--)
Elsevier - Digital Communications and Networks (DCAN) (2018--)
MDPI - Sensors (2016--)
Wiley - Wireless Communications and Mobile Computing (WCMC) (2018--)
IET Intelligent Transport Systems (ITS) (2018--)
IEICE Transactions on Fundamentals (2014--)
- Conference Reviewer (besides 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)