Tony T. Luo

• [Apr 2023] Princeton Review ranked Missouri S&T #3 for best career placement, #9 for internships, and #24 for best return on investment (ROI), among the US top public universities, in the "Best Value Colleges" ranking. [News]
• [Mar 2023] New York Times ranked Missouri S&T the #8 high-value universities (Harvard University is #7).
• [Aug 2022] Forbes reported that DegreeChoices ranked Missouri S&T #1 in the state and #12 among all US universities for graduates’ economic success. [screenshot-1; screenshot-2]
• [Jun 2022] Money ranked Missouri S&T #1 in the state and #5 in the US for engineering.
• [May 2022] Money ranked Missouri S&T #1 in the state and #41 out of 623 universities in the US overall.
• [Jan 2022] Intelligent recognized Missouri S&T as among the Best Master’s in Computer Science Degree Programs of 2022.
• [Jul 2021] SmartAsset ranked Missouri S&T #1 for best value and #1 for starting salary of graduates in the state of Missouri, and #9 in the US for ROI. [screenshot]
• [Oct 2020] Missouri S&T received a $300 million donation from its alumni, Fred Kummer and his wife June Kummer. This gift is the largest ever in the history of Missouri higher education, and one of the largest ever to any public university in the US. Here's what Kummer said: "I owe much of my success to the education I received at Rolla. My Rolla experience taught me how to think, how to work hard and how to manage my own career. [...] We believe that Missouri S&T's best days are ahead."

Research Interests:

• Machine Learning

• Cybersecurity

• Internet of Things

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

Prospective PhD students: please read this before applying.

Research Grant

Biosketch:

Teaching

• Fall 2023:    CS5420 - Introduction to Machine Learning
• Spring 2023: CS3402 - Introduction to Data Science
• Fall 2022:    CS5001 (CS5420 since FS23) - Introduction to Machine Learning
• Spring 2022: CS3001 (CS3402 since SP23) - Introduction to Data Science
• Fall 2021:    CS6407 - Internet of Things with Data Science
• Spring 2021: CS6001 (CS6407 since FS21) - Internet of Things with Data Science
• Fall 2020:    CS6001 (CS6407 since FS21) - Internet of Things with Data Science
• Spring 2020: CS5001 - Internet of Things with Applied Data Science

Publications    (==> See full list with diagrams and pictures)

• [TrustCom'23] Crowdsourcing-based Model Testing in Federated Learning
  Y. Yi, H. Lv, T. Luo, L. Liu, L. Cui
  22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), November 2023, accepted.
• [Globecom'23] Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation
  M. Elmahallawy and T. Luo
  IEEE Global Communications Conference (GLOBECOM), December 2023, accepted.
• [ComMag'23] Catching Elusive Depression via Facial Micro-Expression Recognition
  X. Chen and T. Luo
  IEEE Communications Magazine. To appear in October 2023.
• [ICIP'23] GNP Attack: Transferable Adversarial Examples via Gradient Norm Penalty
  T. Wu, T. Luo, and D. C. Wunsch
  30th IEEE International Conference on Image Processing (ICIP), October 2023.
• [Pattern'23] YOGA: Deep Object Detection in the Wild with Lightweight Feature Learning and Multiscale Attention [arXiv (new)] [DOI: 10.1016/j.patcog.2023.109451]
  R. Sunkara and T. Luo
  Pattern Recognition, vol. 139, pp. 109451, July 2023.
• [EDGE'23] LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing [arXiv] [DOI: 10.1109/EDGE60047.2023.00032]
  R. Das and T. Luo
  IEEE International Conference on Edge Computing (EDGE), July 2023, pp. 150-158.
  Acceptance rate (full paper): 17%
• [MDM'23] One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes [arXiv] [DOI: 10.1109/MDM58254.2023.00020]
  M. Elmahallawy and T. Luo
  24th IEEE International Conference on Mobile Data Management (MDM), July 2023, pp. 45-54.
• [PAKDD'23] TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks [pdf] [arXiv] [DOI: 10.1007/978-3-031-33374-3_4]
  S.S. Saravanan, T. Luo, and M.V. Ngo
  27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 2023, pp. 39–54.
  Acceptance rate: 16.5%
• [ICC'23] Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling
  M. Elmahallawy and T. Luo
  IEEE International Conference on Communications (ICC), May 2023.
• [Preprint] Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
  J. Du and T. Luo
  arXiv preprint 2304.10018, Apr 2023.
• [ARAI'23] Black-Box Attack using Adversarial Examples: A New Method of Improving Transferability [DOI: 10.1142/S2811032322500059]
  T. Wu, T. Luo, and D. C. Wunsch
  World Scientific Annual Review of Artificial Intelligence, vol. 1, pp. 2250005, February 2023.
  Publisher has given free access to view the full text (click above) throughout 2023 (only).
• [BigData'22] AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms [arXiv] [DOI: 10.1109/BigData55660.2022.10021101]
  M. Elmahallawy and T. Luo
  IEEE International Conference on Big Data, December 2022, pp. 5478-5487.
  Acceptance rate (full paper): 19.2%
• [SSCI'22] Learning Deep Representations via Contrastive Learning for Instance Retrieval [arXiv] [DOI: 10.1109/SSCI51031.2022.10022224]
  T. Wu, T. Luo, and D. C. Wunsch
  IEEE Symposium Series On Computational Intelligence (SSCI), December 2022, pp. 1501-1506.
• [WCSP'22] FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs [arXiv] [slides] [DOI: 10.1109/WCSP55476.2022.10039157]
  M. Elmahallawy and T. Luo
  14th International Conference on Wireless Communications and Signal Processing, November 2022, pp. 888-893.
• [ESORICS'22] Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning [pdf] [arXiv] [video] [code] [DOI: 10.1007/978-3-031-17143-7_22]
  A. Gupta, T. Luo, M. V. Ngo, and S. K. Das
  The 27th European Symposium on Research in Computer Security (ESORICS), September 2022, pp. 445-465.
  Acceptance rate: 19%
• [ECML PKDD'22] No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects [pdf] [arXiv] [video] [code] [DOI: 10.1007/978-3-031-26409-2_27]
  R. Sunkara and T. Luo
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), September 2022, pp. 443-459.
  Acceptance rate: 26% (242/932)
• [TIOT'21] Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [video] [DOI: 10.1145/3480172]
  M.V. Ngo, T. Luo, and T.Q. Quek
  ACM Transactions on Internet of Things (TIOT). Vol. 3, No. 1, pp. 1-23, October 2021.
• [WiOpt'21] Data-Free Evaluation of User Contributions in Federated Learning [DOI: 10.23919/WiOpt52861.2021.9589136]
  H. Lv, Z. Zheng, T. Luo, F. Wu, S. Tang, L. Hua, R. Jia and C. Lv
  International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), October 2021.
• [AAMAS'21] A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence
  L. Zeynalvand, T. Luo, E. Andrejczuk, D. Niyato, S.G. Teo, and J. Zhang
  International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2021.
  Acceptance rate: 24.8% (152/612)
• [Globecom'20] Coordinated Container Migration and Base Station Handover in Mobile Edge Computing [video] [code]
  M.V. Ngo, T. Luo, H.T. Hoang, and T. Quek
  IEEE Global Communications Conference (GLOBECOM), December 2020.
• [KDD'20w] 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), Workshop on AIoT, August 2020.
• [ICDCS'20] Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [DOI: 10.1109/ICDCS47774.2020.00191]
  M.V. Ngo, T. Luo, H. Chaouchi, and T. Quek
  IEEE International Conference on Distributed Computing Systems (ICDCS), December 2020, pp. 1227-1230.
• [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 [arXiv]
  L. Zeynalvand, T. Luo, and J. Zhang
  34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, Feb 2020, pp. 7317-7324.
  Acceptance rate: 20%
• [AAAI'20] Mechanism design with predicted task revenue for bike sharing systems [arXiv]
  H. Lv, C. Zhang, Z. Zheng, T. Luo, F. Wu and G. Chen
  34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, Feb 2020, pp. 2144-2151.
  Acceptance rate: 20%
• [AAAI'20w] Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [arXiv]
  M.V. Ngo, H. Chaouchi, T. Luo, and T. Quek
  34th AAAI Conference on Artificial Intelligence (AAAI), Workshop on AIoT, 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), Vol. 23, No. 1, pp. 6:1-25, February 2020.

------------- Partial list of publications before 2020 (==> See full list with diagrams and pictures) ------------

• [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 that seeks a validating crowd to ratify the quality of sensor data contributed by the a contributing crowd. Using a weighted ramdom oversampling (WRoS) technique and a PATOP² algorithm which makes an exploration-exploitation tradeoff, the CV approach offers a unified solution to two disparate (and important) needs: reinforce obscure truth and discover hidden truth. (See illustration.)
• [ICC'18] Distributed anomaly detection using autoencoder neural networks in WSN for IoT [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) for distributed anomaly detection. It contradicts the general belief that deep learning is not suitable for WSN, by (1) making deep learning extremely shallow and (2) dividing and allocating computation load to sensors and the IoT cloud with a two-part algorithm. (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 adopted auctions in incentive mechanism design so far have been winner-pay auctions (WPA), in which only winners (who outbid others and will therefore receive reward) need to pay their bids. By contrast, all-pay auctions (APA) require all the participants regardless of who win the auction to pay their bids. This apparently sounds unreasonable, but when applied to crowdsourcing, it makes perfect sense and gains two important advantages over WPA. (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 abilities, costs, etc.), the daunting challenge of modeling and analyzing such scenarios has restricted researchers to choosing 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. Not only does it achieve better performance, but it also uncovers a counter-intuitive and interesting property called strategy autonomy, in which the asymmetric equilibrium collapses into a symmetric one.
• [TMC'16] Competition-based participant recruitment for delay-sensitive crowdsourcing applications in D2D networks [DOI: 10.1109/TMC.2016.2524590]
  Y. Han, T. Luo, D. Li, and H. Wu
  IEEE Transactions on Mobile Computing (TMC), vol. 15, no. 12, pp. 2987-2999, December 2016.
  
  We tackle the challenge of large delay incurred in Device-to-Device (D2D) networks, by designing two distributed approaches that closely approximate a centralized, dynamic programming algorithm. One is a divide-and-conquer method with Voronoi cells, and the other is a task splitting and delegation scheme.
• [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.
  Acceptance rate: 19% (316 out of 1640)
  
  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 a better alternative, on the basis that it is less competitive and more conducive to larger participation.
• [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.
  
  Characterizing QoS for crowdsensing is a challenge, and this work proposes a new metric called Quality of Contributed Service (QCS) to aggregate individual quality of contributions by taking information quality and time sensitivity into account. We analyze this metric 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.
  Acceptance rate: 19% (319 out of 1650)
  
  All-pay auction + adaptive prize/reward (see the short abstract of TIST'16 or an illustration).

Systems

Besides theory, I am also passionate about developing real systems. I led a team to have developed the following mobile crowdsourcing / crowdsensing applications and made them freely downloadable at Apple Store and Google Play between 2014-2019. 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. [DOI: 10.1109/SAHCN.2014.6990404]
  
  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. [DOI: 10.1109/MASS.2014.32]
  
  WiFi-Scout is a application that provides a city's WiFi map via crowdsensing; the map contains WiFi locations, quality, and login security mode.

People

• PhD students
  Xiaohui Chen (Cornell University)
  Jiadi Du (Rutgers University)
  Manoj Twarakavi (University of Texas at Arlington)
  Tao Wu (Huazhong University of Science and Technology)
  Ronit Das (Truman State University)
  Mohamed Elmahallawy (Tennessee Technological University)
  Nuzaer Omar (Chittagong University of Enginnering and Technology)
  Yasmine Mustafa (German University In Cairo)
  Manish Anand Yadav (Chongqing University of Posts and Telecommunications)
• Alumni students
  Raja Sunkara (Master's 2023; B.Tech. Indian Institute of Technology Madras)
  Shyam Sundar Saravanan (Master's 2023; B.E. Anna University)
Mao V. Ngo (PhD received 2020 from Singapore University of Technology and Design)

Thesis: Mobile Edge Computing with Machine Learning: From IoT Devices to Edge Servers, and to a Hierarchy
Leonit Zeynalvand (PhD received 2021 from Nanyang Technological University)

Thesis: A Holistic Approach to Trust and Reputation Management in Big Data
• Visiting students
  Qing Liu (PhD student visited 2014, National University of Singapore)
  
  Research Topic: Game theory and mechanism design
  Chunchun Wu (Master student visited 2014, Shanghai Jiao Tong University)
  
  Research Topic: Machine learning and reputation systems

Professional Services

• Grant Panelist / Reviewer:
  U.S. Department of Energy (DOE) Grant Proposal Reviewer: 2022
  National Science Foundation (NSF) Grant Panelist: Feb-Mar 2021
  Euregio Science Fund (EGTC) Grant Proposal Reviewer: Apr-Jun 2021
• Journal Editorial Board:
  Annual Review of Artificial Intelligence: Associate Editor, 2021-present.
  Pervasive and Mobile Computing: Area Editor, 2021-present. ⇗
  Ad Hoc Networks: Area Editor, 2019-present. ⇗
  Sensors: Guest Editor (Localization and Tracking for Internet of Things), 2022-2023. Submission deadline: 30 April 2023.
  Wireless Communications and Mobile Computing: Academic Editor, 2018-present. ⇗
  Telecom: Editor, 2018-present.
  Mobile Information Systems: Guest Editor (Big Data Management and Analytics for Mobile Crowd Sensing), 2015-2016.
  Journal of Sensor and Actuator Networks: Guest Editor (MAC Protocols in Wireless Sensor Networks), 2015-2016.
• Journal Advisory Board:
  Sci: 2018-present.
• Conference Program Chair:
  IEEE PerCom CASPer 2016
  ACM ComNet-IoT 2016
  IEEE ISSNIP PSC Symposium 2014
• Conference Publication Chair:
  IEEE PerCom 2023
  IEEE PerCom 2024
• Conference Session Chair:
  IEEE ICC 2023: Learning & Networks
  PAKDD 2023: Federated Learning
  IEEE INFOCOM 2022: Machine Learning
  IEEE INFOCOM 2021: Attack and Anomaly Detection
  IEEE INFOCOM 2020: Internet of Things
• Conference Program Committee:
  2024: AAAI | ICC (Machine Learning for Communications and Networking Track)
  2023: WoWMoM | ICC | WCNC
  2022: INFOCOM | WoWMoM | ICC | WCNC
  2021: INFOCOM | AAAI | WoWMoM | ICC | WCNC | MASS | PST
  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 Transactions on Neural Networks and Learning Systems (TNNLS) (2023--present)
  IEEE Transactions on Knowledge and Data Engineering (TKDE) (2018--present)
  IEEE Transactions on Affective Computing (TAFFC) (2023--present)
  IEEE Transactions on Dependable and Secure Computing (TDSC) (2022--present)
  IEEE Transactions on Mobile Computing (TMC) (2009--present)
  IEEE Transactions on Parallel and Distributed Systems (TPDS) (2020--2021)
  IEEE Transactions on Wireless Communications (TWC) (2013--2014)
  IEEE Transactions on Vehicular Technology (TVT) (2010--2014)
  IEEE Transactions on Cognitive Communications and Networking (TCCN) (2016--2018)
  IEEE Transactions on Network and Service Management (TNSM) (2017--2019)
  IEEE Internet of Things Journal (IOT-J) (2019--present)
  IEEE Journal on Selected Areas in Communications (JSAC) (2008--)
  IEEE Signal Processing Magazine (2021)
  IEEE Network (2015--2019)
  IEEE Computer (2018--2019)
  IEEE Pervasive Computing (2017--2019)
  IEEE Journal on Selected Areas in Communications (JSAC) (2008--2009)
  IEEE/ACM Transactions on Networking (ToN) (2010--2012; 2023-present)
  ACM Transactions on Sensor Networks (TOSN) (2021--present)
  ACM Transactions on Privacy and Security (TOPS) (2021--present)
  ACM Transactions on Intelligent Systems and Technology (TIST) (2021--present)
  ACM Transactions on Internet Technology (TOIT) (2018--present)
  ACM Mobile Computing and Communications Review (MC2R) (2009--2010)
  ACM/SAGE - Collective Intelligence (2021)
  Elsevier - Pervasive and Mobile Computing (PMC) (2013--2019; Editor thereafter) [Outstanding Reviewer 2016]
  Elsevier - Ad Hoc Networks (ADHOC) (2013--2019; Editor thereafter)
  Elsevier - Computer Networks (COMNET) (2008--2019)
  Elsevier - Computer Communications (COMCOM) (2017--2019)
  Elsevier - Journal of Parallel and Distributed Computing (JPDC) (2018--2021)
  Elsevier - Information Systems (IS) (2018--2019)
  Elsevier - Future Generation Computer Systems (FGCS) (2018--2020)
  Elsevier - Digital Communications and Networks (DCAN) (2018--2019)
  MDPI - Artificial Intelligence (AI) (2021)
  MDPI - Applied Sciences (2021)
  MDPI - Healthcare (2021)
  MDPI - Remote Sensing (2020)
  MDPI - Sensors (2016--present)
  Wiley - Wireless Communications and Mobile Computing (WCMC) (2018--2021)
  Wiley - International Journal of Network Management (NEM) (2020)
  Wiley - Discrete Dynamics in Nature and Society (2019)
  Springer Nature - Peer-to-Peer Networking and Applications (PPNA) (2020)
  IET Intelligent Transport Systems (ITS) (2018)
  IEICE Transactions on Fundamentals (2014)
• Conference Reviewer (besides TPC):
  ICNP 2021, 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: compiled by Ernst, Kung, Xie, Leone.
• Conference acceptance rates and participation statistics: Artificial Intelligence | Security | Networking | Theoretical Computer Science
• CORE Ranking for Computer Science Conferences
CORE Ranking is a rigorous 8-level assessment system of major conferences in Computer Science.
The ranking for Computer Science Journals has been discontinued since 2020 but is still a useful reference.
• Top conferences: Machine Learning & Artificial Intelligence | Computer Networks & Communications
• Top journals: Machine Learning & Artificial Intelligence | Computer Networks & Communications
• My DBLP (BibTex downloadable over there using the 2nd icon)
• My Google Scholar