Efficient and Adaptive High-quality Data Collection and Processing in Internet of Things

Interdisciplinary Areas: Internet of Things and Cyber Physical Systems, Data/Information/Computation, Smart City, Infrastructure, Transportation

Project Description

In future smart cities, many decision processes in critical infrastructure and emergency management will be based on machine learning techniques. One particular application will be the processing of large datasets of visual images for defect assessment where the data is collected by a swarm of mobile sensing agents (e.g., UAVs). A critical requirement for the success of such assessment processes is the reliable detection, quantification, and localization of defective regions. Furthermore, in such applications, the real-time assessment is often critical so that the swarm can decide regarding the optimum strategy and corresponding actions for effective data collection in unknown. On the other hand, the reliability of the assessments requires data of good quality, since poor data may negatively affect the accuracy of classification and predictions, and consequently, may introduce additional costs and time overhead. The selected candidate will work on developing a framework for real-time, adaptive, and cost-effective collection of high-quality data for autonomous condition assessment of civil infrastructures. To this end, several technologies will be leveraged to devise effective and inexpensive solutions, including: deep neural networks; signal and image processing techniques; mobile image data acquisition agents (mobile phones, drones, robots); static sensor networks; 5G networks and edge computing; crowdsourcing. 

Start Date


Postdoc Qualifications

PhD in Computer Science, Electrical or Computer Engineering, Civil Engineering, or closely related fields with strong background and experience in Computer Vision, Deep Learning, and Machine Learning.  


Elisa Bertino
Samuel D. Conte Professor of Computer Science 
Professor of Electrical and Computer Engineering (courtesy) 
Cyber Space Security Lab (Cyber2Slab)

Mohammad R. Jahanshahi
Assistant Professor of Civil Engineering
Smart Informatix Laboratory


1. Elisa Bertino, Mohammad R. Jahanshahi (2018), “Adaptive and Cost-Effective Collection of High-Quality Data for Critical Infrastructure and Emergency Management in Smart Cities - Framework and Challenges”, J. Data and Information Quality 10(1): 1:1-1:6 (2018).

2. Jongho Won, Seung-Hyun Seo, Elisa Bertino (2017) “Certificateless Cryptographic Protocols for Efficient Drone-Based Smart City Applications”, IEEE Access 5: 3721-3749 (2017)
3. Fu-Chen Chen and Mohammad R. Jahanshahi, (2018), "NB-CNN: Deep learning-based crack detection using convolutional neural network and naive Bayes data fusion," IEEE Transactions on Industrial Electronics, Vol. 65, No. 5, May 2018, 4392-4400, DOI: 10.1109/TIE.2017.2764844.
4. Srinath S. Kumar, Dulcy M. Abraham, Mohammad R. Jahanshahi, Tom Iseley and Justin Starrc, (2018), "Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks," Automation in Construction, Vol. 91, July 2018, 273-283, DOI: 10.1016/j.autcon.2018.03.028.
5. Jongseong Choi, Chul-Min Yeum, Shirley Dyke and Mohammad R. Jahanshahi, (2018), "Computer-aided approach for rapid post-event visual evaluation of a building façade," Sensorsm Vol. 18, No. 9: 3017.