MIT Unit Affiliation:
Lab Affiliation(s):
Field Intelligence Lab; AutoID Lab; Lab for Manufacturing and Productivity
Post Doc Sponsor / Advisor:
Sanjay Sarma
Areas of Expertise:
  • Internet of Things
  • Design
  • Automotive
Date PhD Completed:
July, 2016
Expected End Date of Post Doctoral Position:
July 28, 2016

Joshua Siegel

  • Post Doctoral

MIT Unit Affiliation: 

  • Mechanical Engineering

Lab Affiliation(s): 

Field Intelligence Lab; AutoID Lab; Lab for Manufacturing and Productivity

Post Doc Sponsor / Advisor: 

Sanjay Sarma

Date PhD Completed: 

Jul, 2016

Top 3 Areas of Expertise: 

Internet of Things
Design
Automotive

Expected End Date of Post Doctoral Position: 

July 28, 2016

Research Projects: 

Secure and efficient architectures for IoT; pervasive sensing for vehicle failure prediction; data-informed design.

Thesis Title: 

Data Proxies, the Cognitive Layer, and Application Locality: Enablers of Cloud-Connected Vehicles and Next-Generation Internet of Things

Thesis Abstract: 

Intelligent and Connected Vehicles reduce cost, improve safety, and enhance comfort relative to isolated vehicles. This ability for cars to sense, infer, and act facilitates data-driven improvements in occupant experience and vehicle design. This thesis explores informed individual vehicle improvements and proposes a secure and e cient architecture supporting connected vehicle applications.

Applying On-Board Diagnostic and smartphone data, I built a suite of prognostic applications. Engine coolant temperature data supports inference of oil viscosity and remaining life. A linear SVM using Fourier, Wavelet, and Mel Cepstrum audio features provides 99% accurate engine mis re detection. PCA-transformed Fourier acceleration features and GPS data inform decision trees attaining 91% wheel imbalance and 80% tire pressure and tread depth classi cation accuracy. These applications demonstrate the ability for local vehicle and peripheral device data to proactively improve individual vehicle reliability and performance. Connectivity facilitates crowdsourced data to further improve current vehicles and future designs.

Exploring vehicular connectivity, I consider data timeliness, availability and band- width cost in the context of an e ciency-improving idle time predictor. This predic- tor uses contextual information to eliminate short idle shuto s in Automatic Engine Start/Stop systems, minimizing driver annoyance and improving compliance.

These applications reveal an opportunity to address excess resource consumption and system insecurity in Connected Vehicles and other constrained devices.

I introduce a secure and e cient model-based Internet of Things (IoT) architecture consisting of a “Data Proxy” utilizing a Cloud-run estimator to mirror an object with limited sensor input. The use of digital duplicates abstracts physical from digital objects, allowing the use of a mediating “Cognitive Layer” consisting of rewall and supervisory elements. These “Cognitive” elements apply the system model to monitor system evolution and simulate the impact of commands against known and learned limits.

Top 5 Awards and honors (name of award, date received): 

Lemelson-MIT National Collegiate Student Prize (2015)
MassIT Government Innovation Prize (2014)
MIT/ISN Soldier Design Competition Boeing Prize (2008)
BMW-EURECOM "Highly Autonomous Driving in the Internet of Things" Ideation Award (2014)
IoT/M2M/Cloud Hero of the Year in the Innovation World Cup (2014/2015)

5 Recent Papers: 

J. Siegel, S. Kumar, I. Ehrenberg, S. Sarma, “Engine Misfire Detection With Pervasive Mobile Audio,” ECML KDD 2016. In LNCS, 2016.
 

J. Siegel, R. Bhattacharyya, A. Deshpande, S. Sarma. “Smartphone-Based Vehicular Tire Pressure and Condition Monitoring.” Proceedings of SAI Intellisys 2016.
 

J. Siegel, R. Bhattacharyya, A. Deshpande, S. Sarma. “Smartphone-Based Wheel Imbalance Detection.” Proceedings of Dynamic Systems and Controls Conference 2015.
 

J. Siegel, R. Bhattacharyya, A. Deshpande, S. Sarma. “Vehicular Engine Oil Service Life Characterization Using On-Board Diagnostic (OBD) Sensor Data.” Proceedings of IEEE Sensors 2014.
 

E. Wilhelm, J. Siegel, S. Mayer, L. Sadamori, S. Dsouza, C. Chau, S. Sarma. “CloudThink: A Scalable Secure Platform for Mirroring Transportation Systems in the Cloud” Transport 30 (3). 2015.

Contact Information:
77 Massachusetts Avenue
Room 35-205
Cambridge
MA
02139