Research Interests

Internet of Things (IoT), Data Privacy, Information Security, Network Security

Publications

1. E. M. Rochester and K. Barker, “Designing Infrastructure-Aware Privacy Assistants for the IoT,” Technical Report, Department of Computer Science, University of Calgary, 2025, doi: 10.11575/PRISM/50020. 

2. E. M. Rochester, A. M. Yousuf, B. Ousat and M. Ghaderi, "Lightweight Carrier Sensing in LoRa: Implementation and Performance Evaluation," ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9149103. 

3. E. M. Rochester, "Mountain Pine Beetle Detection using IoT," (Master's thesis), 2020, University of Calgary, Calgary, AB.

4. E. M. Rochester, J. Ma, B. Lee and M. Ghaderi, "Mountain Pine Beetle Monitoring with IoT," 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019, pp. 513-518, doi: 10.1109/WF-IoT.2019.8767291.

5. A. M. Yousuf, E. M. Rochester, B. Ousat and M. Ghaderi, "Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things," 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 2018, pp. 1-10, doi: 10.1109/IWQoS.2018.8624157.

6. A. M. Yousuf, E. M. Rochester and M. Ghaderi, "A low-cost LoRaWAN testbed for IoT: Implementation and measurements," 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), 2018, pp. 361-366, doi: 10.1109/WF-IoT.2018.8355180.

7. N. Levin, E. Kamolins, A. Gusakov, V. Pugacev, "Undercar Electrical Generator for Railway Passenger Cars: Improvement of Efficiency / Dzelzcela Pasazieru Zemvagona Elektriska Generatora Efektivitates Uzlabosana," Latvian Journal of Physics and Technical Sciences, 2013, vol. 50, (2), pp. 23, doi: 10.2478/lpts-2013-0009.

8. N. Levin, E. Kamolins, V. Pugachev and A. Gusakov, "Synchronous generator with two-channel excitation for power supply of railway passenger cars," 2012 Electric Power Quality and Supply Reliability, 2012, pp. 1-6, doi: 10.1109/PQ.2012.6256192.

Privacy Assistants for Internet of Things (Demo)

Modern Internet of Things (IoT) devices often collect sensitive data, yet users lack interfaces and fine-grained control over privacy decisions in IoT environments.

PADOME (Privacy Assistant with Distributed Opponent Modeling and User Elicitation; also meaning council in Latvian) is a privacy assistant that enables dynamic negotiation of privacy “agreements” between user devices and IoT devices, factoring in user preferences, context, and device constraints. PADOME optimizes the negotiation through:

• User elicitation: capturing user preferences, constraints, and trade-offs in a structured way.

• PA elicitation for opponent modeling: drawing from automated negotiation literature, this involves building models of the counterpart’s (e.g., IoT device's) likely strategies, preferences, and concession patterns. While this is central to PADOME’s design, the current demo does not yet implement opponent modeling elicitation, focusing instead on user-side elicitation, preference setup, and opponent modeling.

This demo showcases a prototype of my DPM 2025 submission (preprints available here). It illustrates PADOME, with the prototype implemented and demonstrated on iOS (iPhone 12 mini), negotiating with an Arduino Nano Sense Rev. 2 as the IoT device. For a higher fidelity view, please see the full-quality demo video here.