The EPIC Lab’s research on indoor navigation and localization with smartphones develops intelligent, resilient, and energy‑efficient sensing frameworks that enable accurate positioning in complex indoor environments. This work spans a broad spectrum of deep learning and signal‑processing innovations, including Siamese encoders, multi‑head attention networks, graph attention models with real‑time edge construction, stacked autoencoders, CNN‑ and transformer‑based pipelines, and CSI‑driven neural architectures. A major thrust of the research focuses on overcoming real‑world challenges such as device heterogeneity, temporal drift, environmental dynamics, and catastrophic forgetting through class‑ and domain‑incremental learning, federated learning, curriculum adversarial training, and compression‑aware model design. Complementary contributions advance security and interpretability via capsule‑network‑based defenses, logic‑based explainability, and adversarially robust training strategies. Together, this body of work establishes a comprehensive foundation for scalable, secure, and high‑accuracy indoor localization systems that operate reliably on everyday smartphones and embedded mobile platforms.
Selected Publications
A. Singampalli, S. Pasricha, “Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Verona, Italy, Mar 2026.
A. Singampalli, D. Gufran, S. Pasricha, “CIELO: Class-Incremental Continual Learning for Overcoming Catastrophic Forgetting with Smartphone-based Indoor Localization”, IEEE Access, 2025.
A. Singampalli, D. Gufran, S. Pasricha, “DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones”,IEEE Conference on Indoor Positioning and Indoor Navigation (IPIN), 2025.
D. Gufran, S. Pasricha, “Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates”, IEEE Conference on Indoor Positioning and Indoor Navigation (IPIN), 2025. (Best Paper Award)
A. Singampalli, D. Gufran, S. Pasricha, “SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference, Mar 2025.
D. Gufran, S. Pasricha, “GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices”, IEEE/ACM ESWEEK (CASES), Oct 2025. (Best Paper Award Candidate)
D. Gufran, P. Anandathiratha, S. Pasricha, “SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024.
D. Gufran, S. Pasricha, “CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization”, IEEE/ACM DATE, Mar 2024.
Y. Yang, S. Tiku, M. R. Azimi-Sadjadi, S. Pasricha, “MLTL: Manifold-Based Long-Term Learning for Indoor Positioning Using WiFi Fingerprinting”, IEEE World Congress on Computational Intelligence (WCCI), Jun 2024.
S. Tiku, S. Pasricha,”Machine Learning for Indoor Localization and Navigation“, Springer Nature Publishers, 2023
D. Gufran, S. Tiku, S. Pasricha, “STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization”, IEEE Journal of Indoor and Seamless Positioning and Navigation, 2023.
D. Gufran, S. Pasricha, “FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices”, ACM Transactions on Embedded Computing Systems (TECS), 2023.
D. Gufran, S. Tiku, S. Pasricha, “VITAL: Vision Transformer Neural Networks for Smartphone Heterogeneity Resilient and Accurate Indoor Localization”, IEEE/ACM DAC, 2023.
D. Gufran, S. Tiku, S. Pasricha, “SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization”, IEEE Embedded Systems Letters, 2023.
S. Tiku, D. Gufran, S. Pasricha, “Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization”, IEEE Conference on Indoor Positioning and Indoor Navigation (IPIN), 2022 (Best Paper Award Candidate)
L. Wang, S. Pasricha, “A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks”, IEEE Conference on Indoor Positioning and Indoor Navigation (IPIN), 2022
S. Tiku, S. Pasricha, “Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices”, IEEE/ACM Design, Automation and Test in Europe (DATE) Conference and Exhibition, Mar 2022.
L. Wang, S. Tiku, S. Pasricha, “CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning”, IEEE Embedded System Letters, Vol. 14, Iss. 1, Mar 2022.
S. Tiku, P. Kale, S. Pasricha, “QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices”, ACM Transactions on Cyber-Physical Systems (TCPS), Vol. 17, Iss. 4, Oct 2021.
S. Tiku, S. Pasricha, B. Notaros, Q. Han,“A Hidden Markov Model based Smartphone Heterogeneity Resilient Portable Indoor Localization Framework”, Journal of Systems Architecture, Vol 108, Sep 2020.
S. Tiku, S. Pasricha, “Overcoming Security Vulnerabilities in Deep Learning Based Indoor Localization on Mobile Devices”, ACM Transactions on Embedded Computing Systems (TECS), Vol. 18, Iss. 6, Jan 2020
S. Tiku, S. Pasricha, “PortLoc: A Portable Data-driven Indoor Localization Framework for Smartphones”, IEEE Design and Test, Vol. 36, Iss. 5, Oct 2019
S. Tiku, S. Pasricha, B. Notaros, Q. Han, “SHERPA: A Lightweight Smartphone Heterogeneity Resilient Portable Indoor Localization Framework“, IEEE International Conference on Embedded Software and Systems (ICESS), Las Vegas, NV, USA, Jun. 2019
A. Mittal, S. Tiku, S. Pasricha, “Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices,” ACM Great Lakes Symposium on VLSI (GLSVLSI), May 2018. (Best Paper Award)
C. Langlois, S. Tiku, S. Pasricha, “Indoor localization with smartphones”, 6(4), IEEE Consumer Electronics, Oct 2017.
S. Tiku, S. Pasricha, “Energy-Efficient and Robust Middleware Prototyping for Smart Mobile Computing,” IEEE International Symposium on Rapid System Prototyping (RSP), Oct 2017.
S. Pasricha, J. Doppa, K. Chakrabarty, S. Tiku, D. Dauwe, S. Jin, P. Pande, “Data Analytics Enables Energy-Efficiency and Robustness: From Mobile to Manycores, Datacenters, and Networks”, ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Oct 2017.
S. Pasricha, V. Ugave, Q. Han and C. Anderson, “LearnLoc: A Framework for Smart Indoor Localization with Embedded Mobile Devices,” ACM/IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Oct 2015.