@article{zavala_murukannaiah_poosamani_finin_joshi_rhee_singh_2015, title={Platys: From Position to Place-Oriented Mobile Computing}, volume={36}, ISSN={["0738-4602"]}, url={https://publons.com/publon/21294393/}, DOI={10.1609/aimag.v36i2.2584}, abstractNote={The Platys project focuses on developing a high‐level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user's actions and interactions in addition to the physical location where it occurs. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and, thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place‐oriented mobile computing vision are representing places, recognizing places, and engineering place‐aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users' devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.}, number={2}, journal={AI MAGAZINE}, author={Zavala, Laura and Murukannaiah, Pradeep K. and Poosamani, Nithyananthan and Finin, Tim and Joshi, Anupam and Rhee, Injong and Singh, Munindar P.}, year={2015}, pages={50–62} }
 @inproceedings{poosamani_rhee_2015, title={Towards a practical indoor location matching system using 4G LTE PHY layer information}, DOI={10.1109/percomw.2015.7134048}, abstractNote={Predicting the location of a user in indoor settings in a practical and energy-efficient manner is (still) a very non-trivial task. The latest challenge in indoor localization is not to design specialized sensors but to design and implement practical data fusion methods using the already available technologies. Current state-of-the-art indoor localization techniques utilize Wi-Fi and a variety of sensors inside smart phones to predict user location. Some also require site-specific input such as indoor floor plans or the location of Wi-Fi access points. In this paper, we propose to use physical (PHY) layer information from 4G cellular network signals such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) to logically predict user location. Since the cellular signals are received by the smart phones at no additional cost, our methodology is very energy-efficient. We implement a prototype system in Android and evaluated it over 60 indoor locations. The prediction accuracy ranged up to 91% with an average localization error of less than 2.3m for any combination of 4G PHY layer information. The results show promise for improvements in current indoor localization systems using cellular signals.}, booktitle={2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM workshops)}, author={Poosamani, N. and Rhee, I.}, year={2015}, pages={284–287} }