NSF CNS Award #1816387 NETS:SMALL: Functional Fabrics for the Internet of Things
PI: Kapil R. Dandekar
Co-PIs: Genevieve Dion, William Mongan, Steven Weber
Md Abu Saleh Tajin
Functional fabrics are textiles designed to perform a wide variety of functions with applications ranging from medicine and sports to realizing the “soft” side (e.g., clothing, drapes, upholstery) of the Internet of Things (IoT). Coupled with pervasive access to smart phones and wireless Internet, they are enabling exciting new application domains, notably in smart homes and wearable health systems. Whereas the majority of these devices today are similar in form to a wrist watch, the next generation of wearables for the Internet of Things are likely to be fully integrated into textile turning the fabric into garment and textile devices.
The functional fabrics that we envision will be ubiquitous and unobtrusive. If designed properly, the user should not even realize that they are surrounded by this technology. The design of functional fabrics will be fully flexible, scalable, and customizable to individuals. The applications they enable will vary widely between what will one day be seen as: mundane (e.g., smart drapes with sensors that measure and regulate temperature within a room), convenient (e.g., smart laundry that will help prevent bright colored clothing from being washed with whites), personal / professional productivity (e.g., convergence of functional fabrics with voice and data transceivers for social / work applications), as well as life-changing/saving (e.g., biomedical functional fabrics with textile based sensors). The use of passive radio frequency identification (RFID), in particular, is an enabling technology for high density Internet of Things deployments since each functional fabric device would not require a battery or cumbersome transceiver electronics.
Major Goal #1: Cognitive Passive RFID - We will i.) design a testbed for functional fabric, passive RFID IoT systems using a network of software defined radio (SDR) based interrogators capable of serving high densities of functional fabrics representative of IoT deployments, ii.) develop localization and processing algorithms using collaborative interrogator beamforming techniques, and iii.) consider security and privacy issues in the data collected by our RFID IoT system.
Major Goal #2: Interrogator Message Concentration Protocol - We consider the challenge in delivering messages from high density IoT device deployments gathered by the RFID interrogators (received in turn from the RFID tags) to the Internet or cloud.
Major Goal #3: Architectures and Protocols for Wearable IoT Sensor Networks - We will develop architectures and protocols to utilize passive, high-density, heterogeneous sensor systems integrated in functional fabrics to serve, access and be accessed by an IoT vertical.
Broader impacts of the proposed project included the mentoring of graduate and undergraduate students. This mentoring includes use of the new Drexel Vertically Integrated Projects (VIP) program involving undergraduate students in research. We are developing augmented reality visualization tools for IoT applications. Finally, we also consider technology commercialization in coordination with the Pennsylvania Fabric Discovery Center as part of the Advanced Functional Fabrics of America (AFFOA) institute.
During the reporting period, our activities considered several areas pertaining to functional fabrics for IoT. We considered the construction and characterization of functional fabric devices that contained wearable antennas, and used electromagnetic techniques to evaluate their efficiency in realistic on-body field tests, along with studying how they degrade when exposed to human sweat conditions. We also considered how functional fabrics can be used for medical IoT systems, specifically through building of a database that can be used to store the results of sensor readings. We specifically considered the creation of a data storage and processing network for medical functional fabric IoT devices focused on applications including respiration and activity classification. The storage and signal processing of data for functional fabric devices raises security concerns, and we consider the development of physical layer security approaches for light-weight IoT devices with limited processing capability. We are also conducting research activities at Drexel to create a heterogeneous IoT testbed using data from RFID but also from LoRa based transceivers.
Acharya, Sayandeep and Mongan, William M. and Rasheed, Ilhaan and Liu, Yuqiao and Anday, Endla and Dion, Genevieve and Fontecchio, Adam and Kurzweg, Timothy and Dandekar, Kapil R. (2019). Ensemble Learning Approach via Kalman Filtering for a Passive Wearable Respiratory Monitor. 23. (3). IEEE Journal of Biomedical and Health Informatics, 23. 1022 to 1031. Published. 2168-2194. 10.1109/JBHI.2018.2857924.
Chacko, James and Juretus, Kyle and Jacovic, Marko and Sahin, Cem and Kandasamy, Nagarajan and Savidis, Ioannis and Dandekar, Kapil R. (2019). Securing Wireless Communication via Hardware-Based Packet Obfuscation. Journal of Hardware and Systems Security. Published. 2509-3428. 10.1007/s41635-019-00070-0.
Gentry, Austin and Mongan, William and Lee, Brent and Montgomery, Owen and Dandekar, Kapil (2019). Activity Segmentation Using Wearable Sensors for DVT/PE Risk Detection. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 477 to 483. Published. 10.1109/COMPSAC.2019.10252.
Jacovic, Marko and Kraus, Martin and Mainland, Geoffrey and Dandekar, Kapil R. (2019). Evaluation of Physical Layer Secret Key Generation for IoT Devices. 2019 IEEE 20th Wireless and Microwave Technology Conference (WAMICON). 1 to 6. Published. 10.1109/WAMICON.2019.8765465.
O’Neill, Patrick and Mongan, William and Ross, Robert and Acharya, Sayandeep and Fontecchio, Adam and Dandekar, Kapil R. (2019). An Adaptive Search Algorithm for Detecting Respiratory Artifacts Using a Wireless Passive Wearable Device. 2019 IEEE Signal Processing in Medicine and Biology Symposium. Published.