We aim to create a communication network among man-made implantable devices and natural cells by coupling sensing, actuation and computing processes to sense, process, control and exchange health information of the body in real time to defend it against infections. We focus on the detection of infections inside the body based on the detection of molecular communication among the infectious bacteria. Novel nanomaterials such as graphene and its derivatives have made it possible to fabricate tiny sensors, i.e., bionanosensors (BNSs), which can detect minute changes in physical variables like pressure, temperature and concentrations of biological molecules. To be able to detect diseased cells in the human body, hundreds of BNSs are foreseen to float through the cardiovascular system to check for anomalies and report them to the outside world. A major concern in this field is localization of body regions where anomalies are detected. Realizing such a localization system is challenging due to the highly dynamic environment, the extremely constrained resources of the sensors and the difficulty of wireless communication in human tissue. The high mobility and limited communication range of the sensors pose strict constraints on localization approaches.
Our proposed system consists of BNSs floating in the cardiovascular system and anchor nodes attached to skin constantly transmitting radiofrequency signals and serving as base stations and gateways to enable access to the measurements of the BNSs. Rather than continuously tracking the highly dynamic sensors, we propose to record the location information from the inertial measurement unit of BSNs with accelerometers and gyroscopes along with the sensor measurements every time the sensor encounters an anomaly. This means, whenever the sensors relay their measurements to anchors, the locations of the anomalies will be transferred as well. Whenever in the vicinity of anchors, the BSNs transfer the location that they recorded for the anomaly to anchor nodes with terahertz backscattering for resource-efficient wireless communication. We simulated the propagation of BSNs in the cardiovascular system and evaluated our localization approach which uses Kalman Filter to compute the position of a BSN using the position information coming from anchors and its inertial measurement unit. It was shown that this terahertz link works well when anchors are placed in parts of the body where the skin is thinner, and the localization approach can provide the location of the anomaly with a small margin of error.
Our ability to miniaturize sensors and electronics is unprecedented, and our understanding of biological systems enables fine-grained manipulation of cellular behavior down to the molecular level. This project will leverage the PIs’ unique combination of expertise at the crossroads of biology, bio-sensor interface design, and wireless communications, to address the challenges for human health applications such as prediction and prevention of infections.
CLIPEUS (Shield in Latin) aims at creating a communications network among man-made implantable devices and the natural cells inside the body under the novel communication paradigm, called Internet of Bio-NanoThings, where sensing, actuation, and computing processes are tightly coupled to pervasively, perpetually, and precisely sense, process, control, and exchange health information of the body in real time to defend it against infections.
CLIPEUS focuses on the detection of infections inside the body based on the detection of the molecular communication among the infectious bacteria by man-made bio-nanothings, called GLADIO (Sword in Latin), consisting of bionanosensors for detection, low power electronics for processing and antenna for near field communication to transfer the data through the tissues to outside of the body. Due to the power limitations of GLADIO, the inherent noise of biological processes, and the non-deterministic response of patients to infections, the collected data will be sparse and noisy. Novel machine learning techniques will be developed to interpret this data for future personalized medicine applications. The system will be extensively tested and ameliorated by phantom experiments as a first step before in vivo experiments.
The project will recruit and train 2 PhD students and 2 Postdocs and has drawn up a comprehensive, multifaceted plan to disseminate, communicate and exploit the project results.