Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication.
Wearable flexible sensors have experienced vigorous development and advancement in the past decade because of their attractive characteristics such as flexibility, stretchability, light weight, and function diversity for widespread applications including personalized health care, soft robotics, prosthetics, and human-machine interfaces (HMIs) (1–5). In the era of the fifth generation (5G) mobile network and the Internet of Things (IoT), numerous sensors are expected to be connected wirelessly (6, 7). These sensors serve as sensor nodes in a communication framework where the sensors and the cloud exchange data at an ultrafast data rate (8, 9). Following this trend, a body area sensor network has been proposed to wirelessly connect wearable transmitters, receivers, and sensors with various functionalities to comprehensively monitor human health conditions (10, 11). The traditional and dominant wearable sensors are based on the resistive mechanism and the capacitive mechanism (12, 13). The need for external power supplies limits their widespread deployment in the IoT because of the power consumption issue and the battery replacement issue. The triboelectric nanogenerator (TENG), since its invention in 2012, has been explored as energy harvesters to drive wearable sensors due to its high output characteristics and wide adaptability (14–18). Benefiting from its versatile configurations and self-powered characteristics, TENG has been further investigated and deployed as self-powered sensors for motion monitoring and health care monitoring (19–23). TENG-based sensors have been integrated into diversified clothes to enable smart socks, belts, and wrist bands for gait analysis, driving status monitoring, and arterial pulse detection, respectively (24–27).
In addition to monitoring functions, TENG-based sensors can also provide promising control functions when configured into HMIs (28–30). Wearable HMIs are in burgeoning demand as an advanced solution to achieve human-machine interactions by virtue of their human state tracking capability (31, 32). Versatile working mechanisms and structural configurations of TENGs have been used to implement different types of HMIs, such as touchpads, gloves, glasses, etc. (33–35). To enhance the control capability, various coding methods for TENG-based HMIs have been developed (36–38).
Shi and Lee (38) developed a highly scalable and wearable control interface by encoding multidigit binary information into a spider net–shaped electrode layout, achieving a multidirectional three-dimensional (3D) control system with only one single electrode. Besides improved control capability, the TENG-based HMIs operate in a self-powered manner without the need for external power supplies, making them ideal sensor nodes in the communication framework of IoT systems. However, these HMIs face security issues in communication. Using current TENG-based HMIs, unauthorized users can also send commands and control designated entities. Potentially, TENG itself can address the security issue by incorporating the biometric identification function into HMIs (39, 40). Recently, the use of TENG for biometric identification has been investigated. Deep learning (DL) analytics as a technique in artificial intelligence has been used to enhance the data analysis of TENG-based sensor signals to achieve biometric identification (41–44). Wu et al. (41) proposed a keystroke dynamics–based authentication system with TENG-based sensors to recognize the identity of users through their unique typing habits, which could potentially push cybersecurity to a new level without the concern of leaking passwords. Shi et al. (42) developed a smart floor monitoring system that can detect the walking position and gait-based identity of a user simultaneously by integrating DL analytics in TENG-based floor mats, enabling secure smart homes. Moving forward, it is desired to combine the biometric identification function and the control function into a single TENG-based sensor where control is only allowed after authority check, so as to enhance the system security.
To function as sensor nodes in IoT systems, TENG-based sensors should be able to transmit their signals to the cloud efficiently and robustly (45). Traditionally, signals from TENG-based sensors are captured by a microcontroller unit (MCU); then, the MCU controls a transmitter to send out the signals (46–48). This method involves many analog-to-digital and digital-to-analog conversions (ADC and DAC) and several communication interfaces, limiting the transmission efficiency and increasing the power consumption on the sensor end. To address this issue, a method based on electromagnetic coupling has been proposed (49, 50). Two closed electrical circuits are placed in proximity, where one hosts the TENG-based sensor and the other hosts the receiver circuit. Upon human interaction, the current flow in the TENG circuit induces a current in the readout circuit via electromagnetic induction. Although the transmission efficiency is improved by removing the intermediate MCU, the transmission distance is limited because of the weak coupling between two circuits. Moreover, the electromagnetic coupling is vulnerable to electromagnetic interference (EMI), limiting their robustness for applications in complex IoT systems where strong EMI occurs because of the presence of numerous electrical components. In this regard, transmitting the TENG signals in the optical communication infrastructure could be advantageous. The optical communication is immune to EMI and can transmit ultrahigh-speed signals with low attenuation and low dispersion over a long distance. Nevertheless, it is challenging to load signals generated by TENG-based sensors into the optical domain efficiently and directly.
Combining triboelectric technology with nanophotonics technology could be a promising solution. Recently, the synergistic effect between triboelectric technology and aluminum nitride (AlN) photonics has been investigated (51, 52). Two main advantages have been proven. On the one hand, thanks to the Pockels effect in AlN photonics (53, 54), the coded control signals generated by TENG-based sensors in the form of high voltage can be loaded into the optical domain by the electro-optic effect without the need for external circuits or power supplies, resulting in optical strings of “ones” and “zeros” that carry the control information (51). On the other hand, when TENG-based sensors are used for monitoring functions, the sensory information can be recorded continuously and accurately because the capacitive nature of AlN photonic devices provides the open-circuit working condition for TENG (52). The availability of the open-circuit working condition can be attributed to the fact that the impedance of AlN photonic devices is several orders higher compared to TENG sensors because of their size difference. Benefiting from the two advantages, optical Morse code transmission and human-machine interaction in the virtual reality/augmented reality (VR/AR) space have been demonstrated using the triboelectric/photonics interface. However, the monitoring and transmission of biometric information have not been investigated using the triboelectric/photonics interface. Furthermore, the previous works did not incorporate the triboelectric/photonic interface into the optical communication infrastructure to exploit its main superior function of transmitting information at ultrahigh data rate. Whether the direct transmission of TENG-based sensor signals in the optical communication infrastructure will disrupt the digital information that is originally propagating in optical fibers remains a severe unknown issue that may hinder practical applications.
Here, we present a biometrics-protected optical communication technology as a low-cost, easy-to-access, and ubiquitous solution for secure communication between users (sensor nodes) and the cloud by leveraging on the DL-enhanced triboelectric/photonic synergistic interface. The synergistic interface is constructed on the basis of the fusion of flexible triboelectric biometric (TEB) scanner and AlN photonics-based biometric-optical information multiplexer (BOIMUX). The TEB scanner is a single-electrode TENG-based sensor that provides both the biometric identification function for secure communication and the control function for human-machine interactions. The system only enables the control function after the user authority is checked by biometric identification assisted with DL. Upon user interactions, enabled by the synergistic effect between triboelectric and nanophotonics, the interface loads biometric information into the optical domain and multiplexes biometric information and digital information that is originally propagating in optical fibers in a self-sustainable manner. The digital signals are sealed in a biometric envelope after multiplexing, thereby eliminating the communication latency and enhancing the complexity of transmitted information. The multiplexed signal in the form of a modulated wave packet is then transmitted efficiently and robustly to the cloud via the optical communication infrastructure. Because of the large frequency difference, low-frequency biometric information does not disrupt high-frequency digital information in the optical domain. In the cloud, the high-frequency digital information and low-frequency biometric information can be separated using fast Fourier transform (FFT) filters. Assisted with DL, biometric identification can be implemented to identify 15 users with around 95% accuracy and 23 users with around 90% accuracy irrespective of the data types of biometric information (electrical, optical, or demultiplexed optical), enabling secure communication between users and the cloud via the triboelectric/photonics interface. Secure exchange of high-speed documents and secure control of smart homes in the VR space are both demonstrated to prove the practicality of the proposed system.
This excerpt was republished from Science Advances under a Creative Commons license to point warfighters and national security professionals to reputable and relevant war studies literature. Read the original article.
The authors are from the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore 117583; and the Center for Intelligent Sensors and MEMS, National University of Singapore, Singapore, Singapore 117608.
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The recent leak of Pentagon documents included the suggestion that China is developing sophisticated cyber attacks for the purpose of disrupting military communication satellites. While this is unconfirmed, it is certainly possible, as many sovereign nations and private companies have considered how to protect from signal interference.
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