Context Recognition of Humans and Objects by Distributed Zero-Energy IoT Devices

Understanding humans and its environment is a key enabler of smart, intelligent applications and services for a future smart society. To deploy such services in our ambient environment, it is expected to fully utilize battery-less and maintenance-free IoT devices and technologies for more ambient, distributed computing. In recent years, Wi-Fi-based communications are becoming more energy-efficient, and channel state information (CSI) has the potential to sense more detailed information about the things in the real world. Besides, ambient backscatter has appeared as a promising technology for zero-energy sensing and communications. Leveraging those state-of-the-art technologies, energy harvested IoT devices for context recognition of humans and objects will be in reality. A significant challenge is how to make use of inferior, less-powerful zero-energy IoT devices to achieve processing of interest, i.e., accurate recognition of humans and objects, while a single device does not work. Therefore, we consider orchestrating distributed tiny IoT devices for both sensing and communications. Particularly, distributed machine learning in the local environment will achieve highly promising sensing in our ambient environment. In this paper, we survey the state-of-the-art technologies for zero-energy sensing and communications in the context of humans and objects sensing and recognition. Then, we address the challenges to be tackled in terms of such distributed, intelligent sensing using zero-energy devices. Finally, we introduce the concept of utilizing distributed IoT devices, followed by the statement about our ongoing work toward future zero-energy sensing and processing.