Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges

Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors, or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free, and privacy-preserving WiFi-enabled Internet of Things platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf WiFi routers to collect channel state information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition, respectively.

Device-Free Occupant Activity Sensing Using WiFi-Enabled IoT Devices for Smart Homes

A critical review of field implementations of occupant-centric building controls

Device-free occupancy detection and crowd counting in smart buildings with WiFi-enabled IoT

The Internet of Things (IoT) has created a ubiquitously connected world powered by a multitude of wired and wireless sensors generating a variety of heterogeneous data over time in a myriad of fields and applications. To extract complete information from these data, advanced artificial intelligence (AI) technology, especially deep learning (DL), has proved successful in facilitating data analytics, future prediction and decision making. The collective integration of AI and the IoT has greatly promoted the rapid development of AI-of-Things (AIoT) systems that analyze and respond to external stimuli more intelligently without involvement by humans. However, it is challenging or infeasible to process massive amounts of data in the cloud due to the destructive impact of the volume, velocity, and veracity of data and fatal transmission latency on networking infrastructures. These critical challenges can be adequately addressed by introducing edge computing. This article conducts an extensive survey of an end-edge-cloud orchestrated architecture for flexible AIoT systems. Specifically, it begins with articulating fundamental concepts including the IoT, AI and edge computing. Guided by these concepts, it explores the general AIoT architecture, presents a practical AIoT example to illustrate how AI can be applied in real-world applications and summarizes promising AIoT applications. Then, the emerging technologies for AI models regarding inference and training at the edge of the network are reviewed. Finally, the open challenges and future directions in this promising area are outlined.

Szu-Cheng Chien

Papers

WinLight: A WiFi-based occupancy-driven lighting control system for smart building