Occupancy Estimation in Smart Building using Hybrid CO2/Light Wireless Sensor Network

Smart building, which delivers useful services to residents at lowest cost and maximum comfort, has gained increasing attention in recent years. A variety of emerging information technologies have been adopted in modern buildings, such as wireless sensor networks, internet of things, big data analytics, deep machine learning, etc. Most people agree that a smart building should be energy efficient, and consequently, much more affordable to building owners. Building operation accounts for major portion of energy consumption in the United States. HVAC (heating, ventilating, and air conditioning) equipment is a particularly expensive and energy consuming of building operation. As a result, the concept of “demand-driven HVAC control” is currently a growing research topic for smart buildings. In this work, we investigated the issue of building occupancy estimation by using a wireless CO2 sensor network. The concentration level of indoor CO2 is a good indicator of the number of room occupants, while protecting the personal privacy of building residents. Once indoor CO2 level is observed, HVAC equipment is aware of the number of room occupants. HVAC equipment can adjust its operation parameters to fit demands of these occupants. Thus, the desired quality of service is guaranteed with minimum energy dissipation. Excessive running of HVAC fans or pumps will be eliminated to conserve energy. Hence, the energy efficiency of smart building is improved significantly and the building operation becomes more intelligent. The wireless sensor network was selected for this study, because it is tiny, cost effective, non-intrusive, easy to install and flexible to configure. In this work, we integrated CO2 and light senors with a wireless sensor platform from Texas Instruments. Compare with existing occupancy detection methods, our proposed hybrid scheme achieves higher accuracy, while keeping low cost and non-intrusiveness. Experimental results in an office environment show full functionality and validate benefits. This study paves the way for future research, where a wireless CO2 sensor network is connected with HVAC systems to realize fine-grained, energy efficient smart building

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Resource Allocation Challenges and Strategies for RF-Energy Harvesting Networks Supporting QoS

This paper specifically addresses the resource allocation challenges encountered in wireless sensor networks that incorporate RF energy harvesting capabilities, commonly referred to as RF-energy harvesting networks (RF-EHNs). RF energy harvesting and transmission techniques bring substantial advantages for applications requiring Quality of Service (QoS) support, as they enable proactive replenishment of  wireless devices. We commence by providing an overview of RF-EHNs, followed by an in-depth examination of the resource allocation challenges associated with this technology. In addition, we present a case study that focuses on the design of an efficient operating strategy for RF-EHN receivers. Our investigation highlights the critical aspects of service differentiation and QoS support, which have received limited attention in previous research. Besides, we explore previously unexplored areas within these domains