IoT technology has improved the quality of life as well as provides intelligence and comforts to the user. A Wireless Sensor Networks (WSN) technology is one of the powerful technologies that enable IoT objects to communicate with each other. Device-Free Localization (DFL) system exploits changes in radio signals across different environments aims to detect and track entities without requiring them to carry any additional RF devices or to be cooperatively participating with the localization process. This paper proposes a received signal strength indicator (RSSI) - based DFL system that can be used to detect human presence in an area of interest (AoI) using changes in signal strength measurements. The main objective of this paper is to design a DFL system that can be easily deployed and configured using IRIS mote from MEMSIC based on `plug and play' concept and operates in real time. The system is deployed in CEASTech's Research Room by using the IRIS mote as the communication module in the network. The principle behind this is that the human body and motion have strong impact on the signal strength of indoor wireless links being transmitted from a transmitter to one or more base stations by either shadowing or diffraction, or by scattering. Result shows that signal strength fluctuate by 3.97dBm and from 10 to 15dBm with the presence of stationary human and moving human respectively.

The number of registered vehicles worldwide is increasing dynamically in recent years, over 72 million automobiles were sold in 2015. The AAA Foundation for traffic safety found that driver or passengers spending an average of 46 minutes per day inside a vehicle. Unfortunately, human awareness of indoor air quality in the vehicle cabin is surprisingly low. The quality of air within the vehicle cabin can influence the occupants' comfort level. One of the main indicators of enclosed environment's air quality is the carbon dioxide (CO ₂ ) content which is released through human breathing. High level of CO ₂ can cause the occupants feel fatigue, drowsiness and slow reaction of actions. Many published works show that air conditioning (AC) with recirculation mode inside a vehicle could minimize the particulate pollutant flow into the cabin. However, it does not reduce the amount of accumulated CO ₂ . The purpose of this paper is to study the concentration of the accumulated CO ₂ under different vehicle speed and number of occupants under AC recirculation mode by applying qualitative data collection method. Then, the cooling period of cabin temperature after vehicle parked under blazing sun. Experimental results show that accumulated CO ₂ concentration exceeded the recommended threshold value of 1200 ppm after 10 minutes when two passengers occupied the vehicle's cabin. The CO ₂ concentration exceeded the threshold 3.33 times faster for two occupants compare to one occupant in the cabin with the same speed. Vehicles moving at higher speed of 90km/h shows a lower accumulated concentration of CO ₂ when compared with lower vehicle speed. Vehicle speed of 50 km/h had recorded the shortest time taken with 9 minutes to pass 1200 ppm. Then, a car which parked under a 36.5 °C on a sunny day for 3 hours will reach the cabin temperature of 49.5 °C.

Over the past few decades, human activity monitoring has grabbed considerable research attentions due to greater demand for human-centric applications in healthcare and assisted living. For instance, human activity monitoring can be adopted in smart building system to improve the building management as well as the quality of life, especially for the elderly people who are facing health deterioration due to aging factor, without neglecting the important aspects such as safety and energy consumption. The existing human monitoring technology requires additional sensors, such as GPS, PIR sensors, video camera, etc., which incur cost and have several drawbacks. There exist various solutions of using other technologies for human activity monitoring in a smartly controlled environment, either device-assisted or device-free. A radio frequency (RF)-based device-free indoor localization, known as device-free localization (DFL), has attracted a lot of research effort in recent years due its simplicity, low cost, and compatibility with the existing hardware equipped with RF interface. This chapter introduces the potential of RF signals, commonly adopted for wireless communications, as sensing tools for DFL system in human activity monitoring. DFL is based on the concept of radio irregularity where human existence in wireless communication field may interfere and change the wireless characteristics.

Urban flooding is increasingly exacerbated by the accumulation of floating debris in rivers, which obstructs water flow, degrades water quality, and poses significant risks to human safety and environmental sustainability. Effective monitoring of floating debris is therefore critical for early flood warning and long-term water resource management. This study presents a real-time monitoring framework that integrates deep learning-based object detection models, You Only Look Once (YOLO) with video surveillance for the identification and quantification of floating debris in urban rivers. Field deployments were conducted in flood-prone sites in Shah Alam, Malaysia, to evaluate the system under real-world environmental conditions. Results show that YOLOv7 achieved higher accuracy and robustness across diverse debris classes and lighting conditions compared to YOLOv9, with precision, recall, and F1-scores demonstrating strong detection reliability. Beyond technical accuracy, the system provides timely and actionable information for flood risk assessment, river management, and environmental monitoring. By automating debris detection and quantification, this study contributes to Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action), offering a scalable monitoring solution for flood-prone regions.