This research investigates air quality and environmental conditions at COMSATS University Islamabad Abbottabad Campus and Missile Chock using IoT-based sensors. Sensors were deployed at both sites to monitor PM 2.5 levels, AQI, temperature, humidity, and gas concentrations. Data was collected over specific intervals and analyzed to identify trends and differences. The methodology involved using IoT-based sensors to capture real-time data on environmental parameters and air quality indicators at both locations, i.e., COMSATS University and Missile Chowk. At COMSATS University, PM 2.5 levels consistently fell within the "Good" category, with readings ranging from 11 to 35 µg/m³. AQI values improved over time, dropping from 91 to 2, indicating effective air quality management. Temperature and humidity remained stable, ranging from 19°C to 21.4°C and 57% to 63%, respectively. The MQ-2 sensor said that the gas levels were between 2550 and 3776 parts per million. On the other hand, Missile Chock had higher PM 2.5 levels, which varied from "Moderate" to "Poor," with values between 11 and 139 µg/m³. Initially, the AQI measurements revealed "Moderate" pollution, but with time, they became better and reached "Good." The humidity was between 18% and 20%, while the temperature was between 30°C and 31.5°C. The MQ-2 sensor results showed that the gas levels were generally high, between 3887 and 4159 ppm. The survey shows that major cities like Missile Chock have greater pollution because of people and automobiles. On the other hand, green locations like COMSATS University have cleaner air. We need to always be aware of air pollution and do something about it so that the air quality in cities improves and the health hazards that come with it decrease.
Crack monitoring of pavements is an ever-evolving technology with new crack identification technologies being introduced frequently. Although older technologies consisted of physical removing the pavement section using coring, however new methods are available that are non-destructive and yield a higher performance than conventional technologies. This paper compiles various crack monitoring technologies such as wireless sensor networks, photo imaging, laser imaging, 3D road surface profile scans, acoustics wave propagation technology, embedded strain sensors and onboard vehicle sensors that majorly use an artificial intelligence algorithm to identify and categorize the cracks. The research also includes the use of convolutional neural network that can be used to analyze pavement images and such neural network can localize and classify the cracks for crack initiation and propagation stage. The research concludes with the favor of using the optical imaging technology called Syncrack which serves better performance in terms of time of prediction by 25% and accuracy by 30% when compared to other sensing technologies.