## 5 Discussion  
The integration of IoT and remote sensing technologies presents a transformative opportunity for the textile industry in Pakistan, offering a range of applications to enhance operational efficiency, ensure product quality, and advance sustainability objectives. This research aims to address critical sustainability goals by leveraging technological innovations within Pakistan's textile sector. To calculate the percentage changes, the following formula was used:  
\[\text{Percentage change}=\frac{\text{New value}-\text{Old Value}}{\text{Old Value}}\times 100\]  
Applying the formula to each parameter that was used in our study, we achieved their respective percentages as follows:  
1. Force Sensitive Resistor (FS-500) \[\text{Force}(\text{F})=\text{Resistance}(\text{R})\times\text{Voltage}(\text{ V})\]  
Figure 11: Graph showing speech recognition. 2. Temperature Sensors (LM35) \[\text{Temperature}(^{\circ}\text{C})=\text{VoltageOutput(mV)}/10\]
3. Measuring Humidity Relative Humidity (%RH) = Sensor Output Value
4. PIR Motion Sensors (HC-SR501): detect motion by measuring changes in infrared radiation. 5. Light Intensity Sensors (PV-200) Illuminance (Lux) = Sensor Output Value (V) \(\times\) Calibration Factor
6. GPS Modules (NEO-6M): GPS modules provide latitude and longitude coordinates. 7. Tank Water Level Sensors (UltrasonicSensor_v3.1) Distance (cm) = Time of Flight (\(\upmu\)s) \(\times\) Speed of Sound (cm/\(\upmu\)s)/2
8. RPM Sensors (RPM-1000) RPM = (Frequency of Pulses/Number of Pulses per Revolution) \(\times\) 60
9. IP Cameras: effectiveness is qualitative, based on recorded incidents and monitoring coverage. 10. Speech recognition involves several steps including the following: Word Error Rate (WER): WER = S + D + I/ N \(\times\) 100 where \(\bullet\) S = the number of substitutions; \(\bullet\) D = the number of deletions; \(\bullet\) I = the number of insertions. N = Total number of words in the reference text Recognition Accuracy = (1 - WER) \(\times\) 100 Average Response Time = Total Response Time/Number of Commands Percentage Improvement (Old Efficiency Metric - New Efficiency Metric/Old Efficiency Metric) \(\times\) 100  
For this, we calculated a reduction in command errors and task completion time. Speech recognition reduced the average time to execute a command from 10 s to 8 s which is equivalent to 20%. The specific and measurable outcomes observed during the implementation of IoT sensors across different machinery and manufacturing phases are outlined below. * The force-sensitive resistor (FS-500) installed on weaving and knitting machines reduced machinery breakdowns by 20% through precise pressure monitoring, enhancing operational efficiency. * Temperature sensors (LM35) placed in washing and drying machines during the respective phases resulted in a 15% decrease in fabric spoilage via real-time temperature control, ensuring optimal conditions for production. * Humidity sensors (DHT22) utilized in spinning and storage units decreased equipment corrosion by 2.5%, extending machinery lifespan by regulating moisture levels. * PIR motion sensors (HC-SR501) deployed in the facility's security systems reduced unauthorized access incidents, enhancing safety. * Light intensity sensors (PV-200) implemented across the facility's lighting systems lowered energy consumption by 27% by optimizing lighting, reducing operational costs. * GPS modules (NEO-6M) in delivery vehicles facilitated a 15% improvement in delivery efficiency by enabling real-time shipment tracking, minimizing delays. * Tank water level sensors (UltrasonicSensor_v3.1) achieved a 20% reduction in water usage by monitoring levels, promoting sustainability by reducing further waste with this sensor; alerts were generated to fulfill the purpose of recycling when reaching a certain limit.