# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 4 Proposed Solution  
In this section, a new scheme has been presented, entitled Energy Efferent Fuzzy Data Aggregation System (EE-FDAS), to resolve the identified problem. In the healthcare sector, various types of sensors are deployed in the patient's body that collect readings in different formats. These readings vary from each other as sensors that collect single-part values, such as BPM, produce 16-bit readings. Similarly, some sensors generate readings into two values. For instance, sensor monitoring BP generates data in two parts, including systolic and diastolic readings. Both generate 16-bit data which collectively becomes 32-bit long. It makes the environment extremely heterogeneous which ultimately raises energy utilization. In the proposed scheme, at level 1, when sensors generate data it is checked whether it lies in the normal range or not. If it falls in the normal range, instead of transmitting the whole 16 or 32 bits, only Boolean digit 0 is transmitted which takes only 1 bit. The phases of EE-FDAS are shown in Figure 2.  
The patients are mainly equipped with wearable sensors checking different parameters including blood sugar level, heartbeat rate, blood pressure [38] body temperature, respiration rate or pulse oximetry [39], gastrointestinal tract, oxygen saturation, and  
Figure 2: Phases of EE-FDAS. hemoglobin [40]. The thresholds represent standardized value ranges globally recognized for numerous healthcare parameters. Moreover, our proposed solution is adaptable to changes in thresholds because the collector node consolidates and processes values based on the specified threshold ranges. These ranges can be easily updated in the collector nodes at any stage if needed. The sensors take readings from patients' bodies in the form of concatenated strings. For instance, patient A data values for body temperature, heart rate, oxygen level, cholesterol, and sugar level in the format of 98.9:75:98:130:180. The data readings gathered from the body are categorized as normal or critical. For instance, the Beats per Minute (BPM) of a healthy person lies between 60 to 100 beats per minute, and is considered as the normal range, but if the readings are greater or lower than these threshold values, it is considered as critical. Similarly for BP, the normal range is 120/80 mmHg. When the Systolic Pressure or Diastolic Pressure varies from these defined thresholds, it is considered a critical reading. In a healthy human being, these parameter readings remain stable and fall within the normal range. In such occasions, the senor nodes simply transmit a single Boolean digit 0 to the CH. Conversely, if a person is unwell, irregularities might be visible in one or two sensor readings, while the other data readings usually remain consistent. In such cases, the critical data is transmitted in its original format, while the other sensors deliver only the Boolean digit. In very rare circumstances, all the deployed sensors generate unusual readings. For such cases, all the values are sent in their original format. The detailed algorithm is explored in the base paper [22]. Practically, the likelihood of this case is much lower than the first two cases. To describe it well, a case study of Patient B is presented. Patient B is equipped with a set of wearable sensors to monitor crucial health parameters, including blood pressure (BP), temperature, cholesterol level, oxygen saturation level, heart rate, and sugar level. These sensors provide real-time data, allowing for continuous health monitoring. The fluctuation occurs in heart rate and cholesterol level. The generated concatenated string is generated as 0:0:210:0:45:0. The EE-FDAS scheme presents a mechanism that justifies its suitability for most of the practical applications in healthcare settings. For example, patients with chronic conditions, such as hypertension or hypotension, are equipped with wearable sensors. It transmits relevant health data based on the patients' real-time health conditions.