# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 4 Proposed Solution
It optimizes data transmission by sending concise information during normal health conditions, ensuring remote healthcare providers receive critical data promptly for timely intervention. Similarly, patients with diabetes, particularly people who are at risk of hyperglycemia or hypoglycemia, are equipped with wearable sensors that continuously monitor their blood glucose levels. These patients may experience sudden fluctuation in blood sugar. These crucial data readings are promptly transmitted, acting as a timely intervention to prevent the patient from reaching a critical and potentially life-threatening state. The list of notations for the proposed scheme EE-FDAS is shown in Table 1. \begin{table}
\begin{tabular}{l l l} \hline \hline \(\mathbf{Sr.}\) & **Notation** & **Description** \\ \hline
1. & SL & Cluster Heads \\
2. & TH\({}_{\text{L}}\) & Lower Threshold \\
3. & TH\({}_{\text{U}}\) & Upper Threshold \\
4. & TH\({}_{\text{L1}}\) & Lower Threshold for 1 st parameter \\
5. & TH\({}_{\text{U1}}\) & Upper Threshold for 1 st parameter \\
6. & TH\({}_{\text{L2}}\) & Lower Threshold for 2 nd parameter \\
7. & TH\({}_{\text{U2}}\) & Upper Threshold for 2 nd parameter \\
8. & N[i].Reading[] & Sensor readings for the Node N[i] \\
9. & dataString & Concatenated string having all values \\
10. & R\({}_{\text{data}}\) & Received Data at the Fog Server \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of Notations. Transmitting only critical data significantly reduces data volume and increases the efficiency of the proposed solution. The Time Division Multiple Access (TDMA) method is used to ensure collision-free data transmission. This approach also assists doctors and medical professionals in accessing only clear-cut and suspicious information for better diagnoses and treatment more efficiently. Additionally, this approach will significantly reduce the load on CH as well as reduce energy consumption. Furthermore, it increases the lifetime of nodes and provides only critical data to doctors. The primary emphasis of the algorithm lies in processing data generated by sensors. Initially, the data is examined to identify and eliminate duplicate values. Subsequently, the processed readings are transmitted to CHs for additional analysis. In the case of normal health readings, a Boolean representation is employed. Upon receiving this data at CHs, alerts are generated for emergencies, reporting to the relevant staff. The data is forwarded to a Fog server for detailed analysis by medical professionals. Further, this data is stored at the central repository, facilitating potential future needs. The algorithm is running on healthcare sensor nodes. When wearable sensor devices take data readings from the patient's body, the proposed algorithm checks it for duplicated values before transmitting it further. Sensor nodes in the proposed scheme perform the task of collecting health-related data. These sensors are deployed to monitor various health parameters such as blood sugar levels, heartbeat rate, blood pressure, body temperature, respiration rate, pulse oximetry, gastrointestinal tract conditions, oxygen saturation, and hemoglobin levels. The collected data are in different formats and checked for duplicated data readings. In steps 4-23, Algorithm 1 selects the healthcare sensor node as the input and delivers a de-duplicated data list as the output. If the number of readings for the current sensor node is 1, it calls the first function, AggregateMessage, with the one reading set as the input. The purpose of the function is to process the reading and concatenate it to the dataString. For all sensor nodes, produced data are saved after time t. In case the number of readings for the current sensor node is 2, it calls the second AggregateMessage function, which proceeds two readings as the input. The function opts for both readings and concatenates them to the dataString.