## 5 The Proposed Protocol
### Communication Overhead of the Proposed Protocol
#### 5.6.4 Data Redundancy and Data Fusion Optimization  
The process of replicating data over several SNs in order to guarantee data integrity and reliability is known as data redundancy. Redundancy must be properly controlled to prevent needless energy and bandwidth utilization, even if it might be advantageous for fault tolerance. Data fusion further enhances this process by combining data from multiple sources to produce accurate, comprehensive, and useful information. By reducing the volume of raw data transmitted and processed, data fusion minimizes redundancy and promotes efficiency, particularly in sensor networks. The proposed protocol integrates data redundancy and fusion techniques to ensure sustained communication, optimize resource utilization, and enhance network performance. Redundancy is dynamically adjusted based on current network demands, while data fusion techniques are employed to refine the data collection process. These improvements help to balance energy consumption, reduce latency, and maintain high levels of reliability. The following techniques can be used to optimize the proposed protocol and manage data fusion and redundancy:  
* _Redundant data aggregation:_ This proposed protocol uses redundant data aggregation, which aggregates and processes multiple copies of data from different SNs inside a DFG to improve reliability of data and alleviate network congestion. This method means gathering redundant data from DFGMs at PDFH, FSDFH, or SSDFH, aggregating it to remove duplicates, and then sending it to the sink node after extracting valuable information. By doing the above, we guarantee that the integrity and completeness of the data are maintained, even in the event that any data packets are lost or distorted in transmission. Through the reduction in the sent data amount, this technique maximizes bandwidth utilization while simultaneously enhancing fault tolerance and data reliability. Moreover, it lowers the network's energy consumption since fewer data packets must be sent across lengthy distances. It is important to note that this method is applied to every IoT application listed in Table 3, which makes it more equipped to manage the difficulties of dynamic and resource-constrained IoT circumstances. * _Energy-adaptive and selective redundancy methods:_ The following lines provide more details on these methods: 1. _Energy adaptive redundancy levels:_ In the proposed protocol, adaptive redundancy levels refer to dynamically modifying the quantity and distribution of redundant data and operations in response to SN's energy levels and real-time network circumstances to assess the health of the network and determine the appropriate amount of redundancy in real time. For example, if network stability is threatened, the proposed protocol can boost redundancy to provide reliable data transfer and fault tolerance, as long as SN energy levels are higher than the energy-critical threshold. On the other hand, it can decrease redundancy to save bandwidth and energy in steady circumstances. Because of its flexibility, the network can minimize needless overhead while maintaining excellent performance and resiliency. By adjusting redundancy based on variables including SN energy levels, network traffic, and data criticality, this method maximizes the trade-off between reliability and resource utilization. 2. _Selective redundancy:_ In the proposed protocol, selective redundancy is a strategy that aims to improve network resilience and reliability by purposefully replicating critical data and functions only at particular network points/parts. The proposed protocol applies redundancy selectively according to the significance of the data and the application's criticality. For example, critical data may need more redundancy in IoT healthcare applications, such as health monitoring information. As shown in Table 3, we have a variety of IoT applications in our proposed protocol, which we categorize into non-medical and medical applications.