## 3 System Model and Design Objectives  
To maintain network reliability for QoS performance, confidentiality, integrity, and availability [35] and guarantee privacy preservation [36; 37], the proposed SDAAA system model utilizes an intelligent WSN healthcare application monitoring [38] scenario. The WSN application scenario includes a WPAN (wireless personal area network) and a WBAN (wireless body area network) as shown in Figure 1. We monitor patient data and protect healthcare applications (HAs) against all cyberattacks, including sinkhole nodes and sybil node cyber attacks [38; 39]. Figure 1 assumes a wireless sensor network consists of a clustered-based topology with limited mobile cluster nodes (MCNs), which are mobile sensors, and static cluster nodes (SCN), which are static sensors, deployed with cluster head nodes (CHNs) in the WSN. However, this cluster provision is unavailable in contending WSN privacy models such as SD, EEHA, HAS, IIF, and RHC protocol system models. Thus, these contending WSN models do not possess MCNs or SCNs or the CHN attributes that enhance network reliability and energy efficiency. However, based upon the cluster network deployment in the proposed SDAAA approach, sensor nodes collectively complete monitoring tasks. Due to cost limitations, the sensor nodes are without tamper-resilient hardware. In addition, stationary resourceful heterogeneous sensor nodes are in locations directly connected to the base station (BS). Our proposed SDAAA aggregation system model applies to each cluster in the WSN application, which utilizes four types of sensor nodes in the wireless sensor network: actor nodes (ANs), mobile sensor nodes (MSNs), data aggregator nodes (DANs), and event-monitoring nodes (EMNs). These four types of node provide reliability in the network. By contrast, as stated previously, contending models like SD, EEHA, HAS, IIF, and RHC protocols do not utilize cluster-based network design in their network. Thus, the contending secure data aggregation models do not have the four types of sensor nodes that provide reliability. In our proposed SDAAA approach, the BS is the target point used to compute the aggregation result. The following assumptions are held. * Cyberattacks, including sinkhole and sybil node hacks, may occur in healthcare computer systems. * EMNs are static cluster nodes (SCNs) that link the static sensor nodes or mobile cluster nodes (MCN) that link the mobile nodes; they are deployed together with the CHNs in the network, and they have the capability to be utilized by WPANs to monitor patients' health conditions. SCNs or static sensor nodes are deployed in fixed infrastructure equipment. In this research, the EMN communicates with the mobile sensor nodes or MCNs and sensing center to query for patient sensing data. * DANs can be either static or mobile sensor nodes that serve as CHNs (cluster head nodes). DANs receive data from the neighboring nodes, aggregate the data, and forward the aggregates to the BS or the gateway. DAN plays a major role in the secure aggregating process. DANs possess more computational and processing capabilities and storage capacity than EMNs. * MCNs also link mobile sensor nodes attached to patients and any vehicles within the healthcare environment. They extend the capability of the static nodes in the WSN. MCNs also extend the coverage efficiency of patients and vehicles in the network. Moreover, MCNs can forward updated information to DANs. * ANs are actor nodes and can also serve as gateway nodes. They are potent power nodes and processing units for decision processing. ANs have additional computational power and resources responsible for collecting the data from DANs. In addition, they support the BS in identifying legitimate DANs. Figure 2 initiates a data fragmentation process for secure data aggregation. EMNs are responsible for collecting the data from patient health monitoring events. An EMN forwards the data to the data fragmenting node to fragment data into variable sizes.