## 3 Research Statement  
We contemplate the set of WSN nodes denoted by \(N\) and forwarding nodes, which were distributed over a 2D area, as shown in Figure 2. Each sensor node sends sensing information periodically to monitor the events in military surveillance to the forwarding node, considered to be a trusted sensor node, which forwards the aggregation result \(A_{result}\) calculated using the double weighted trust evaluation [23] to the base station. The sensor node's battery capacity is denoted by \(E_{max}\) initially with fully charged battery up to the maximum capacity. The minimum energy of a node is denoted as \(E_{min}\) for WSN nodes (for it to be capably operational). The weight trust evaluation system updates the weight of the sensor node periodically after the aggregation result to detect whether nodes are faulty or malicious in the WSN to make correct conclusions in monitoring applications based on prior decisions. Affected or compromised sensor nodes transmit information to the base station. Accurate and timely identification of malicious nodes is essential to provide reliable operation of networks. Network life limitations are widely considered to be the main performance barrier to providing single network security and permanently changing the network in a wireless sensor network. Every sensor node produces sensitive information (alarm) to monitor specific military use targets. In the divergence of a malicious node, when a node with normal characteristics starts sending an alarm, the neighboring node also starts sending an alarm after a short interval. The FN collects the sensor measurements of the corresponding sensor nodes, where \"1\" indicates the alarm. It calculates the weighted values as \(A_{result}=0\) or 1. At the end of the FN integration, all loads assigned to the member nodes are renewed [22; 24; 25]. Then, nodes with \(W_{i}\) that are more or less equal to a certain \(W_{min}\) converging value are identified as malicious. The \(W_{min}\) value is expected to be 0 if the load from 0 to 1 is considered a malicious terminal and is not disabled from full processing. Every sensor node uses power to send and receive data. \(P_{i}\) is determined as the rate of consumption of energy by sensor node \(i\in N\). In this research proposal, we utilize the certain energy dissipation [26; 27]. Multi-hop data transfer is used to transfer data through nodes. The symbol \(f_{ij}\) implies that the transmission flow from the sensor network node is represented between \(i\) to \(j\), whereas \(f_{iB}\) is the rate of flow transmission from network node \(i\) to the \(B\), which represents the base station. The balancing constraint at every node \(i\) for the flow rate is shown by Equation (1). \[P_{i}=\rho.\sum_{k\in N}^{k\
eq i}f_{ki}+\sum_{j\in N}^{j\
eq i}f_{ji}+C_{iB}f_{ iB}\hskip 28.452756 pt\forall i\in N \tag{1}\]  
Figure 2: An example sensor network with N sensor nodes with unmanned aerial vehicles. where \(C_{ij}/C_{iB}=\beta_{1}+\beta_{2}D_{ij}^{\kappa}\). We proposed a novel approach to recharging the battery at every legitimate node: an unmanned aerial vehicle (UAV) is engaged in the network, and it is assumed that UAV is completely charged to complete a full rotation until it reaches the charging station for the next cycle. As discussed earlier, the double-weighted trust-based evaluation of nodes belonging to the network yields the value of \(W_{i}\) depending on the data sensed and on the aggregation result from the base station. The trusted weight for the individual node is calculated in the base station. Further aggregation result weighting is given accordingly. Faulty or malicious nodes are identified through the value of \(W_{i}\). Here, we use the technology of wireless energy transfer [22] through which we can charge the sensor nodes via unmanned aerial vehicles [22; 24; 25]. The UAV begins from the service station (\(S\)), with its travel speed being \(V\) (in \(m/s\)).