## 4 The Proposed Method: SLP-E
### Protocol Execution Phase
#### 4.2.2 Scenario-2
Each trial's simulation comes to an end when an adversary finds the source node or when all packets are transmitted to the BS. The base station, which serves as the hub for all incoming network traffic, is where the enemy begins its backtracking procedure. Each sensor node in the network is initially charged with 0.5 joules of energy. In this example, we use the locations of the source nodes: (200, 0), (550, 0), (900, 0), and (1050, 0). We use the Python programming language to implement and simulate the performance of the proposed technique. ### Result Analyses

In this section, we present the results and explain them in detail. The plots for the safety period metrics are shown in Figure 4:8 It is observed that the PRLPRW technique performs better when the source is closer to the BS and in the mid-range of the network. However, as the distance between the source and the BS increases, the safety period has a lower value than in all other protocols, except the SPR technique. In SRR protocol, the safety period increases as the distance between the source and the BS increases. This trend is seen for distances up to 900 units. Beyond this distance, the safety period decreases. A similar trend of increasing safety period with the increase in the distance between the BS and the source is seen in the SLP-R technique. In this case, there is an increase in the safety period metric for all distances between the source and the BS. In these three techniques, the safety period is having larger values for the larger distances between the source and the BS. The reason behind this trend is that these protocols have distance-dependent behavior. That is, the larger the distance of separation between the BS and the source node position, the larger the safety period. In the PSSLP technique, the safety period has larger values for all the positions of the source node, except when it is closer to the network edge where its value decreases marginally. In the proposed technique, the safety period metric has the highest value compared to other solutions. This holds true for all source node-to-base station (BS) distances within the network, and it is achieved because we ensured an equitable distribution of the number of hops a packet must traverse before reaching the base station (BS), regardless of the source node's placement within various network settings. It is seen that the existing protocols have a different trend in safety period metric value when the source is closer to the network edge (i.e., in between 900 and 1050 units of distance from the BS). This phenomenon can be attributed to the fringing effect. However, in the proposed protocol, this is not the issue. We also observe that SPR has the smallest safety period metric, as it does not focus on privacy. That is, the amount of safety period is minimal when the source is close to the sink. As the distance between the source and the sink grows, so does the safety period. When the source node is close to the BS, PSSLP operates pretty well. However, when the source node is close to the network boundary, the safety period shortens. Thus, present methods have non-uniform safety periods. No matter where the source is in the network, SLP-E has nearly the same level of privacy. This behavior of SLP-E is attributed to the fact that the proposed scheme adapts the routing protocol based on the position of the source in the network, i.e., whether it is scenario-1 or in scenario-2. Thus, the goal of attaining uniform location privacy is accomplished. The degree of randomization in the routing path is indicated by the entropy metric. The entropy increases with the degree of randomness in the routing paths that packets take. In comparison to the other five strategies, the suggested SLP-E has the best entropy, as shown in Figure 4:9 When compared to other protocols, SRR has less entropy. All source position settings in the network exhibit this tendency. Because routing protocols do not take the location of the source in the network into account, PRLPRW, SLP-R, and SRR have low values of entropy.