## 7 Conclusions  
This paper introduces a new algorithm called ECKN for wireless sensor networks that combines localization estimation, packet routing, and sleep scheduling. ECKN uses a mobile sink to estimate sensor node positions through trilateration, and proposes algorithms for accepting or rejecting position packets from the mobile sink. The results indicate that ECKN enhances the network lifetime while maintaining the packet delivery ratio. In addition to the ECKN algorithm, the paper presents an algorithm for forwarding event packets from sensor nodes to the sink based on the distance and residual energy of neighboring nodes. It also introduces bridges as a way to reduce the number of intermediate nodes between the source sensor node and the sink, thus shortening paths. The proposed sleep scheduling algorithm is based on CKN, which ensures network connectivity with each node having at least k awake neighbors. The proposed algorithm assigns different k values based on the node's distance from the sink, with nodes closer to the sink having a larger k value to handle higher network traffic and improve the network lifetime. The paper proposes a localization algorithm that utilizes a mobile sink for trilateration-based sensor node position estimation. Additionally, it presents algorithms for finding reference points and accepting/rejecting position packets. ECKN with bridges outperforms other algorithms in terms of the packet delivery ratio and number of hops. The sleep  
Figure 17: (**a**) Network lifetime and (**b**) number of alive nodes using different sleep schedulers. Figure 18: Delivery ratio using different sleep schedulers. scheduling algorithm assigns varying k values based on the distance to the sink, similar to CKN, for the network lifetime and delivery ratio. Based on simulation experiments, the proposed ECKN with bridges outperforms ECKN without bridges and GGF. This superiority is attributed to the creation of bridges by ECKN, which reduces the distance between the source node and the sink, thereby increasing the likelihood of successful packet delivery. ECKN without bridges lags behind GGF because ECKN takes into account not only the node positions but also their remaining energy. This additional consideration can sometimes result in longer paths for packets, thereby increasing the chances of packet drops. The success of packet delivery depends on the number of hops taken by the event packet from the source to the sink node. ECKN with bridges achieves the lowest average number of hops due to the creation of bridges, which effectively shortens the path between the source and sink nodes compared to GGF. In terms of network overhead, GGF and ECKN without bridges exhibit similar behavior, while ECKN with bridges incurs higher overhead. This discrepancy can be attributed to the larger time-to-live (TTL) used by ECKN with bridges during bridge creation. Unlike GGF and ECKN without bridges, which maintain the same TTL for every position packet from the sink, ECKN with bridges adjusts the TTL based on the possibility of shortening the path between an area and the sink's position. When a positive adjustment is made, increasing the TTL of the position packet allows it to reach a more distant region. Consequently, the position packet is resent more times, leading to increased network overhead. Regarding the network lifetime, ECKN demonstrates similar performance to CKN using k = 3. This similarity arises from the fact that nodes closer to the sink have larger values for k, which enhances the probability of reaching the sink. Therefore, due to the varying values of k based on the distance to the sink, ECKN performs as well as CKN using k = 1 in terms of the network lifetime while maintaining a good delivery ratio comparable to CKN using k = 3. In future work, we plan to investigate the impact of a different number of sinks and sink mobility in the position estimation of the nodes and packet routing.