###### Abstract
The proposed mechanism estimates the positions of sensor nodes by exchanging position packets between a mobile sink and the sensor nodes. As the mobile sink is more powerful, it houses a GPS module and shares its position within the network. When a sensor node receives a position packet, it uses a simple trilateration algorithm to estimate its own position. A _qualified node_ is calculated with at least three position packets, resulting in a more accurate position estimation, as shown in Figure 1a. However, even with only one or two samples, it is still possible to obtain a rough estimate. With only one sample, the sensor node assumes that it is at the same position as the sink. With two samples, it considers its position as the midpoint between the intersection of the position packets' circles. The proposed ECKN is able to maintain similar network performance (throughput, overhead) when compared to the state-of-the-art benchmark approach while localizing nodes and extending the network lifetime simultaneously. The main difference between the proposed ECKN and the existing works found in the literature is the fact that it is a joint localization, routing, and energy-aware mechanism. Other existing works take each of these issues separately, or at most two, simultaneously, e.g., localization and routing. ECKN demonstrates that it is possible to extend the wireless sensor the network lifetime while performing localization and proper routing. Although trilateration is a viable solution for the localization problem, there are some issues that need to be addressed. GPS location is not 100% accurate due to the noise that creates some uncertainties in its estimations [9]. When the sensor node receives position packets and calculates the intersection between them, it is not always able to find a common point where they intersect. Instead, it ends up with six different intersection points, as shown in Figure 1b. Using this information, the sensor node has to estimate its position by deciding which intersection points, called _reference points_, are the best to be used in the estimation. Furthermore, in the worst-case scenario where the sink moves randomly within the WSN boundary, some position packets might not be ideal for performing trilateration. Figure 1c shows an example of poor samples, in which collinear position packets are received by a sensor node, making it impossible for the trilateration algorithm to estimate an accurate position. In a WSN, communication between the source and destination nodes may require a multi-hop communication strategy due to constraints on the radio range and other resources. As mentioned earlier, nodes can properly forward messages to their destinations by being aware of their positions. This information is widely used to improve the performance of routing protocols. A popular approach to this routing issue is geographic routing. Geographic routing is one of the most popular routing schemes in WSNs because it scales well, meaning that network size is not a problem with this scheme. The forwarding decision is based on the position of the node and its neighbors. The most straightforward algorithm for geographic routing is greedy geographic forwarding (GGF). The idea behind GGF is to forward a packet to the neighbor node that is closer to the destination in terms of Euclidean distance based on their position coordinates. Figure 2 shows an example of greedy geographic forwarding, where node 1 receives a packet destined for the sink and then forwards that packet to node 3, which is the closest neighbor to the destination. If there is no neighbor that is closer to the destination, GGF fails, and the packet is dropped. Research efforts have focused on duty-cycled approaches [3; 10; 11] aimed at improving the network lifetime while maintaining a high delivery guarantee. Guaranteed delivery refers to the success ratio of forwarding a message from the source to the destination, which requires that there is at least one path connecting these two nodes in the network.