## 1 Introduction  
In order to obtain first-hand data of wildlife's living conditions, researchers have to keep watch in the wild, suffer from boredom and the rugged environment and get a limited amount of data which has low accuracy. In the cultural relics protection, military investigation and so on, to obtain some relevant data, the similar situation often occurs. To deal with the problem, wireless sensor networks (WSNs) are applied. For example, in \"the Great Wall conservation\" project, more than 200 sensor nodes are deployed on the Great Wall. Theoretically, the WSN can get the complete realtime data related to the ruin. However, in reality, the nodes would consume much energy during forwarding processes and it is difficult to charge them, which results in obtaining the incomplete or delayed data. However, an effective WSN should be able to sense and forward data stablely and timely. For instance, the real-time and complete data in military investigations can provide effective support for fighting Therefore, how to save the energy consumption of nodes, reduce the transmission delay and improve the throughput in a WSN becomes an unavoidable problem [1; 2]. Researchers have proposed many methods to address the above-mentioned problem [3; 4]. In these studies, the hardware solutions are not only expensive, but also limited by the hardware technology and equipments. Thus, the software solutions are becoming more and more favored. A lot of software approaches are to use network coding strategies to improve network throughput, reduce transmission delay, and save energy consumption [5; 6; 7]. Surely, if network coding can be carried out (all links have the same transmission success rate and the two ways of a link also have the same transmission success rate), the network performance will be improved, and the more coding opportunities, the more significant effect [8]. In another word, an effective coding opportunity in a network mainly depends on the preconditions that all paths have the same transmission success rate on all links and the same transmission success rate in the two directions of a link. However, in an actual network, there may not always be many effective coding opportunities because of different transmission success rates (an example is described in Section 3). This will limit the performance improvement in the network. ANCR [9] proposed in this paper chooses effective coding opportunities to minimize transmission count overall by taking into account various transmission success rates on different links and in two directions of a link. In this way, the total number of transmission in a network will decrease, the network latency and energy consumption will also reduce and the network throughput will be improved. Summary of Results: In this paper, in order to simulate ANCR, we firstly establish the network topological graph and the data stream model of the network with the real relevant parameters in the WSN employed for monitoring wild animals in Qinling region in China. Then we establish the routing decision model which can automatically determine network coding opportunities, and apply the neighborhood search algorithm to adjust routes to reduce the total number of broadcast transmission in the network. According to the simulation results, we can know that ANCR shows the same network delay and throughput as NCR while the networks have sparse data streams. The main reason is that if there is no any coding opportunity in the network, ANCR will degenerate to NCR. On the contrary, the denser the data streams, the more the intersections (of the data streams). The inherent coding opportunities and the coding opportunities produced by adjusting routes will also become more. In this situation, ANCR is better than NCR, which is mainly reflected in the decrease of the total number of transmission because ANCR can use high-quality links and create network coding opportunities. This leads to the shorter transmission time, the higher network throughput and the lower energy consumption of the network.