# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 4 Performance Evaluation with Simulations  
In this section, the performance of the proposed multi-source cooperative data collection schemes with the mobile sink node is evaluated by simulation. We assume each active source node can successfully receive the data from at least two neighbor source nodes and the sparse cooperation models mentioned above can be formed for the wireless networks with uniform random deployment of dense sensor nodes. In the simulation, Binary Phase Shift Keying (BPSK) constellations are employed and the data from each source node undergo independent and identically distributed quasi-static Rayleigh fading. We compare the performance of the one-round cooperation, the two-round cooperation, and the non-cooperation data collection scheme. In the non-cooperation data collection scheme, each source node individually transmits its data to the sink node by employing an LDPC code with the same code code length and code rate with the cooperative schemes, when the sink node approaches and awakes it. The iterative decoding based on message passing with maximum iteration of 30 times is performed at the sink node \(d\) for all the data collection schemes. The simulation stopping criterion is that the maximum number of frame is 1,000,000 or the error frame number reaches 100 for all the simulations. Firstly, the bit error rate (BER) of the one-round cooperation scheme is compared with the non-cooperation scheme in Figure 6. The overall code length \(L\) in all the schemes equals to 1024 bits and the code rate is \(r=1/2\). When the number \(N\) of the source nodes involved in the cooperation set \(S\) increases, the data length \(K\) of each source decreases with the constraint \(L=K\times N/r\) in order to maintain the constant code length \(L\) for comparison. We simulate three one-round cooperation cases with different \(N\), that is, the cooperation with 4, 8, or 16 nodes. In Figure 6, it is observed that the one-round sparse cooperation with distributed LDPC codes exhibits better performance over the non-cooperation scheme. It can be also observed that the performance of the cooperation scheme improves with the increasing of the cooperative node number \(N\). Moreover, the encoding complexity at each source node in the one-round sparse cooperation scheme is lower than the non-cooperation, for distributed encoding is employed in the cooperation scheme. Secondly, the BER performance of the two-round cooperation with different numbers of source nodes is illustrated in Figure 7. Here, we assume the source-sink channels denoted by \(s_{i}-d,1\leq i\leq N\) keep constant during the same moving round of the sink node and change for two rounds due to the different locations of the sink node. It is observed that the two-round cooperation with four sources can achieve about 28 dB gain over the non-cooperation scheme with one round moving of the sink node at a BER of \(10^{-5}\). It can also further improve the performance by employing more cooperative source nodes. For the non-cooperation, the sink node can also move two rounds to collect data and the non-cooperation with two-round moving improves the performance over one-round scheme, attributed to the diversity in Figure 7. In addition, the proposed cooperative data collection schemes lead to some degree of complexity such as the routing protocol compared with the non-cooperation scheme. Thirdly, the performance of the two multi-source cooperation models is compared in Figure 8. It can be observed that the two-round cooperative scheme can achieve much better performance over the one-round cooperation with the same number of cooperation nodes. For example, the two-round cooperation scheme with eight source nodes can achieve about 21 dB gain over the one-round scheme. The performance improvement is attributed to two reasons. On the one hand, additional diversity gain can be obtained due to the wireless channel change when the sink node moves twice along the region of source node.