# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 5 Performance Evaluation
### Performance Comparison of IPSA, RS, and LDS  
We compare the performance of IPSA against two typical geographical coverage-based participant selection schemes: the randomized selection (RS) scheme and the linear distance-based selection (LDS) scheme.  
The RS algorithm intends to randomly select nodes in the sensing field. To implement the RS, we randomly select certain grids and a corresponding participant for each selected grid and put them into active mode. We then compute the corresponding approximation errors with different numbers of active grids.  
LDS algorithm intends to select nodes according to their relative linear distance to central node in the clusters in the sensing field [1]. To implement the LDS algorithm, we select participants to be put into idle mode according to a probability:  
\[p\left(x\right)=\ \left\{\begin{array}{l l}\gamma x/R_{center}&0\leq x\leq x_{s};\\ 1&x_{s}<x\ \leq\ R_{center}\end{array}\right. \tag{19}\]  
where \(x\) is the distance from the participants to the central participant which is located in the center of Intel Berkeley Research lab (_i.e._, #4 participant) \(R_{center}\) is the transmission radius of the central participant, and \(x_{s}\) is a threshold determined by \(R_{center}\) and \(\lambda\), the target fraction of participants to be put into idle mode, and \(\gamma\) is a constant to enforce \(\lambda\) fraction of participants to be of idle mode. In this experiment, we select \(R_{center}=100\), \(\gamma=0.6\) and \(x_{s}=100\). In the Intel Berkeley Research Lab sensing field, as the length and width are 41 m and 32 m, respectively, which are smaller than the sensing field of a single cluster, the LDS in this paper is used to investigate the node-selecting strategy according to their relative linear distance to central node in whole sensing field in effect.  
For comparison purposes, we do not set a loss-threshold when running the IPSA algorithm. Instead, we tried different numbers of active participants, and plot the results as different percentages of participants in idle mode of all participants. Only lumped approximation errors corresponding to the testing data set are reported. The results are summarized in Figure 7.  
It is obvious that IPSA demonstrates better performance than LDS and RS. Given the fraction of participants selected to idle mode \(\lambda\), IPSA causes less approximation error than LDS and RS, indicating better sensing data quality. Larger \(\lambda\) will produce greater performance differences between them. From Figure 7, we can also observe that RS performs better than LDS does. According to [7], the sensing coverage degree of the LDS scheme is slightly smaller than that of the RS scheme. This could be due to  
Figure 7: Approximation errors comparison of IPSA _vs._ RS, LDS.  
the principle of LDS: allowing participants that are far away from the central participant turn to idle mode with higher probability. Border areas are not likely to be covered with active participants as a result. Less coverage degree would probably result in worse data quality.  
To obtain the same information integrity, IPSA could select more participants and put them in idle mode, thus saving more energy. Referring to Figure 7, with the constraint that the approximation error is kept below 2.5% (0.025), the IPSA method needs fewer than 30% of participants (15 participants in this case) than would be required by the RS (22 participants) or LDS (29 participants).  
Figure 8 shows the distribution of active participants by these three selection schemes. The results shown in Figure 7(a) further illustrated the in-homogeneous spatial correlations among sensing data as the selected active participants are cluttered at the top of the sensing field.  
Figure 8: Active participants’ distribution by IPSA, RS, and LDS. (**a**) IPSA; (**b**) RS; (**c**) LDS.