# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 5 Performance Evaluation  
In this section, experiments using real-life environmental data (sensor network data set in Intel-Berkeley Lab [32]) are performed to: (i) evaluate the properties; (ii) prove the feasibility; and (iii) compare the performance of the proposed algorithm. All experiments use the following three measurement metrics:  
* _Number of selected active participants_: the number of selected active participants generated by the algorithm for the whole task set in each sensing interval.
* _Number of participated tasks_: the number of participated tasks for each participant for the whole task set over the sensing period.
* _Approximation error_: the expected approximation error per participant which is a criterion to measure the loss of data integrity caused by putting a portion of the participants into idle mode.  
All results reported here are the averages from multiple runs over different periods from the Intel-Berkeley Research Lab data set. To simulate cooperative sensing in mobile crowd-sourcing systems, we use real life environmental monitoring data from the Intel-Berkeley Lab sensing dataset, in which the sensing samples are humidity and temperature readings taken from 54 wireless sensor nodes between 28 February and 5 April 2004. The sampling interval is 31 s per sample. A layout of these sensors is shown in Figure 2(a). The reason we pick the Intel-Berkeley Lab sensing data set is that it is the only environmental data available to us which has fine-grained geographically-distributed and temporally consecutive environmental observations. As shown in Figure 2(a), the whole sensing field in the Intel-Berkeley Lab is divided into 112 (14 \(\times\) 8) grids of equal size served as the sensing task unit areas. The length and the width of the whole sensing field are 41 m and 32 m respectively. Since some grids are missing sensor nodes, they are treated as obstructive areas and no-task areas which are excluded from this experiment, as shown in Figure 2(b). Specifically, there is no task releasing in either obstructive areas or the no-task areas, or participant movement in the obstructive areas. In this simulation experiment, there are 100 participants moving in the Intel-Berkeley Lab. All the participants are randomly placed within the whole area. The random waypoint [34] model is selected as the mobility model, with varying maximum mobility speeds (_i.e._, 0.25, 0.5, 0.75, 1 m/s), and the same pause time of 900 s. We use 11 days of data in which there are few sensor nodes suffering missing data records from the dataset to perform various experiments. In this simulation experiment, we only choose subsets of grid sectors as working-grids and subsets of corresponding participants in the working-grids as active participants in our participatory system for every sensing task interval (_i.e._, 31 s). The selected active participants will sense data around their grids during the assigned time interval. We use the 11-fold cross validation to examine the approximation errors of the 11 days' worth of Intel-Berkeley Lab sensing data. Specifically, we treat one day as the testing time unit. Then, one day's data from the 11 days' data is selected in turn as the testing data and the remaining 10 days as training data. During the testing period, if a participant is selected as being put into idle mode, its data readings then will only be used to compute the approximation error for the purposes of performance comparison.