We also develop a simulator to evaluate the performances of the proposed dispatch scheme. We set up a sensing field as a 100 × 100 grids, on which there may be several obstacles. We randomly pick up 50, 100, 150, 200, 250, and 300 grids (excluding those grids representing obstacles) as event locations. The values of ∆m (that is, the cost to move one grid-length) and ∆t (that is, the cost to make a 90-degree turning) are set to 1 and 2, respectively. We mainly compare our priority-based dispatch scheme with the TSP approximate solution [16].
Fig. 5.1 shows the comparison of the total moving time of the mobile sensor under our priority-based dispatch scheme and the TSP approximate solution. We can observe that the TSP solution has a larger total moving time, because it is only an approximate solution. Our priority-based dispatch
scheme adopts a greedy approach, so that it can have a smaller total moving
Figure 5.1: Comparison on the total moving time of the mobile sensor.
Fig. 5.2 gives the comparison of the costs (by Eq. (3.3)) of the Hamilton cycles found by our dispatch scheme and the TSP solution. As can be seen, our priority-based dispatch scheme can find a Hamilton cycle with a cost smaller than that of the TSP solution. This indicates that event locations with higher priorities could be visited first.
Fig. 5.3 shows the waiting time of event locations, by adopting our priority-based dispatch scheme. In this experiment, we randomly select 100 locations as event locations. In Fig. 5.3, we can observe that event locations with higher priorities can have a shorter waiting time, which satisfy our goal.
1000 1500 2000 2500 3000 3500 4000
50 100 150 200 250 300
number of event locations
cost
priority-based scheme TSP solution
Figure 5.2: Comparison on the costs of the Hamilton cycles.
0
(a) waiting time of each event location
0
(b) average waiting time of each 10 event locations
Figure 5.3: Waiting time of event locations.
Chapter 6
Conclusions
In this thesis, we have proposed a scenario to use mobile sensors to de-tect events in a region without any deployment of wireless sensor network.
Mobile sensors will be first requested to conduct fully scanning of the region to collect rough information of the environment and to identify potential event locations. Then, mobile sensors will be dispatched to visit these event locations according to their priorities. We have proposed a priority-based dispatch scheme for mobile sensors to visit event locations. In particular, our dispatch scheme can reduce the total time for the mobile sensor to visit all event locations, while event locations with higher priorities can be vis-ited first. In this way, those event locations with higher priorities will suffer from lower waiting time. Simulation results have shown that our proposed dispatch scheme outperforms the approximated solution of TSP, on both the
total moving time of the mobile sensor and the average waiting time of event locations with higher priorities. In this thesis, we have also implemented a prototyping system to realize our dispatch idea. Such a prototyping sys-tem can be used to monitor CO2 densities in an indoor environment. The prototyping experience is also reported in this thesis.
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