## 5 Performance Evaluation
This process increases the number of MA's hops which leads to a large delay, especially when the source nodes are distributed sparsely over the network. Meanwhile, the proposed SMIP approach reduces the task duration by constructing the shortest MA itineraries with fewer MA hops. For time-sensitive applications (e.g., wireless multimedia sensor networks and video sensor networks [28]), it is important to consider both energy consumption and task duration (energy-delay product, EDP) in order to evaluate the performance of the proposed approach. EDP can be calculated as EDP = energy \(\times\) delay, such that the smaller the value of EDP, the better performance obtained. Figure 6 illustrates the overall EDP performance of CL-MIP, GIGM-MIP, and SMIP. Due to the high energy and task duration, CL-MIP has the largest value of EDP, which yields the worst overall performance when compared to GIGM-MIP and SMIP. Evidently, the proposed SMIP approach outperforms GIGM-MIP  
Figure 4: The impact of number of source nodes on energy consumption. with a decreased EDP value up to 17.8%, and continues to achieve better performance even when the number of source nodes increases. This verifies the effectiveness of the proposed approach. Figure 7 illustrates the accumulated hop counts, which includes both source and intermediate nodes of the compared algorithms. In SIP algorithms, the hop count is defined as the average hop count of the MA itinerary. In MIP algorithms, the average hop count is the accumulated hop counts of all the MAs' itineraries. As shown in Figure 7, the results indicate that the CL-MIP algorithm produces the largest hop count among other MIP algorithms by constructing many MA itineraries, which returns a large number of hops. Besides, the GIGM-MIP algorithm accumulates fewer hops compared to the CL-MIP algorithm. On the other hand, the proposed SMIP approach has the lowest number of hops due to the shortest distance utilized by the MAs as well as fewer itineraries. Figure 5: The impact of the number of source nodes on task duration. Figure 6: The impact of the number of source nodes on energy-delay product (EDP). Figure 8 shows the evaluation of the task distance metric. It represents the accumulated distance travelled by all MAs until they return to the sink. When the number of source nodes increases, the accumulated distance spent by all MAs increases due to the construction of many intermediate nodes along with MAs\({}^{\prime}\) itineraries. Note that the intermediate nodes consume more energy than the source nodes due to data forwarding, while the source nodes are visited only once for data collection process. As shown in Figure 8, the proposed SMIP approach achieves the shortest distance compared to GIGM-MIP and CL-MIP algorithms. This achievement is related to the fact that the proposed SMIP approach utilizes fewer intermediate nodes when constructing the MAs itineraries. This indicates that the proposed approach can be suitable for target tracking problems in wireless sensor networks where the distance is a very important factor. Figure 8: The impact of the number of source nodes on distance traveled by all MAs. Figure 7: The impact of the number of source nodes on hop counts.