## 5 Performance Evaluation  
In this section, we evaluate five performance metrics: task energy consumption, task duration, energy-delay product (EDP), hop count, and task distance. For each data point in each presented figure, we take the average of 30 simulation runs with a different selected random number of source nodes. The proposed SMIP approach was compared against the GIGM-MIP algorithm as well as the CL-MIP algorithm. For each MA itinerary, local closed first (LCF) algorithm was used to determine the visited source nodes order. The LCF algorithm searches for the next node with the shortest distance to the current node. The sink node is the starting point of the main MA itinerary, while the starting point of the SMA itinerary is the point where the main MA made spawn. The sink node is the ending point for both itineraries (main MA and SMA). Figure 4 shows the impact of the number of source nodes on the task energy consumption. The task energy consumption is the energy spent for transmitting, receiving, and exchanging control messages to perform the data collection task from all source nodes. In this figure, when the number of source nodes increased, the energy consumption of the CL-MIP algorithm goes high from 0.32 to 1.5 J / Task. The CL-MIP algorithm consumes high energy due to the distribution of a large number of \begin{table}
\begin{tabular}{c c} \hline \hline
**Network Parameters** & **Value** \\ \hline Network’s Terrain & 1000 m \(\times\) 500 m \\ Number of deployed nodes & 800 \\ Number of source nodes & 10–40 \\ Transmission range & 60 m \\ Raw data size & 1024 bits \\ \hline
**MA Parameters** & **Value** \\ \hline MA processing code & 1024 bits \\ MA accessing delay & 10 ms \\ Raw data reduction ratio & 0.8 \\ Aggregation ratio & 0.9 \\ Data processing rate & 50 Mbps \\ Data payload threshold & 1500 bits \\ \hline
**SMA Parameters** & **Value** \\ \hline SMA processing code & 128 bits \\ SMA accessing delay & 10 ms \\ Data processing rate & 50 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters of SMIP approach. MAs to the network which leads to an increase in the number of MA's hops. GIGM-MIP algorithm consumes less energy than CL-MIP algorithm when the number of source nodes increases. This is because GIGM-MIP algorithm distributes the data gathering task among the MAs in each partition. On the other hand, the proposed SMIP approach achieves 61.8% and 13.2% energy decrease when compared to CL-MIP and GIGM-MIP, respectively. Significantly, this achievement is obtained for two reasons. The first is the minimization in the number of MA hops. The second reason is that the processing code (aggregation code) of the main MA is carried once to each partition, while in GIGM-MIP algorithm, each partition may have multiple processing codes carried by the MAs. Additionally, in SMIP approach, the accumulated data of the main MA is sent back to sink separately by the SMA, while in the GIGM-MIP algorithm, each MA has to carry its accumulated data to the sink. In a SIP algorithm, the task duration is the average delay from the time when the MA is dispatched by the sink to the time when MA returns back to the sink. In MIP algorithms, however, since multiple MAs are dispatched to the network and work simultaneously, the task duration of the MIP algorithm is the delay of the MA that returns back to the sink at last. In the proposed SMIP approach, the task duration is the delay of either main MA or SMA that returns back to the sink at last. As depicted in Figure 5, the proposed SMIP approach achieves a significant improvement in terms of task duration. This improvement is about a 39.4% and 7.04% decrease of task duration as compared to CL-MIP and GIGM-MIP algorithms, respectively. In both CL-MIP and GIGM-MIP algorithms, each MA should travel along its itinerary starting the migration from the sink to perform the data collection process from the source nodes, and then returns back to the sink with the accumulated data.