5.4 Simulation Results
5.4.4 Evaluation of DTA-mhop Algorithm
local tasks decreases the proportion of the global tasks in the whole local and global DAGs.
The consequence is that the gain by usingGlobal TAdecreases from 136.63% to 123.89%. The corresponding algorithm runtime ofGlobal TAis stable all the time while theLocal TAconsumes more time asKincreases (see Fig. 5.10b). AlthoughSJTAprovides the static solution which is easier to implement in the slave and master nodes, DJTAachieves longer network lifetime and requires much less execution time. For example, when they are 20 local tasks, executingDJTA needs 0.037 seconds and achieves 1060.23% network lifetime improvement, while executing SJTArequires 0.528 seconds and extend the network lifetime 660.63% longer.
In addition to the local tasks, the impact of the number of global tasks, H, on the proposed algorithms is further estimated. The results are shown in Fig. 5.11. The gains of Global TA, SJTAandDJTAon extending the network lifetime over the no-scheduling strategy increase while the gain of Local TAis slightly decreasing. This is due to the fact that Local TAonly focuses on the workload distribution of local tasks. Correspondingly, its algorithm runtime remains the same as H increases from 15 to 30, while the others spend more time on executing the algorithms (see Fig. 5.11b). Besides,DJTAperforms better than SJTAon the network lifetime increase and execution time, which are consistent with the above-mentioned results. Note that, comparing withT = 200 rounds of local tasks, the workload of the global tasks is still relatively very small which leads to very small gains for Global TA. Thus, the next set of simulations adjusts the workload proportion of the global tasks by changing its execution period to evaluate the algorithm performance.
As the above depicted, changing the number of global tasks does not affect the algorithm too much when the execution periodT = 200. The impact ofT on the performance of the proposed algorithms is investigated in this part and the results are illustrated in Fig. 5.12. It is obvious that Global TA extends the network lifetime significantly whenT is very small, i.e., the workload proportion of the global tasks is relatively large. It improves the network lifetime in average by 173% whenT = 50. As the interval length ofT increases, the performance of Global TA goes down. On the contrary,Local TAextends the network lifetime longer whenT changes from 50 to 1000. Its performance is closer toSJTAandDJTAdue to the decreasing global task workload.
SinceSJTAandDJTAtake both the local and global tasks into account, they prolong the network lifetime dramatically all the time. Meanwhile, their algorithm execution times decrease, as the global task workload is reducing whenT increases.
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Number of global tasks, H
Networklifetimeincrease
Local TA Global TA
SJTA DJTA
(a)
15 20 25 30
10−2 10−1 100
Number of global tasks,H
Algorithmruntime(sec.)
Local TA Global TA
SJTA DJTA
(b)
Figure 5.11: Effect of the number of the tasks in global application on (a) network lifetime increase and (b) algorithm runtime (there are 10 slave nodes in the network and 10 tasks in local application, and T = 200 rounds).
50 100 200 500 1000
100 100.5 101 101.5
The interval length of T
Networklifetimeincrease
Local TA Global TA
SJTA DJTA
(a)
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10−2 10−1 100
The interval length of T
Algorithmruntime(sec.)
Local TA Global TA
SJTA DJTA
(b)
Figure 5.12: Effect of the execution period of the global application on (a) network lifetime increase and (b) algorithm runtime (there are 10 slave nodes in the network, 20 and 10 tasks in global and local applications, respectively).
for cluster based WSNs,CDTA:
• No scheduling strategy: each sensor node does not share any workload of its neighboring nodes and just executes the first task of itself.
• CDTA: firstly, CDTA algorithm is executed to obtain the task allocation solutions by considering that each sensor node connects with the sink node by one hop; then the sensor nodes apply the obtained task allocation solutions and just forward the data of their neighboring nodes.
The increase of the network lifetime with respect to the no-scheduling strategy and the algorithm runtime are investigated by changing: a) The number of sensor nodes, n; b) The number of tasks,K. The configuration parameters are summarized in Table 5.4, and only one parameter is changed in each experiment.
Table 5.4: Configuration parameters in the simulations to evaluate the performance of the task allocation algorithm for multi-hop mesh networks.
Parameters Values
Default Varied
Number of sensor nodes,n 20 {10, 20, 30, 40, 50}
The number of tasks,K 10 {5, 10, 15, 20}
Fig. 5.13 shows the effect of the number of the sensor nodes on the algorithm performance. It is obvious that the improvements of the network lifetime of bothDTA-mhopandCDTAincrease as the number of sensor nodes, n, changes from 10 to 50. As depicted in Fig. 5.13a, DTA-mhop achieves the gain of 299.08% network lifetime improvement when there are 10 sensor nodes while this gain increases to 908.61% whenn equals 50. The proposed task allocation algorithms can efficiently balance the workload among the sensor nodes and the sink node, which makes the network last longer than using the no-scheduling strategy. Moreover, theDTA-mhop performs much better thanCDTA, and the superiority ofDTA-mhopbecomes more significant as nincreases. This is due to the fact that theCDTAconsiders the sensor nodes are connected with the sink node by one hop, which does not consider the energy cost of multi-hop transmission.
Asnincreases, the workload of multi-hop transmission becomes heavier. Thus, it can be seen in Fig. 5.13a that DTA-mhop extends the network lifetime from 113.48% to 128.25% longer thanCDTA whennequals 10 and 50, respectively. Further on, Fig. 5.13b shows that the time requirements for executing CDTA and DTA-mhop algorithms are very close and both of them increase asnchanges from 10 to 50. Specifically, the algorithm runtime ofDTA-mhopincreases in average from 0.018 to 0.047 seconds and the runtime ofCDTAgoes up from 0.012 to 0.044
seconds. Since the DTA-mhop algorithm uses the summation of the task allocation solutions for each sensor node, its complexity is related to the number of sensor nodes as well asCDTA algorithm.
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Number of sensor nodes, n
Networklifetimeincrease
Multi-hop mesh WSNs
CDTA DTA-mhop
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10−4 10−3 10−2 10−1
Number of sensor nodes, n
Algorithmruntime(sec.)
Multi-hop mesh WSNs
CDTA DTA-mhop
(b)
Figure 5.13: Effect of the number of sensor nodes on (a) network lifetime increase and (b) algorithm runtime ofCDTAand the proposedDTA-mhopfor multi-hop mesh WSNs (there are 10 tasks in each individual application).
In addition to estimate the effect of the number of sensor nodes, another set of simulations is conducted to investigate the algorithm performance by changing the number of the tasks.
As demonstrated in Fig. 5.14a, the gains of the network lifetime improvement by using DTA-mhop andCDTA task allocation algorithms slightly become larger. Moreover, the superiority ofDTA-mhop overCDTA decreases as the number of tasks increases, e.g., DTA-mhop extends the network lifetime in average from 122.79% to 113.8% longer thanCDTAwhen the number of tasks changes from 5 to 20. This can be explained by the fact that the proportion of the workload of increased tasks becomes larger, which makes the effect the multi-hop transmission smaller. As the above analyzed that the complexities ofDTA-mhopandCDTAare the same, their algorithm runtimes are still very close as shown in Fig. 5.14b. This phenomenon is consistent with the results in Fig. 5.13b.
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Number of tasks, K
Networklifetimeincrease
Multi-hop mesh WSNs
CDTA DTA-mhop
(a)
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Number of tasks, K
Algorithmruntime(sec.)
Multi-hop mesh WSNs
CDTA DTA-mhop
(b)
Figure 5.14: Effect of the number of tasks in each application on (a) network lifetime increase and (b) algorithm runtime ofCDTAand the proposedDTA-mhopfor multi-hop mesh WSNs (there are 20 sensor nodes in the network).