Advanced Mathematics and Numerical Modeling of IoT

10 20 30 40

0

10

20

30

40

50

60

70

80

90

100

Success allocation ratio (%)

Number of nodes

MAB

LAB

Figure 15: Successful allocation ratio.

10 20 30 40

0

50

100

150

200

250

Allocation completion time (s)

Number of nodes

SO = 7

SO = 6

SO = 5

Figure 16: Allocation completion time.

according to the network size. Moreover, even though the
network size is extended during run time, it is possible to
dynamically change the expected number of slots in the
process of network formation since each beacon contains BO
and SO information.

7. Conclusion

In this paper, we introduced IEEE 802.15.4e DSME beacon
scheduling, evaluated its validity and performance, and
proposed a concrete design model. Through experiments, we
found some problems in the pure DSME beacon scheduling
algorithm by analyzing results. Therefore, based on the
results, we revised the pure DSME beacon scheduling algo-
rithm step by step and proposed an enhanced DSME beacon

scheduling including new features: limited permission noti-

fication and a repetitive SAD architecture.

The proposed E-DSME model is expected to contribute

to design and modeling of beacon scheduling for large-scale

sensor networks or IoT sensor domains.

Conflict of Interests

The authors declare that there is no conflict of interests

regarding the publication of this paper.

Acknowledgments

This paper is extended and improved from a paper accepted

at the KCIC-2013 conference. This work was supported by

the Basic Science Research Program through the National

Research Foundation of Korea (NRF) funded by the Ministry

of Education, Science and Technology (2012R1A1A2041271).

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