Figure 11: Total energy spent by the scanning devices dur-
ing the discovery procedure when the number of inquiring
devices remains the same and the number of scanning de-
vices increases.
Figure 12: Total elapsed time for all inquiring devices to
find a scanning device during the discovery procedure when
the number of inquiring devices remains the same and the
number of scanning devices increases.
D2D technologies were not originally conceived to
support such a massiveness of devices trying to con-
nect with each other. Other D2D technologies were
born to handle mMTC scenarios, but they are not stan-
dardized yet. Therefore, it is useful to analyze, test,
and assess the available D2D technologies to check
out the technology with the best performance during
the discovery procedure, which is the process with
more signaling.
In this work we compared the discovery pro-
cedures’ performances of Wi-Fi Direct and Clas-
sic Bluetooth. Then, we compared Classic Blue-
tooth with BLE. The results show that Classic Blue-
tooth was 5 times faster than Wi-Fi Direct during the
devices’ discovery. However, Classic Bluetooth is
slower than BLE. BLE also experiences fewer col-
lisions and consumes less energy than Classic Blue-
tooth during the devices’ discovery. BLE allows that
around 100 inquiring devices find at least 1 scanning
device out of 10 scanning devices in less than 10 sec-
onds. BLE also allows that around 10 inquiring de-
vices discover at least 1 scanning device out of 100
scanning devices in less than 1 second, in contrast
with the 10 seconds the Classic Bluetooth takes to
do the same task. Therefore, BLE is faster and con-
sumes less energy than Bluetooth, and Wi-Fi by tran-
sitivity. For these reasons, BLE is one of the most
promising D2D communication technologies—out of
the three analyzed in this work—for enabling mMTC
in the Next-Generation networks. However, BLE is
a very short-range D2D technology, and it has some
security vulnerabilities during the pairing procedure
which can be overcome by other D2D technologies
like LTE-Direct.
This work was partially supported by
RNP, with resources from MCTIC, Grant No.
01250.075413/2018-04, under the Radiocommuni-
cation Reference Center (Centro de Refer
ˆ
encia em
Radiocomunicac¸
˜
oes - CRR) project of the National
Institute of Telecommunications (Instituto Nacional
de Telecomunicac¸
˜
oes - Inatel), Brazil.
REFERENCES
Ali, A., Hamouda, W., and Uysal, M. (2015). Next gener-
ation m2m cellular networks: challenges and practi-
cal considerations. IEEE Communications Magazine,
53(9):18–24.
Camps-Mur, D., Garcia-Saavedra, A., and Serrano, P.
(2013). Device-to-device communications with wi-fi
direct: overview and experimentation. IEEE wireless
communications, 20(3):96–104.
Cho, K., Park, G., Cho, W., Seo, J., and Han, K. (2016).
Performance analysis of device discovery of bluetooth
low energy (ble) networks. Computer Communica-
tions, 81:72–85.
Duflot, M., Kwiatkowska, M., Norman, G., and Parker, D.
(2006). A formal analysis of bluetooth device discov-
ery. International journal on software tools for tech-
nology transfer, 8(6):621–632.
Khan, M. A., Ch
´
erif, W., Filali, F., and Ridha, H. (2017).
Wi-fi direct research - current status and future per-
spectives. Journal of Network and Computer Applica-
tions, 93.
Liu, J., Chen, C., and Ma, Y. (2012). Modeling and perfor-
mance analysis of device discovery in bluetooth low
energy networks. In 2012 IEEE Global Communica-
tions Conference (GLOBECOM), pages 1538–1543.
IEEE.
Sun, W., Yang, C., Jin, S., and Choi, S. (2016). Listen chan-
nel randomization for faster wi-fi direct device discov-
ery. In IEEE INFOCOM 2016-The 35th Annual IEEE
International Conference on Computer Communica-
tions, pages 1–9. IEEE.
A Comparison among Wi-Fi Direct, Classic Bluetooth, and Bluetooth Low Energy Discovery Procedures for Enabling Massive Machine
Type Communications
169