IEEE Consumer Electronics Magazine - July 2018 - 59

[130, 30, 70, 170, 170, 130]. The output of the f ^ DED ih function is matrix R M # N , where M is the number of devices and N
is the length of the modulation code. Each row denotes the
value of the spread signal of each device. We consider a Z 1 # M
unit matrix with all 1s, where M is the number of devices. We
define the g (R) function as
N1 # N = Z1 # M RM # N
^N hij =

N

The FS-OpenSecurity model
improves network security in
reducing overhead on the SDN
controllers by separating the
control and monitoring functions.

/ Z ik R kj, i = 1 and 4 # j # N

k=1

g ^ R h = PD 1 # N + N 1 # N ,

(2)

where ^N hij is the value of output matrix obtained by multiplying matrix R M # N by matrix Z 1 # M at i and j, and Z ik and
R kj are the values of unit matrix Z 1 # M and output matrix
R M # N at i, j, and k. Here, we consider the minimum length of
the modulation code for all of the devices to be four to reduce
the correlation between the two codes. PD 1 # N is a matrix
with padding data. In this case, we consider the padding data
to be equal to 100 to adjust the signal level from 0 to 255, so
as to fit the 8-bit data depth, which results in comparable
signal levels.
At the receiver end, the green-colored LED ray signal is
separated using the green color optical filter from the RGB
LED rays signal. As mentioned previously in this section, the
transmitted output data signal is [130, 30, 70, 170, 170, 130],
which contains the green color magnitude of device number 1
and device number 2. The received signal is depadded using
the h ^ M h function in (3):
h ^ M h = M 1 # N - PD 1 # N ,

(3)

where M 1 # N matrix represents the received data signal and
PD 1 # N matrix is the padding data.
After depadding the received data signal, we despread the
data signal parallel for each device data using the respective
modulation code by utilizing the f ^ DED ih function in (1). In
this case, the data signal after depadding is [30, −70, −30,
70, 70, 30]. After despreading the data using the device's
own modulation code, device number 1 and device number 2
will get [−30, 70, −30, 70, 70, −30] and [30, 70, 30, 70, 70,
30], respectively.
After de-spreading the received data signal, using the
p ^Qh function in (4), we obtain the values 20 and 50 for
device number 1 and device number 2, respectively, at the
receiver end:
N

/ Q ij

p i ^Qh =

j=1

N

, 1 # i # M,

(4)

where Q M # N is the output matrix of the f ^ DED ih function, i is
the device's identification, M is the total number of devices,
and N is the length of the modulation code. In this case, the
value of p i (Q) for device number 1 and device number 2 will
be 120/6 and 300/6, respectively, which are equal to 20 and
50, respectively.

BLOCkChain netwOrk inFrastruCture
In the DistArch-SCNet model, to achieve simplicity and efficient communication with each IoT-forwarding device in a
smart city, all of the controllers in the IoT network are interconnected in a distributed blockchain network manner. To
provide policy-based network management, increased agility,
simpLi-Fied design, and more secure automation in more liberal IoT workflow systems, each controller in the distributed
blockchain network implemented the FS-OpenSecurity software-defined networking (SDN) model proposed in our previous work [14]. The FS-OpenSecurity model improves
network security in reducing overhead on the SDN controllers by separating the control and monitoring functions.
In the blockchain technique, to create a more competent
mining process, a mining pool is assembled; this pool is a
group of miners putting together their computing capabilities.
Here, all of the SDN controllers in our proposed distributed
blockchain network architecture act as a miner. To achieve
high efficiency during the operation of the blockchain network, we must choose the best controller based on statistical
data that are generated and collected.
To optimize the performance of miners, we propose an efficient controller-hopping algorithm (ECHA) using the discrete
firefly metaheuristic approach. As an algorithm inspired by
nature, the firefly algorithm was originally proposed by Xin-She
Yang [15]. The ECHA method takes into account the generated
and collected statistical data during the online operation. To
determine which controller or group of controllers can be mined
more efficiently, the statistical data are used to calculate the average generation time and standard deviation among pools.
ECHA captures the time taken by the controller to generate
the last block (response). We consider C (t) = C i (t), 6 i = 1,
2, f n to be the capture time taken by controllers in the distributed network, where C i (t) is the capture time taken by i
controller to generate the last block. t avg is the average block
generation time by every controller, and it can be calculated
by the sum of total capture time taken by all of the controllers
and the total number of controllers in the distributed blockchain network. { is the standard deviation of the generation
time of each controller.
Algorithm 1 represents the ECHA offline algorithm for
efficiently choosing the controller to send a request. It works
offline to achieve efficiency and reduce the runtime overhead.
The ECHA offline algorithm takes C (t), t avg, and { parameters as input and gives the C i chosen controller as output to
send a request. In the ECHA offline algorithm, it compares
july 2018

^

IEEE Consumer Electronics Magazine

59



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