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255

0.02

rr1

random

ENR

ECJ(R=inf)

ECJ(R=0)

ε
acc

r
r
1

random

ENR

ECJ(R=inf)

ECJ(R=0)

0.018

250

0.016

245

0.014

240

0.012

0.01

235

0.008

230

0.006

225

0.004

220

0.002

0

215

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

c

c

(a) Error-rate

(b) Computation time
T

Figure 25. Error rate and Computation time for colluding rate
c
(
acc
=0.01
,
s =0.1
,

f =0.35
,
q =0.1
,
p
d
=0
, with blacklisting)

2400

0.02

rr1

random

ENR

ECJ(R=inf)

ECJ(R=0)

ε
acc

rr1

random

ENR

ECJ(R=inf)

ECJ(R=0)

2200

0.018

2000

0.016

1800

0.014

1600

0.012

1400

0.01

1200

0.008

1000

0.006

800

0.004

600

0.002

400

0

200

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

pd

pd

(a) Error-rate

(b) Computation time
T

Figure 26. Error rate and Computation time for defection rate
pd
(
acc
=0.01
,
s =0.1
,

f =0.35
,
q =0.1
,
P
up
(steady)=0.8
, with blacklisting).

In cases with blacklisting

Simulation results for VC systems with blacklisting are shown

in Fig.23 - Fig.26.

Fig.23 (a) and Fig.24 (a) shows the error rates of each scheduling method as a func-

tion of sabotage rate
s
and fraction
f
, respectively. These figures show error rates of

credibility-based voting are less than
acc
regardless of the used scheduling method. This

means credibility-based voting can guarantee the reliability condition
≤
eacc
for any
s

and
f
regardless of the used scheduling method.

Fig.23 (b) and Fig.24 (b) shows computation times of each scheduling method. As a

function of sabotage rate
s
, the computation time of each method increases at first at
s
of

0.2, then retains almost identical values for larger
s
. This is true because, when
s
is large,

all saboteurs are caught until the end of the computation and all results produced by the