Computational Intelligence - August 2012 - 21

B. Type-2 FLSs Applications to
Wireless Sensor Network Lifetime Analysis

C. Type-2 FLSs for Application in Cyber Security

50
45
40
35
30
25
20
15
10
5
0

VI. Type-2 FLSs Application to the Healthcare
and Medical Domains
A. Type-2 FLSs Applications to Diabetic Diet
Recommendation

In [20], researchers from Changhua Christian Hospital, National University of Tainan, Taiwan and University of Essex, UK,
developed a type-2 fuzzy FLS ontology applied to personal
diabetic diet recommendation.

Type-1
Type-2

0

Average Quality (dB)

In [19], a previously developed type-1 fuzzy logic based anomaly detection network security cyber sensor was extended by
relying on type-2 fuzzy logic. The resulting system was experimentally tested incorporating a TOFINO network security
cyber sensor (see Fig. 11) based on a network test bed schematically represented in Fig. 12.
The paper showed how the increased amount of information in terms of the uncertainty flow in the type-2 FLS
(though the width of the type-2 reduced fuzzy output set) is
highly relevant in informing the judgement about a specific
state of cyber security and is an advantage not available when
employing type-1 FLSs. An example of the results in [19] is
shown in Fig. 13.

20

40
60
Noise Level (%)
(a)

50
45
40
35
30
25

80

(a)

100

(b)

FIGURE 9 Received video frame after a 40% noise level addition to
delay measurements with: (a) a type-1 FLS and (b) a type-2 FLS [16].

Type-1
Type-2
0.03

20
15
10
5
0

20

40
60
Noise Level (%)
(b)

80

100

FIGURE 8 The performance of type-1 and type-2 FLSs for an
increasing noise level, average against (a) packet loss rate and
(b) video quality [16].

Lifetime Normalized
to Upper Bound (MSE)

Packet Loss (%)

As stated in [18], a wireless sensor network consists of a
number of small, energy-constrained nodes. Basic components of a sensor node include a single or multiple sensor
modules, a wireless transmitter-receiver module, a computational module and a power supply module. Such networks
are normally deployed for data collection in areas where,
post initial deployment, human intervention may not be
possible to recharge or replace node batteries, resulting in
limited network lifetime. Most applications have pre-specified (minimum) lifetime requirements, thus making the reliable estimation of lifetime of such networks prior to
deployment a necessity.
In [18] a type-2 FLS was applied to the analysis of the
lifetime of a wireless sensor network. A type-2 FLS
(referred to as FLLE2) was compared against a type-1 FLS
(referred to as FLLE1) as well as against a non-fuzzy system (referred to as the first node die method). Fig. 10
shows the results of the comparison. It shows that the
type-2 FLS based system (FLLE2) outperforms the non
fuzzy "first-node-die" method and the type-1 FLS based
system FLLE1 in terms of better estimation [lower Mean
Square Error (MSE)].

First Node Die
FLLE1 Estimation
FLLE2 Estimation

0.025
0.02
0.015
0.01
0.005
0

1

1.5

2

2.5 3 3.5 4
Observing Windows

4.5

5

FIGURE 10 Comparing the type-2 FLS, type-1 FLS and the non fuzzy
"first node die" method [18].

AUGUST 2012 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

21



Table of Contents for the Digital Edition of Computational Intelligence - August 2012

Computational Intelligence - August 2012 - Cover1
Computational Intelligence - August 2012 - Cover2
Computational Intelligence - August 2012 - 1
Computational Intelligence - August 2012 - 2
Computational Intelligence - August 2012 - 3
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Computational Intelligence - August 2012 - Cover3
Computational Intelligence - August 2012 - Cover4
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