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1. SPECIAL SECTION ON INDUSTRIAL SENSOR NETWORKS WITH
ADVANCED DATA MANAGEMENT: DESIGN AND SECURITY
Received March 30, 2015, accepted May 18, 2015, date of publication June 1, 2015, date of current version June 9, 2015.
Digital Object Identifier 10.1109/ACCESS.2015.2439034
A Hybrid Security and Compressive Sensing-Based
Sensor Data Gathering Scheme
JIN QI1, XIAOXUAN HU2, YUN MA1, AND YANFEI SUN2
1School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Corresponding author: Y. Sun (sunyanfei@njupt.edu.cn)
This work was supported in part by the National Natural Science Foundation of China under Grant 61003237, in part by the China
Post-Doctoral Science Foundation Funded Project under Grant 2015M571790, and in part by NUPTSF under Grant NY213047,
Grant NY213050, Grant NY214102, and Grant NY214098.
ABSTRACT The use of cryptographic techniques such as encryption and hashing largely increases the
energy consumption of sensors, which aggravates the original critical energy constraint problem of wireless
sensor networks (WSNs). To reduce the burden of sensors, compression can be utilized. Since the traditional
chaos-based schemes are not directly applicable for WSNs, we present a hybrid security solution. The hybrid
security consists of 8-bit integer chaotic block encryption and a chaos-based message authentication codes.
It aims to promote the security and performance of data gathering. In this paper, a hybrid security and
compressive sensing-based scheme for multimedia sensor data gathering is presented. It has light security
mechanism and thus decreases the complexity and energy consumption of system. Performance analysis
about security and compression is carried out. The results show that our scheme is more applicable for
WSNs multimedia data gathering from security and compression efficiency.
INDEX TERMS WSNs, multimedia data gathering, chaotic block encryption, compressive sensing.
I. INTRODUCTION
Recent years, there is a strong interest in WSNs for
multimedia or secure communications [1], [2], which can
gather large volume sensitive data and thus provides the
impetus for extending the capabilities of WSNs for many new
and advanced applications. Security requirement of many
WSNs applications are normally fulfilled according to
confidentiality, authentication, integrity, and availability
with encryption and hashing technologies. But current
cryptographic algorithms charge high in computation and
memory, which impose a significant burden on the limited
energy sources of sensor nodes. This may cause the
application of state-of-the-art cryptographic algorithms
infeasible. A widely used network security method is
compressing data with data compression algorithms before
encryption. Data compression is a process of reducing the
amount of data, which removes redundancy, repeatability and
irrelevancy of original data. But traditional protocols such as
SSL cannot be applied directly to WSNs because they are
compute-intensive. To deal with this problem, a promising
approach is compressive sensing (CS) technology [3]–[5],
which implements data compression in WSNs.
CS has a low complexity for transmitting computationally
complex data while keeping high compression ratios for
sparse data.
Data gathering techniques based on CS have been lately
researched [6], [7], with the objective of minimizing the
total energy consumption when collecting data from
sensors. Since information contained in some multimedia
communication is sensitive and important, we have to
prevent any unauthorized access to this system. Commonly
encryption is incorporated into CS technique for security.
Symmetric cryptographic algorithms are usually applied in
WSNs for security, like AES and SKIPJACK. AES [8] and
SKIPJACK [9] both have the requirement for large storage,
which are not appropriate for the data gathering in resource-
limited WSNs. Thus we should consider a lightweight chaotic
encryption method. As a research focus, chaotic encryption
is a widely applied non-traditional encryption technology.
Chaotic-based cryptography is based on the complex
dynamics of nonlinear maps that are deterministic but simple.
Thus it can provide a secure and fast solution for data
protection, which can be appropriately applied in WSNs data
gathering. However, many literatures on chaos
encryption [10], [11] have poor efficiency. To increase
the encryption performance, we propose a hybrid security
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2. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
strategy that combines a chaotic block encryption and
light-weighted message authentication codes (MACs). It can
avoid the data redundancy resulting from padding in
encryption, and thus fit for WSNs communications with small
packets.
In this thesis, we address security problem for multimedia
sensor data gathering in WSNs. A hybrid security and
CS based sensor data gathering scheme is presented.
A performance analysis on security is carried out; also the
influence of different compression ratio is discussed.
The rest of the paper is organized as follows. Section II
present the hybrid security strategy. Section III introduces CS
in WSNs applications. Section IV proposes a HSCS based
sensor data gathering scheme. Conclusions are given
in Section V.
II. BACKGROUND AND RELATED WORK
In this part, we will present some related work, and mainly
focus on CS and MACs.
A. BACKGROUND OF CS
First, we will give a brief introduction to CS. Compressed
Sensing techniques [1]–[4] is not a new concept. But it
does bring us a good approach to recover a compressible
signal from under-sampled random projections, also refer
to as measurements. A compressible vector signal x∈RN
(x = [x1x2 . . . xN ]T ) is k-sparse if x has k non-zero elements.
In another case, x may be dense (most elements of x are
non-zeros) but can be considered as sparse in ψ domain
if x = ψθ and θ is a k-sparse vector. When applying CS
to a data gathering system, vector x represents data from
N sensors in the network. The gain when applying CS comes
from the shrinkage of the number of transmitting measure-
ments in comparing with the number of the original sensing
data. Vector y can be regarded as the measurement vector,
contains data sampled from N sensor readings: y ∈ RM
(y = [y1y2. . .yM ]T ), where M N [4]. For signal sampling,
the random measurements are generated by y = φx where
φ ∈ RM is the measurement vector with yi = N
j=1 ϕi,jxj,
where ϕi,j are all entries on the ith row of the projection
matrix ∅. For signal recovery, the number of measurement
for reconstruction of the original signal perfectly with high
probability is M = O(klogN/k) following the l1 optimiza-
tion problem given in [4].
ˆx = argmin x 1, subject to y = φx (1)
In case we need sparsifying matrix to make x sparse in ψ
domain (ψ can be DCT or Wavelet depending on the signal
properties).
ˆθ = argmin x 1, subject to y = φψθ (2)
Where θ 1 = n
i=1 |θi|. The l1 optimization can be
achieved by linear programming techniques, such as Basis
Pursuit [3].
Still, we need to put noise into consideration while
sampling and sending the measurements (in our WSN data
gathering case we collect measurements and send them to the
base station): y = φx + e, with [[e]]2 < e and recover:
ˆx = argmin x 1
, subject to y = φx 2
< e. (3)
B. BACKGROUND OF MACs
Message Authentication Codes (MACs) [14] are symmetric
cryptographic algorithms that provide data integrity and
the authentication of data origin. Data integrity enables the
recognition of any modification or manipulation of the
message during data communication. The authentication of
data origin provides confirmation that the message come from
the sender, who shares the used secret key with the receivers.
Due to the sharp demand for secure transmission,
communication systems can use MACs for security
consideration. MACs are constructed in such a way that any
modification of the message results in large changing of
original data in a MAC. This effect is known in cryptography
as ‘‘avalanche effect’’, which follows such law: every
modified message produces an incorrect MAC at the
verification. If the verification fails, the message is not
authentic and it is regarded as useless.
For multimedia application, the strong verification
condition for data authentication is not applicable, since the
digital content is continuously modified and manipulated as
a result of compression and conversion in these applications.
Any of these modifications would be considered as a forgery
in case of MAC verification. Therefore it is necessary to
research such technique that make the modifications of a
single or a few message bits do not result in any modification
of MAC.
In the last decade there are researches for the construction
of ‘‘robust’’ MACs [5], [6] that are less sensitive to modifica-
tions on messages. Literature [7] presented a MAC algorithm
for correction of data that used a different verification as
Soft Input Soft Verification (SISV). The received MAC and
the one recalculated of the received message are compared,
as by regular verification. But it is unnecessary to be equal
for the successful verification: which can also be successful if
one or two, or few bits of both compared MACs are different.
This algorithm is viewed as a basis of Soft Input Decryption.
Both algorithms are iterative and combine channel decoding
and cryptographic verification like this: the message is
corrected with both channel decoding and MACs. These
two papers are also referred in our paper, with more expansion
in data transmission efficiency.
III. HYBRID SECURITY STRATEGY
The hybrid security strategy adopted in this paper is based on
the research of MACs and chaotic block encryption that has
integer chaotic mapping.
A. DISCRETIZATION OF INTEGER CHAOTIC MAP
WSNs usually adopt embedded CPU without supporting of
complicated computation like float number operation. Thus
we need the discretization process for integer chaotic map.
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3. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
Here we adopt one-dimensional Logistic mapping and
transform it to an integer chaotic map that are discrete both
in time-domain and frequency-domain [13]. The chaotic
function of a Logistic mapping is:
xn+1 = ζxn(1 − xn), (4)
where xn is the result of the nth iteration. When system
parameter ζ is among the range of 3.57 to 4, the iteration
result of Logistics has such chaotic characteristics as noise
like [14]. There exists the equivalent form of (4):
xn+1 = 1 − αx2
n , (5)
where α ∈ [0, 2], xn ∈ [−1, 1]. (5) can be transformed to:
k2
xn+1 = k2
− α(kxn)2
, (6)
where k = 0. Set yn = k(xn + 1), then we get:
xn = yn/k − 1, xn+1 = yn+1/k − 1, (7)
Put the values of (7) into (6). When α = 2, we can
simplify (6) as:
yn+1 = 4yn − 2/k · y2
n, (8)
If we assign integer to all yn, it can be inferred that (8) are
the iteration formula in unsigned integer range with machine
word length. The computing of (8) only needs addition,
subtraction multiplication, and shifting in embedded system,
which is in favor of energy efficiency for WSNs. Experiments
show (8) has perfect stochastic characteristic under 32-bit
machine word. It can guarantee security and computing
performance in the meanwhile. Thus we choose 32-bit integer
chaotic series as the sub-key of the encryption in our
HSSC scheme.
B. CHAOTIC BLOCK ENCRYPTION
We use 8-bit block Feistel [13]. Each block of plaintext
is divided equally into low and high part, denoted as
L and H. Feistel round operation is achieved through the
XOR of Li−1 and Hi−1 with the help of round key ti and
F function. F function is an 8-bit integer chaotic iterative
that is computed as: expand the 4-bit low Li−1 to 8-bit, and
XOR it with 8-bit ti. The result (namely yn) is input into
c function and 8-bit integer chaotic iterations are carried out.
The output of iterations (namely yn+1) is divided equally into
low and high part, which will do XOR operation and generate
output f.
The structure of Feistel is:
Hi = Li−1
Li = Hi−1 ⊕ f,
(9)
Where each element is 4-bit. The security performance of
Feistel mostly roots from the nonlinearity characteristic in the
8-bit integer chaotic computation of encryption round process
of c function. Besides, sub-key is increased by increasing the
round number in Feistel structure, which can further promote
the security of the chaotic block encryption. The substitute
structure of 8-bit Feistel block encryption is simple and thus
is suitable for data gathering in WSNs.
C. MACs BASED ON CHAOTIC BLOCK ENCRYPTION
The system security cannot be well guaranteed with
encryption alone given the attacker know the seed
information. Thus we consider hash function to solve the
problem. Notice that hashing alone cannot guarantee integrity
as an imposter may hash and send spurious data with hash
function knowledge. However, a combination of hashing
and encryption is more robust to resist attack of imposter.
We utilize the cipher block chain mode (CBC) for hashing
in message authentication codes (MACs) computing.
As symmetric cryptographic algorithms, MACs [15] can
provide data integrity and the authentication of data origin.
Integrity checking enables the recognition of any modifica-
tion or manipulation of the data during transmission. The
authentication of data makes receiver ensure the message
originates by sender. Sender and receiver share the same
secret key.
For those communication systems that demand secure data
transfer, MACs can provide strong protection against
forgeries. Any modification of the message results in
changing about half of bit a MAC. Such effect is known
as ‘‘avalanche effect’’ in cryptography: each modified
message results in an incorrect MAC at the verification and
the authentication verification is not passed. Thus we
adopt MAC in our hybrid security scheme. Besides, any
modification of message will be considered as a forgery in
MAC verification. Whereas small changes of message bits
are possible in multimedia application of WSNs.
To degrade the sensitivity to modifications of MAC, we can
add software counter in the receiver comparing the received
MAC and the one recalculated of the received message.
If the non-matching cases are below the allowable level, the
received message is also regarded as safe.
FIGURE 1. Structure of MA-CBE.
The structure of our message authentication algorithm
based on 8-bit chaotic block encryption (MA-CBE) is
illustrated in Figure 1.
In Figure 1, CB is the cipher grouping process. F function
is defined as:
tn = Jn−1 mod xupper + xn
= Jn−1 mod xupper + 4tn−2 − 2/k · t2
n−2, (10)
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4. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
MA-CBE uses 32-bit integer Logistic chaotic map, whose
process is as follows:
Step I, divide the original message I into n single-byte
block I1, I2, . . . , In;
Step II, I1 is encrypted into J1 (it has the same length
with I1) with the original key t1 and iteration original
x1: J1 = CBt1 (I1);
Step III, t1 and J1, as the input parameters of R function, are
used to compute final output. Besides, J1 and x2 will generate
the key t2 that is required in next block;
Step IV, repeat step I∼ step III, until reach In;
Step V, Compute R function, obtain the final MAC with
length m bytes, labeled as MAC [m].
The construction of R function is: enlarge and mapping
each bit of the 8-bit Ji into the corresponding bit of the
m byte MAC [m]. Notice ti is used as the index Ind to
determine the subscript of target unit in MAC [m]. Ji is
defined as: all the bits are set zero except the ith bit of Ji
(reserved). For R function, each block carries out rotate
operations: traverse i from 1 to 8, which has two steps.
First, t rotate left for i-1 bits, from where intercept the former
h bits to construct integer ind. The value of h and m follows:
2h = m. Second, conduct such operation:
MAC[Ind] = MAC[Ind] ⊕ Ji, (11)
Our hybrid security strategy combines F function,
R function, and CBC mode, which enhances the diffusion
effect of the hashing and thus promotes the whole security
of our scheme.
IV. CS IN WSNs APPLICATIONS
The main idea behind CS describes the reconstruction of
signals from a small number of linear non-adaptive measure-
ments by using some optimization techniques [16]. Suppose
original signal X = [x1, x2, . . . , xn]T has a k-sparse represen-
tation under transform basis matrix (size n×n). It satisfies:
X = · S =
n
i=1
iSi, (12)
where S is a k-sparse column vector representation
of X (k n). Only k coefficients are needed to represent the
signal. Remaining n-k coefficients are equal to zero and we
can discard those coefficients without much perceptual loss.
In CS, we do not need to send the original signal X of size n.
Instead, we may send a sample measurement Y = φ · X
(size m = O(k log n)), where φ is a random sample basis
matrix of size m × n (m n). The original signal at
destination recovers properly by solving the convex
optimization problem:
min
˜S
˜S
lp
s.t. Y = φX = φ ˜S, (13)
where lp-norm for vector means v p = i |vi|p 1/p
.
By solving lp-norm, the problem gets transformed
into a linear problem which is quite straight forward.
After recovering the sparse vector ˜S from the above
optimization problem, the original signal X is recovered by
letting X = ˜S. The performance of CS heavily depends on
the sparse representation of the original signal, which means
how few sample measurements to recover signal are needed.
CS itself may intrinsically provide confidentiality, given that
the adversary does not know matrix φ [17].
V. HSCS BASED SENSOR DATA GATHERING SCHEME
CS has a low complexity for transmitting computationally
complex data while keeping high compression ratios
for sparse data. The Hybrid Secure and Compressive
Sensing (CSCS) based Sensor data gathering scheme in this
paper covers two operations: hybrid security (encryption +
hashing) and compressive sensing. They are performed to
reduce data volume and keep security.
A. APPLICATION SCHEME
For sensitive multimedia data gathering in WSNs, it is hard
to sustain sufficient security and low energy cost
simultaneously. Here we propose a hybrid security and
CS based (HSCS) scheme for sensitive correlative sensor data
collection. Firstly, the captured data are encoded within
sensor nodes. Then HSCS is followed: CS is conducted;
hybrid security strategy as encryption algorithm (chaotic
block encryption) and integrity checking (MACs) are
implemented before transmission.
The process of HSCS is as follows. After signal samples
are compressed with CS, they are encrypted using an
encryption algorithm. Here we use 8-bit integer chaotic block
encryption. Then integrity examination is carried out with
a cryptographic hash algorithm to prevent malicious
modifications. Message Authentication (MA) Algorithm
is adopted. The whole security operation is discussed
in Section III, namely MA-CBE. A block diagram of
HSCS system is given in Figure 2.
FIGURE 2. Block diagram of an HSCS system.
Notice in the receiver, the inverse hashing should neglect
the few transmitting errors. It can be accomplished by a
software counter. If the number of incorrect cases is under
an allowable level, the authentication verification is passed.
To reduce complexity and measure energy consumption,
we use a digital CS that does not include analog-to-digital
converter (ADC). Compression algorithm is applied linearly
after ADC. A top-level view of the compression compo-
nent is shown in Figure 3, where a linear feedback shift
register is used to create random matrix. Compression
process consists of multiplying the input vector with
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5. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
FIGURE 3. Compression hardware component.
a random matrix φ. The energy consumption (namely
multiplication number) depends on the matrix size. Partition
of input and compression ratio has significant impact on
execution time and energy consumption. Compression ratio
indicates the compression capability of the proposed
algorithm, which is calculated as:
r =
ciphertext length
plaintext length
× 100%. (14)
B. PERFORMANCE ANALYSIS AND DISCUSSION
Our evaluation has two parts. Firstly, it addresses the security
of HSCS; secondly, it analysis the compression performance
from energy consumption under different compression ratio
and input/output size.
1) SECURITY ANALYSIS
To examine the security performance, we will do verification
experiments for MA-CBE. The length of MAC is
set as 16 bytes (favorite length in hash functions).
We randomly choose 216 bits message from a C++ source
program. First, compute the MACs of original message,
and then do some transformations to the original message,
then re-compute the MACs of the new message. We make
four transformations, namely: change the word ‘‘turn’’
into ‘‘from’’; alter ‘‘7’’ to ‘‘4’’; change the first word ‘‘for’’
to ‘‘four’’; transform the punctuation ‘‘.’’ of the last line
to ‘‘;’’. The original MACs and the corresponding new MACs
are as follows:
E3210C4146625BA2377D8584A2EE25B3
87F93CD0F9287E233D3BEE25C15D2451
8FCA86EC55FBF6CE6D7C145D05FC6348
657A28AD83BEA68959EA05B52BF89217
73658B8E99E95579482AAE3CF1230B46
The above MACs show our MA-CBE has such character-
istics that it can diffuse the original message into each bit
of the output, while compressing the digest of original
message. Any modification to plaintext will result in
‘‘avalanche effect’’ in output MACs, namely the large number
of transformation.
To further testify the performance of MA-CBE, we next
will make statistical analysis on the change of MACs.
Choose arbitrary 216-bit message and compute the MACs,
labeled as ma. Toggle arbitrary bit of the message, and
re-compute the MACs, labeled as ma’. Count the
mismatching bits between ma and ma’, labeled as D.
This measurement experiments are repeated for X times.
There are several common performance metrics
for hash functions:
¯D =
1
X
X
i=1
Di, (15)
D =
1
X − 1
·
X
i=1
(Di − ¯D)
1/2
, (16)
G =
¯D
128
× 100%, (17)
G =
1
X − 1
·
X
i=1
(
Di
128
− G)2
1/2
× 100%, (18)
In (15) ∼ (18), ¯D and G is the average of D; the standard
deviation of D and P reflect the deviation between
original message and the average. The smaller D and P
are, the more distinct the feature of diffusion and chaos are.
Notice that the ideal diffusion and chaos can make each bit of
MACs change at a 50% probability while tiny change exists.
The values of ¯D, D, G, and G for different X (256, 512,
1024, 2048) are showed in Table 1.
TABLE 1. Measurements of MA-CBE.
The results in Table 1 show MA-CBE has good
performance in various performance metrics.
2) COMPRESSION PERFORMANCE ANALYSIS
To evaluate the energy consumption of each component in our
CSSC architecture, we carried out simulation with hardware
implementations Synopsys Design Compiler based on 65-nm
TSMC standard cell library [18]. Our estimation addresses on
CS and MA-CBE.
Suppose input data is partitioned into blocks of a-byte,
output block size is b bytes, compression ratio is r. Here
a and b are width and height of φ respectively. Figure 4 and
Figure 5 show the energy consumption of CS. Figure 4 shows
the energy consumption under different a and r, b is
equal to a.
FIGURE 4. Energy consumption under different a and r.
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6. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
FIGURE 5. Energy consumption under different a, with constant r = 8.
Figure 5 shows the situation under different a and
constant r (set as 8), b is 8 times of a.
Figure 4 and Figure 5 give the average consumption for
compressor implementations based on different input and
output block sizes. We can see from the figures that execution
time and energy consumption of the compressor are
proportional to the size of φ.
To measure the energy consumption of MA-CBE,
we address on input size. It determines the computation
amount and energy consumption. Suppose input and output
in simulation are both b bytes, and inputs are multiples of
the block size. For chaotic block encryption with block size
1 byte, the energy consumption Eenc(b) = 1.47b + 5.6pJ,
with error rate 0.25%. For hashing (MACs) with block size
4 bytes, the energy consumption Ehash(b) = 0.49b + 26.2pJ,
with error rate 0.012%.
To investigate the energy performance of the system with
and without compression, we next will compute the energy
consumption under the two situations. Let Ecs(a,b) be the
energy consumption for compressive sensing a bytes input
signals into b bytes, Eenc(b) and Ehash(b) are described as
above. Let E(a) be the total energy for encryption and
hashing a bytes without compression, E(a,r) be the energy
for compressing a bytes, with compression ratio r = a/b.
Then the energy saving with compression is:
η(b, r) = 1 −
E(br, r)
E(br)
= 1
−
Ecs(br, b) + Eenc(b) + Ehash(b)
Eenc(br) + Ehash(br)
, (19)
Figure 6 illustrates the energy reduction under different
b and r. Figure 6 (a) shows how energy decreases with r when
b is constant and (b) is that b changes and r is constant.
We can see from Figure 6 that η(b, r) increases if
compression ratio is larger, and decrease as output size
increase. HSCS is nearly 68% more energy-efficient than
encryption and hashing alone with appropriate r.
When r = 8, notice that if b is larger than 66, the additional
energy saving of CS exceeds the saving that encryption and
hash yield.
FIGURE 6. Energy saving with HSCS. (a) Constant output size b =16bytes.
(b) Constant compression ratio r = 8.
3) DISCUSSION
The experiment results show that our HSCS is especially
suitable for WSNs and greatly reduces the energy consump-
tion in secure sensor data gathering. With proper parameters,
our scheme has lower energy cost compared to the case
with no compression or hybrid security strategy. Besides,
we regulate that MA-CBE only operates a few rounds, which
will bring more energy bonus in comparing with other chaotic
encryption algorithm.
VI. CONCLUSIONS
This thesis presents a security scheme for multimedia data
gathering in WSNs. It constructs a lightweight hybrid
strategy MA-CBE with 8-bit integer chaotic block encryption
and MACs based hashing. The experiments and simulations
show our scheme has significant performance in security and
compression (mainly from energy-efficiency). MA-CBE is
robust and powerful in secure data gathering. With appr-
opriate compression ratio, input data size, and data
correlation, the energy-efficiency can promote more
than 67%.
Different with other MACs, our lightweight hybrid security
scheme allows a few mismatching bits. With the help of a
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7. J. Qi et al.: Hybrid Security and CS-Based Sensor Data Gathering Scheme
software counter, it can pass the authentication verification
under a certain threshold. This will reduce the sensitivity of
MACs to a few errors during transmission.
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JIN QI received the B.S. degree in computer sci-
ence and technology from Liaocheng University,
Liaocheng, China, in 2005, the M.S. degree in
computer application from Jiangnan University,
Wuxi, China, in 2008, and the Ph.D. degree in
information network from the Nanjing University
of Posts and Telecommunications, Nanjing, China,
in 2012. He has been a Lecturer with the School of
Internet of Things, Nanjing University of Posts and
Telecommunications, since 2012.
His main research interests are in the areas of intelligent computing, future
network, and Internet of Things.
XIAOXUAN HU received the B.S. degree in
automation from the Nanjing University of
Posts and Telecommunications, Nanjing, China,
in 2014, where she is currently pursuing the
M.S. degree in pattern recognition and intelligent
systems.
Her main research interests are in the areas of
intelligent computing, future network, and Internet
of Things.
YUN MA will receive the B.S. degree in internet of
things engineering from the Nanjing University of
Posts and Telecommunications, Nanjing, China, in
July 2015. She will study in the Hong Kong Uni-
versity of Science and Technology, Hong Kong,
for the M.S. degree in telecommunications, since
September 2015.
Her main research interests are in the areas of
intelligent computing, future network, and Internet
of Things.
YANFEI SUN received the Ph.D. degree in infor-
mation network from the Nanjing University of
Posts and Telecommunications, Nanjing, China,
in 2006.
He has been a Professor with the School of
Automation, Nanjing University of Posts and
Telecommunications, since 2006.
His main research interests are in the areas
of intelligent optimization, network management,
machine learning, and future network.
724 VOLUME 3, 2015
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