ABSTRACT idea of sensor networks based on collaborative effort

ABSTRACT

 

The main goal of the data aggregation schemes
in wireless sensor networks is to gather and aggregate data in an energy
efficient manner so that network lifetime is enhanced. However, the technique still has the inherent security problems and can
be easily compromised by a vast of attacks, such as reply attacks, data
interception and data tampering, etc. Hence, the goal is to design a secure and
efficient data integrity scheme, given an Identity-based aggregate signature
with a designated verifier for wireless sensor networks. In this model the
sensor nodes are grouped into clusters for efficient data transmission, Clustering
is an effective way to enhance the system performance of wireless sensor
networks. This model
can not only keep data integrity but also can reduce bandwidth and storage cost
for wireless sensor networks. The security of this scheme is provably secure in
the random oracle model under the computational Diffie–Hellman assumption.

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Keywords: Wireless Sensor Network, Id-based Cryptography, Data Aggregation,
Aggregate Signature, Data Integrity, Coalition Attack, Elliptic Curve
Cryptography, Verifier, Encryption, Decryption.

 

I.      
INTRODUCTION

 

A wireless sensor network (WSN) is a wireless network
consisting of spatially distributed autonomous devices using sensors to monitor
physical or environmental conditions, such as temperature, sound, pressure, etc.
These sensor nodes, which
consist of sensing, data processing, and communicating components, result in
the idea of sensor networks based on collaborative effort of a large number of
nodes. Such sensor nodes could be deployed in home, military, science, and
industry applications such as transportation, health care, disaster recovery,
warfare, security, industrial and building automation, and even space
exploration.

 

In WSNs, data aggregation refers
to the use of aggregation techniques to reduce the amount of bytes required to
code the different pieces of information and, thus, the traffic load which
needs to be processed within the network. Because of these advantages a lot of
attention has been paid to WSNs 1. Sensor nodes are usually resource-limited
and power-constrained; they always suffer from the restricted storage and
processing resources.

 

The concept of aggregate signature was first
introduced by Boneh et al. at Eurocrypt 2003 2.
The aggregate signatures are digital signatures where anyone, given n
signatures on n messages from n users, can combine all of these signatures into
a single short signature. The resulting signature can convince a verifier that
the n users indeed signed the n corresponding messages. By this way, aggregate
signature can greatly reduce the computational and communication overhead. Hence, aggregation is useful technique in
reducing storage cost and bandwidth. However, the technique still has the inherent security problems, such as
eavesdropping, reply attacks, data forge and data tampering, etc. Hence,
designing a secure and efficient data aggregation method is very significant
for WSNs.

 

This paper proposes a
model combining the features of aggregate signature scheme and ID-based
cryptography to give an ID-based data integrity scheme using cluster method for
WSNs.The security of this model can resist all kind of coalition attacks
3. The aggregate signature scheme is valid if and only if every individual
signature used in the aggregation is valid.

This paper is classified
as following: In section-II the basic details of Aggregate Signature schemes
and ID based cryptography is discussed. In section-III and section-IV the complete
system and security model of ID-based data integrity scheme is analysed and
reviewed and to show how to resist all kinds of coalition attacks. The design
and implementation is discussed in section-V. The Simulation results and
performance analysis of this implementation of ID based data integrity scheme is
discussed in Section-VI and section-VII. Finally, the section-VIII deals with
the conclusion.

 

II.   
RELATED WORK

 

The concept of aggregate signature was introduced
by Boneh et al in 2003. The main feature of Aggregate
signatures is that it allows an efficient algorithm to aggregate n signatures
of n distinct messages from n different users into one single signature. The
resulting aggregate signature can convince a verifier that the n users did
indeed sign the n messages.

 

To let a signature scheme function, the public key has to be bound with
the identity of the owner of the public key. Traditionally, this is provided by
the public key infrastructure (PKI) in which a third party known as certificate
authorities (CAs), issue digital certificates to bind a user and his public
key. In this scheme, before using the public key of a user, the participant
must first verify the certificate of the user, which results in a large amount
of computing and storage cost to manage certificates, to overcome these
problems Shamir introduced the identity-based public key cryptography (ID-PKC)
4 to simplify certificate management in PKI systems. In this scheme, the
user’s public key is easily generated from this user’s any unique identity
information (e.g. the serial number, a mobile phone number, an email address,
etc), which is assumed to be publicly known. A trusted third party, called the
private key generator (PKG), generates and issues secretly the corresponding
private keys for all users using a master secret key. Hence, ID-PKC suffers
from a key escrow problem which implies that all the users have to fully trust
PKG.

 

To address the key escrow problem of ID-PKC scheme,

Al-Riyami and Paterson 5 invented
a new scheme called certificateless public key cryptography (CLPKC). CL-PKC
also exploits a third party called Key Generation Center (KGC) to help a user
to generate his private key. However, the KGC can merely determine part of the
private key for each user. In CL-PKC, the user computes the resulting private
key with the partial private key resulted from the KGC and the secret
information chosen by the user. As a result, CL-PKC systems avoid the key
escrow problem.

 

Since then, many ID-based aggregate signature schemes have been presented
6 7.But, most of the existing CLAS schemes cannot sustain a type of
practical and harmful attacks called coalition attacks 8. If a coalition
attack can generate a valid aggregate signature using a few invalid single
signatures with the collusion of two or more signers. If this attack is
succeeded then the aggregate signature will pass the validation. This indicates
that a valid aggregate signature may fail to prove the validity of every single
signature involved in the aggregation. So, this paper mainly focuses on
designing the secured and efficient aggregate algorithm which can resist such
coalition attacks.

 

III.
SYSTEM ARCHITECTURE

 

The main aim of this system model is to protect data integrity while
reducing bandwidth and storage cost for WSNs.The system architecture consists of
four components namely:

Key
Generator
Base
Station
Aggregator
Sensor
Nodes

Figure
1 Architecture of the system

Key Generator is a key server which generates unique public and private keys for base
station and sensor nodes and uses Elliptic Curve Cryptography algorithm
to generate keys. It also shares public keys of sensor nodes
and base station.

 

Base Station possesses much more
computational power and larger memory and it is often connected
to a better source of energy. The base station’s primary goal is to gather
sensed data from sensor nodes in WSN. Sensed data can be stored, visualized and
analyzed.

 

Aggregator is one of the important methods
for prolonging the network lifetime in wireless sensor networks (WSNs). It
involves grouping of sensor nodes into clusters and electing cluster heads
(CHs) for all the clusters. CHs collect the data from respective cluster’s
nodes and forward the aggregated data to base station.

 

Sensor Nodes are used by wireless sensor nodes to
capture data from their environment. They are hardware devices that produce a
measurable response to a change in a physical condition like temperature or
pressure. Each sensor node
belongs to one cluster, sends encrypted messages to their aggregator, and the
messages will finally be sent to data center via aggregator.

 

IV. SECURITY MODEL

 

An ID-based Data-Integrity signature (IBDS) scheme is a tuple of
probabilistic polynomial-time algorithms.

The description of each algorithm is as follows.

Setup, KeyGeneration, Sign, Verify, Aggregation, VerifyAgg.

 

Setup: This algorithm is run by a key generation center

(KGC). G1, G2 are two cyclic groups of prime
order p. Let ê: G1 × G1 ? G2 be a
bilinear pairing, and let P be
an arbitrary generator of G1. H1, H2 and H are
full-domain collision resistant hash functions. H1, H2: {0, 1}*
? G1, H: G2 ?Z*p. KGC chooses x,y ? Z*p randomly and
computes P0 = xP, PKctr = yP. Then the system parameters are param = {ê,G1,G2, P, p,H1,H2,H, P0}, the
master secret key is msk = x. The data center’s public-secret
verification key is (PKctr =
yP, SKctr = y).

 

Key Generation: This algorithm takes a user’s identity IDi. Compute Qi=H1(IDi) and the sensor node’s
corresponding private key is Di = xQi. The KGC sends Di
to the user IDi through
a secure channel.

 

 Sign: This
algorithm takes a system parameters params, a message mi, an identity IDi and corresponding private key Di as input, and outputs an
individual signature ? on the message mi
for the user with identity IDi and generates ti ? Z p*

Ti = tiP,

hi = H2(Ti, IDi,mi),

Ui = Di + ti
hi.

 

Verify: This algorithm takes a system parameters params, an identity IDi,
a message mi and an individual signature ? as input the verifier
computes Qi = H1(IDi) and hi
= H2(Ti, IDi,mi),
then accepts if the following equation holds:

ê(Ui, P) = ê(P0,Qi) ê(Ti,
hi).

 

Aggregation: This algorithm is run by an aggregate signature generator and allows
the generator to compress multiple single signatures into an aggregate
signature. Each sensor node with the identity IDi provides a signature ?i = (Ui,
Ti, IDi, mi) on a message mi ?
{0, 1}
of its collection, i = 1, ·
· · , n. the aggregator computes

 

r = H(ê(U1, PKctr), ·
· · , ê(Un, PKctr)),

 

? = (U, T1, · · · , Tn)
is the aggregate signature with identities {ID1, ID2, . . . , IDn}
on messages {m1,m2, . . 
,mn} respectively.

 

VerifyAgg: To verify the validity of an aggregate signature ?Agg = (U, V, W) for
message-identity pairs {(m1, ID1),. . . ,(mn,IDn)}, the verifier computes agg

 Qi = H1 (IDi),
hi=H2 (Ti,mi, IDi), for i=1,.. ,n, and checks.

Where

 

 

 

V.DESIGN AND IMPLEMENTATION

 

A.  Design

 

In this model implementation is done by combining the
highlights of aggregate signature scheme and ID-based cryptography, given an
ID-based Data Integrity scheme (IBDIS) using cluster method for WSNs. This
model mainly focuses on designing the aggregate signature scheme which can
verify each and every individual signature of sensor nodes to resist attacks. The sensor
nodes are grouped into clusters so that network lifetime of nodes is increased
and simultaneously reduces bandwidth and storage cost.

The new aggregate signature scheme results in a short aggregate
signature that is valid if and only if every individual signature involved in
the aggregation is valid.

 

In order to
provide the end-to-end confidentiality,
Elliptic curve cryptography (ECC) is used. ECC is a public key
cryptography approach based on the algebraic structure of elliptic curves over
finite fields where the elliptic curves are defined over prime fields Fp, where
p is a large prime number. For current cryptographic purposes, an elliptic curve is a plane curve over a finite field (rather
than the real numbers) which consists of the points satisfying the equation.

{displaystyle y^{2}=x^{3}+ax+b,,}

Along with a
distinguished point at infinity denoted ?.

Where a, b ?  Fp such  that  4a3
+ 27b2 ? 0 (mod p).

For dealing the
security risks, secure data aggregation scheme must provide the following
security requirements.

Confidentiality: ensures that the plaintext can only be
accessible by the authorized user. All data captured must be encrypted and
prevent intermediate node to access to the plaintext.

 

Integrity: ensures that the received data has not been
altered, either maliciously or accidentally, during transmission.

 

Authenticity: ensures that the received data is sent by the
claimed sender.

 

Availability: ensures the survivability of the network
despite denial of service attacks.

 

Freshness: ensures that each message is recent and no
old messages replayed by an attacker.

 

Efficiency: a security protocol must
be efficient in term of computation and communication overhead in order to preserve
energy and prolong the network life time.

 

ECC algorithm is probabilistic in nature and the security relies on the
hardness of algorithm. However, when considering security against active
adversaries, a verification of the data integrity is needed in order to ensure
that all the data were ported successfully, each

sensor of the network computes a tag using HMAC algorithm on cipher text,
and every intermediate node then verify the data integrity, execute the
homomorphic operation if the verification hold; otherwise, the packet will be
dropped, with this process the data integrity of  all packet is maintained and all senders are
authenticated.

 

B. Implementation

 

The Implemented model of the proposed system
consists of three major components known as data center, aggregator and sensor
nodes which are in large numbers. Aggregator works as a cluster head, can
produce the aggregate signature and send it to the data center with the
messages generated by the sensor nodes.

The
complete block diagram of the implementation is shown in the following:

Figure
2 Block Diagram of the System

 

 

 

This paper proposed an improvement for ID-Based Aggregate Signature Scheme by
providing an initial approximation of trustworthiness of sensor nodes which
makes the data not only coalition free, but also more secure and efficient. The
implementation is described below:

?  Sensor nodes,
if they want to transmit messages, first they want to register with key server,
for this case we make use of key generator to generate unique keys like public
and private keys using Elliptic Curve Cryptography (ECC). The same procedure
repeats for cluster head and even for base station.

?  Sensors in
order to send the messages to the cluster head they make use of public key of
base station and its own private key to generate a shared key for encrypting
the message.

?  This
encrypted message is sent to the cluster head, where cluster head will
aggregate the message and produces the aggregate data and signature for it and
sends to the base station.

?  In base
station in order to decrypt the message sent form the cluster head it make use
of public key of the sensor and using its own private key it generates a shared
key which will decrypt the sent messages.

?  If the
decrypted message is same as the encrypted messages then we can say that the
matching is successful.

 

VI. SIMULATION
RESULTS

 

The code is developed and
simulated in the Network simulation (NS) tool.
The sensor nodes are grouped into clusters and appropriate cluster heads are
selected to reduce the energy consumption and increase the network lifetime.

Sensor node has limited
resources in terms of computation, memory and battery power, aggregator has a
certain ability to calculation and communication range and it works as a
special sensor node, data centre has a strong computing power and storage
space. So, our scheme’s objectives are trying to reduce the communication cost
and computation cost of aggregator and sensor node without loss of generality.

In the following, we evaluate our scheme in terms of

·        
Energy
Consumption

·        
Computation
Overhead

·        
Communication  Overhead

Figure 3 Energy Consumption

 

Figure 4 Computation Overhead

 

Figure 5 Communication Overhead

VII. PERFORMANCE EVALUATION

 

The performance comparison is obtained by comparing our ID-based data
Integrity scheme (IBDIS) with the Certificateless aggregate signature scheme
(CLAS) as following.

Energy Consumption: The energy consumed is decreased when compared to the previous scheme
from the (figure 3) graph, because of the clustering of nodes vast amount of
load on nodes is reduced.

Computation Overhead:  Computation
Overhead is obtained by calculating the number of routing packets to number of
packets sent. The comparison graph (figure 4) shows that the computation
overhead is improved and reduced by a fare margin.

Communication Cost: The comparison of communication cost (figure
5) indicates that the aggregate scheme can reduce transmission in one data aggregation. It is performed by calculating the
number of routing packets to number of received packets.

 

Energy Consumption
(joules)

No. of nodes

CLAS-scheme
(existing)

IBDIS-scheme
(proposed)

50

176

155

100

182

159

150

179

167

200

184

171

250

188

176

Table
1 Energy Consumption

Computation Overhead

No. of nodes

CLAS-scheme
(existing)

IBDIS-scheme
(proposed)

50

33.49

29.49

100

43.76

34.76

150

55.07

51.07

200

58.03

54.43

250

66.69

62.33

Table 2
Computation Overhead

 

 

 

Communication Overhead

No. of nodes

CLAS-scheme
(existing)

IBDIS-scheme
(proposed)

50

35.65

32.65

100

40.38

37.38

150

43.42

42.42

200

48.39

45.39

250

53.91

50.30

Table 3 Communication Overhead

 

VIII. CONCLUSION

 

This paper raised data transmission
and security issues and proposed an ID-based data Integrity scheme using
cluster method for WSNs, which protects data integrity and resist coalition
attacks. This scheme consists of cluster heads which
can compress many signatures generated by sensor nodes into a single one. The experimental
results show that our IBDIS scheme can not only reduce communication overhead
and computation overhead but also can reduce Energy consumption. It is also
proved that this scheme can stand up against any coalition attacks, as
aggregate signature is valid if and only if every individual signature involved
in the aggregation is valid. In future work, the aim is to improve the
performance of the aggregation scheme by using a novel
cluster-head choice technique to extend network lifetime and reliability.

 

VI. REFERENCES

1. J. Yick, B. Mukherjee and D. Ghosal,
“Wireless sensor network survey,”Computer
Networks, vol. 52, pp. 2292-2330, 2008.

2. D. Boneh,
C. Gentry, B. Lynn and H. Shacham, “Aggregate and verifiably encrypted
signatures from bilinear maps”, in
Proc. Eurocrypt 2003, Warsaw,
Poland. LNCS, pp. 416-432, 2003.

3. F. Zhang, L. Shen and G. Wu, “Notes on the security of
certificateless aggregate signature schemes,” Information Sciences, vol. 287, pp. 32-37, 2014.

 

 

4. A. Shamir, “Identity-based cryptosystems and signature schemes,” in Proc .CRYPTO 1984, Santa Barbara,
California, USA, August 19-22,Springer-Verlag, Berlin LNCS, vol. 196, pp.
47-53, 1984.

5. S. Al-Riyami, K. Paterson, Certificateless public key cryptography, in:
ASIACRYPT 2003, LNCS, vol. 2894, 2003, pp. 452–473.

6. L. Zhang, B. Qin, Q. Wu and F. Zhang, “Efficient many-to-one
authentication with certificateless aggregate signatures,” Computer Networks, vol.

54, no. 14, pp. 2482-2491, 2010.

7 H. Xiong, Z. Guan, Z. Chen and F. Li, “An efficient certificateless  aggregate signature with constant pairing
computations,” Information Sciences, vol. 219, no. 10, pp.
225-235, 2013.

8. F. Zhang, L. Shen and G. Wu, “Notes on the security of
certificateless aggregate signature schemes,” Information Sciences, vol. 287, pp. 32-37,2014.