Abstract: the most important technologies for the




Now a day’s wireless sensor network is most important technology
for transferring data through network with secure manner. Before transferring
message from source node to destination node we can find out path consisting of
connected links. To identify the routing from source node to destination node
so many end to end routing protocols are existing in the world. In this paper we
are implementing a novel design secure end to end routing protocol for transfer
data with securely. Before performing data transformation process we can
implement two more fundamental concepts are user key establishment and
authentication. The user authentication process enables for identify users by
group key manager. The generation key we are using differ Hellman key exchange
algorithm.  The authentication of both
users we are implementing a random nonce based authentication schema. Before
transferring data to destination node the source will send ids to group key
manager. The server will find routing from source node to destination node,
using that path data will be transferred to destination node.  Before transferring message the source node
will encrypt the message and send to destination node. By performing data
encryption and decryption process we are using cryptography technique. So that
by implementing those concepts we can improve efficiency of network and also
provide more security of transferred message.

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Keywords:  wireless sensor
network, authentication, cryptography, routing, shared key, encryption and

I.      Introduction


Wireless sensor network (WSN) is widely
considered as one of the most important technologies for the twenty-first century.
In the past decades, it has received tremendous attention from both academia
and industry all over the world. A WSN typically consists of a large number of
low-cost, low-power, and multifunctional wireless sensor nodes, with sensing,
wireless communications and computation capabilities. These sensor nodes
communicate over short distance via a wireless medium and collaborate to
accomplish a common task, for example, environment monitoring, military
surveillance, and industrial process control. The basic philosophy behind WSNs
is that, while the capability of each individual sensor node is limited, the
aggregate power of the entire network is sufficient for the required mission.
In many WSN applications, the deployment of sensor nodes is performed in an ad
hoc fashion without careful planning and engineering. Once deployed, the sensor
nodes must be able to autonomously organize themselves into a wireless
communication network. Sensor nodes are battery-powered and are expected to
operate without attendance for a relatively long period of time. In most cases
it is very difficult and even impossible to change or recharge batteries for
the sensor nodes. WSNs are characterized with denser levels of sensor node
deployment, higher unreliability of sensor nodes, and sever power, computation,
and memory constraints. Thus, the unique characteristics and constraints
present many new challenges for the development and application of WSNs.


to the severe energy constraints of large number of densely deployed sensor
nodes, it requires a suite of network protocols to implement various network
control and management functions such as synchronization, node localization,
and network security. The traditional routing protocols have several
shortcomings when applied to WSNs, which are mainly due to the
energy-constrained nature of such networks. For example, flooding is a
technique in which a given node broadcasts data and control packets that it has
received to the rest of the nodes in the network. This process repeats until
the destination node is reached. Note that this technique does not take into
account the energy constraint imposed by WSNs. As a result, when used for data
routing in WSNs, it leads to the problems such as implosion and overlap. Given
that flooding is a blind technique, duplicated packets may keep circulate in
the network, and hence sensors will receive those duplicated packets, causing
an implosion problem. Also, when two sensors sense the same region and
broadcast their sensed data at the same time, their neighbour’s will receive
duplicated packets. To overcome the shortcomings of flooding, another technique
known as gossiping can be applied. In gossiping, upon receiving a packet, a
sensor would select randomly one of its neighbours and send the packet to it.
The same process repeats until all sensors receive this packet. Using
gossiping, a given sensor would receive only one copy of a packet being sent.
While gossiping tackles the implosion problem, there is a significant delay for
a packet to reach all sensors in a network. Furthermore, these inconveniences
are highlighted when the number of nodes in the network increases.


Our focus is on routing security in
wireless sensor networks. Current proposals for routing protocols in sensor
networks optimize for the limited capabilities of the nodes and the application
specific nature of the networks, but do not consider security. Although these
protocols have not been designed with security as a goal, we feel it is
important to analyse their security properties. When the defender has the
liabilities of insecure wireless communication, limited node capabilities, and
possible insider threats, and the adversaries can use powerful laptops with
high energy and long range communication to attack the network, designing a
secure routing protocol is non-trivial. We present crippling attacks against
all the major routing protocols for sensor networks. Because these protocols
have not been designed with security as a goal, it is unsurprising they are all
insecure. However, this is non-trivial to fix: it is unlikely a sensor network
routing protocol can be made secure by incorporating security mechanisms after
design has completed. Our assertion is that sensor network routing protocols
must be designed with security in mind, and this is the only effective solution
for secure routing in sensor networks


II.   related work

Currently, the progression of wireless
technology in various application areas including military, industrial,
environmental, medical, crisis management, smart environments to name but a
few, leads to the emergence of wireless sensor networks (WSNs) at an
accelerated pace to collect and communicate information from remote locations
wirelessly. A wireless sensor network (WSN) can be treated as a co-operative
network of small size, low power, smart devices named as Nodes or Motes, which
have the capability of sensing a physical phenomenon (like temperature,
humidity, pressure, vibration…etc) and relay the same or processed information
to a sink via wireless links possibly with multiple hops between these nodes.
The unique characteristics of WSN such as small size, low power consumption,
autonomous, mobility, dense in volume, self-healing and self-organizing poses
some constraints in terms of power consumption, storage, processing
capabilities and bandwidth requirement. Even though energy efficiency is of a
major concern, providing the required Quality of Service (QoS) in terms of
timeliness, reliability, fault tolerance, is also of a major concern for the
respective applications. For an instance, a wireless sensor network which is
deployed in a nuclear power plant to monitor the release of radioactive fluids,
has to detect the leakage at an infant stage and the corresponding alert has to
relay to the control room with in a defined dead time, otherwise it may cause
catastrophic effect. Likewise, WSNs have gained an immense attention for their
ability in meeting the real time QoS guarantee in many time critical scenarios.
In general, real time packet communication guarantee can be categorized as i)
Hard Real Time (HRT) ii) Soft Real Time (SRT) . HRT should support a
deterministic dead time. That implies, delivery of a message after the dead
time is considered as a failure, sometime it may lead to a catastrophic effect.
On the other hand, SRT supports probabilistic dead time, which allows some sort
of latency in message delivery. Providing a real time communication in case of
WSNs is a challenging task because of the highly unpredictable nature of
wireless links, variable data packets relaying and energy, bandwidth constraints.
The requirement of real time guarantee can be addressed from different
mechanisms in different layers of protocol stack of WSN. I.e. by means of an
efficient protocol in MAC layer, efficient routing protocol in network layer,
by in network data aggregation mechanism and even cross layer design approach .
In this paper, we presented a comprehensive survey of various real time routing
protocols in WSNs, which meets the requirement of timeliness along with other
QoS in time critical applications.


Routing is
another very challenging design issue for WSNs. A properly designed routing
protocol should not only ensure a high message delivery ratio and low energy
consumption for message delivery, but also balance the entire sensor network
energy consumption, and thereby extend the sensor network lifetime Motivated by
the fact that WSNs routing is often geography based, we propose a
geography-based secure and efficient Cost-Aware Secure routing (CASER) protocol
for WSNs without relying on flooding. CASER allows messages to be transmitted
using two routing strategies, random walking and deterministic routing, in the
same framework. The distribution of these two strategies is determined by the
specific security requirements. This scenario is analogous to delivering US
Mail through USPS: express mails cost more than regular mails; however, mails
can be delivered faster. The protocol also provides a secure message delivery
option to maximize the message delivery ratio under adversarial attacks. In
addition, we also give quantitative secure analysis on the proposed routing
protocol based on the criteria proposed in 
Routing is a challenging task in WSNs due to the limited resources. Geographic
routing has been widely viewed as one of the most promising approaches for
WSNs. Geographic routing protocols utilize the geographic location information
to route data packets hop-by-hop from the source to the destination. While
geographic routing algorithms have the advantages that each node only needs to
maintain its neighbouring information, and provide a higher efficiency and a
better scalability for large scale WSNs, these algorithms may reach their local
minimum, which can result in dead end or loops. To solve the local minimum problem,
some variations of these basic routing algorithms were proposed in. In ,
source-location privacy is provided through broadcasting that mixes valid
messages with dummy messages. The main idea is that each node needs to transmit
messages consistently.


III. proposed system


In this paper we proposed a
novel design end to end routing protocol for finding shortest path and also
provide authentication of communication entities in the network. Before
performing the finding shortest route the source node and destination node will
generate shared key and perform the authentication process. After completion of
authentication process the source node will send destination id to server.
Before performing encryption and decryption process we can find shortest route
by using end to end routing protocol. After that the sender will encrypt
message and convert into cipher format. The completion of encryption process
the sender will send that cipher format data to destination node through the
path. The destination node will retrieve that data and perform the decryption
process. By performing decryption process the destination node will get
original message. The implementation procedure of user’s authentication is as


Mutual Authentication


After completion of building network we can
perform the mutual authentication of both users in the network. By implementing
process of mutual authentication is as follows.


1. Now if two users U1 and U2
have become adjacent to one another, then these users are need to execute
authentication process so that User U1 proves to U2 and
user U2 proves to U1.


2. Before performing verification process each user
will choose two prime numbers p and g.


3. After that each user will choose one private key
(a) and calculate public key based on following formula.

       Public key= g a mod p


4. After calculating public keys each user will
shared those values and again will calculate shared key base on following


key= pub a mod p


5. By calculating those shared keys are same for
both users.


6. After completion of shared keys each user will be
verified by each other by performing following process.


   i).User U1
choose random nonce and send message that is received by user U2.


  ii).User U2
also choose random nonce and send message that is received by user U1.


  iii). After
sending that random nonce user U1 will generate verification message
for User U2. The generation of verification message is as follows.


(U1, U2, H (n|U1|U2|shared key1)


After generating verification message that send to
User U2


iv).The user U2 also generate
verification message for User U1 and send that message to User U1.


    Verify (U2,
U1, H (U2|U1|n|shared key2))

After sending those verification messages each and every user will
verify and both verification messages are equals those are the authenticated
users. If both verification messages are not equal those are not authenticated
users. After that the sender will choose the destination node id and send that
id to server. By using those ids of sender and receiver the server will find
out shortest route by calculating shortest distance between nodes or users or
group members.



Generation of
distance matrix and finding Shortest Routing:


       In this
module the server will generate distance matrix and finding shortest route. The
implementation process of distance matrix is as follows.


1. The server will
get all nodes of distance points and using those points we can generate
distance matrix.


2. Take the each node distance
points and calculate difference between each node put into matrix format. This
process will repeat until completion of all nodes distance.


3. The distance of each node to
other node is as follow.


= (x1-x2) + (y1 –y2 )


4. Finding distance source node
to other nodes by using following formula


int max=0;


int min=di;