cnr-contemporary node recovery from a failure in wireless sensor actor networks

585 Pages·2012·60.68 MB·English

Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
college,TamilNadu,India,March 28-29,2014
CNR-CONTEMPORARY NODE RECOVERY FROM A FAILURE IN
WIRELESS SENSOR ACTOR NETWORKS
1 2
S.Sathish , T.Parameswaran
1PG scholar, CSE, Anna University Regional Centre, Coimbatore, Tamil Nadu, India, [email protected]
2Assistant Professor, CSE, Anna University Regional Centre, Coimbatore, Tamil Nadu, India, [email protected]
Abstract
Recent years, Wireless Sensor-Actor Networks (WSANs) have started to receive growing attention due to their potential in many real-
time applications. For some of these applications space exploration, costal and border protection, search and rescue. In WSNs, it is
necessary to maintaining the internodes interaction and stalwartly connected network topology at all time. Topology management
issues are very significant in the context of wireless networks such as sensor-actor networks. A breakdown of an actor-node may cause
the network to partition into disjoint blocks and changes the routing path. In wireless sensor actor network, a number of schemes have
been proposed for restoring the network connectivity. Prior approaches and strategies were discussed about only non simultaneous
node recovery from a multiple failure with the bunch of topology changes. In general, simultaneous node recovery can lead to enlarge
the some of the inter-actor path length. Our approach scrutinize the contemporaneous node recovery from a multiple node failure with
maintain the nominal topology changes by using a Least Disruptive PILOT Node (LDPN) algorithm. In LDPN algorithm, by use a set
of PILOT (Pre-defined, Intelligent, Lightweight topology management) nodes that can be used whenever a link between two sensor
nodes is about to break. This approach removes the failure node by using a best PILOT node. PILOTnode moves and act as a bridge
between two neighbors node. The LDPN algorithm can reinstate the network connectivity and make sure the minimal topology
changes within the network.
Index Terms: Failure, network recovery, topology management, PILOT.
1. INTRODUCTION
Since the start of the third Millennium Wireless Sensor Networks (WSNs) generated interest from industrial and research perspectives.
Recent years have witnessed a growing interest in the applications of wireless sensor-actor networks (WSANs). Of particular interest
are applications in remote and hard areas in which human intervention is risky or impractical. Examples include space exploration,
battle field surveillance, search-and-research, costal and border protection; today such networks are used in many industrial and
consumer applications.
The WSN is built of “nodes” from a few to several hundred or even thousands, where each node is connected to one (sometimes
several) sensors. In WSANs involves two types of nodes: 1) Sensors and 2) Actors. Sensors are inexpensive and highly constrained in
energy and processing capacity. On the other hand, actors are more capable nodes with relatively more onboard energy supply and
richer computation and communication resources. Each such sensor node has several parts: a radio transceiver with an internal or
external antenna, a microcontroller, processor, and an electronic circuit for interfacing with the sensors and energy sources[1]. Due to
the small form factor and the limited onboard energy, sensor nodes are susceptible to failure. To mitigate the risk of reduced degree
connectivity and loss of coverage that a faulty node may cause, the network deployment usually engages redundant nodes that can
either act as a passive spare or pick additional load if some nodes fail. In essence, such a strategy provisions the tolerance of node
failure at the time of network setup. Other approaches for recovery from a node failure pursue more reactive strategy by changing the
position of some nodes to restructure the network topology [2].
This proposed system analyze and investigate the contemporary (simultaneously) node recovery from a multiple node failure with
minimal topology management changes in WSANs. In this paper, our approach presents a Least-Disruptive PILOT Node (LDPN)
algorithm for recovering node from a multiple failure. In this approach make use of a set of lightweight nodes in order to bridge failing
connections in a sensor networks. In this paper, we describe an approach for topology management by making use of a set of PILOT
(Pre-defined, Intelligent, Lightweight tOpology managemenT) nodes that can be used whenever a link between two sensor nodes is
about to break.
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
college,TamilNadu,India,March 28-29,2014
The next section describes the overview of related work. Section 3 describes the problem identification in the previous approaches.
Section 4 explains the LDPN algorithm in detail. Section 5 describes the results. The paper is concluded in Section 6.
2. RELATED WORK
A number of schemes have recently been proposed for restoring network connectivity in partitioned WSANs [2]. All of these schemes
have focused on reestablishing severed links without considering the effect on the length of pre failure data paths. Some schemes
recover the network by repositioning the existing nodes, whereas others carefully place additional relay nodes. On the other hand,
some work on sensor relocation focuses on metrics other than connectivity.
2.1 Node Repositioning
The main idea of this category of recovery schemes is to reposition some of the healthy nodes in the network to reinstate strong
connectivity. LeDiR [3] fits in this approach. Least-Disruptive topology Repair (LeDiR) relies on the local view of nodes and ensures
that no path between any pair of affected nodes is extended relative to its prefailure status. LeDiR algorithm assumes that only the non
simultaneous node failure will take place in the network. The main ides for LeDiR is to pursue block movement instead of individual
nodes in cascade. LeDiR identifies the smallest among the disjoint blocks.
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Fig-1: WSANs with faulty node.
The above fig-1 shows the node failure wireless sensor-actor networks. Each node sends hello messages to its neighbor’s node and
ensures the availability of neighbors. Then only starts the transmission. In the above fig-1 the node 1 does not send any
acknowledgement for its hello message to its neighbors. The nodes 2,3 and 4 assumed, that node 3 has failed and break the network
connectivity. In the above fig-1, the network stays strongly connected after the loss of a non leaf node like17. Meanwhile, the failure
of the cut vertex node 1 leaves nodes 2, 3 and 4 was isolated from the rest of the network.
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
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Fig-2: Replacing the failure node by LeDiR.
The fig-1 shows the node failure in wireless sensor-actor network. The fig-2 shows a node recovery from a single node failure by
using least disruptive topology repair. The node 2, node 3, and node 4 does not make any communication with other nodes. It act as
th
individual block. This block move towards to the sink node (0 node) and make the connectivity based on LeDiR algorithm. This
approach was used to recover non simultaneous node failure only. It does not opt to multiple node failure.
There are several approaches published and differ in the level of involvement expected from the healthy nodes, in the required
network state that needs to be maintained, and in the goal of recovery process. Distributed Actor Recovery Algorithm (DARA) [4]
requires every node to maintain a list of their two-hop neighbors and determine the scope of the recovery by checking whether the
failed node is a cut vertex. DARA pursues a probabilistic scheme to identify cut vertices. A best candidate (BC) is selected from the
one-hop neighbors of the dead actor as a recovery initiator and to replace the faulty node. PArtition Detection and Recovery
Algorithm (PADRA) [5] identifies a connected dominating set to determine a dominatee node. The dominate does not directly move
to the location of the failed node; instead, a cascaded motion is pursued to share the burden.
The restoring inward motion (RIM) [6] approach exploits the nodes mobility and requires only 1-hop neighbor information to recover
from a node failure. The restoration process was both localized in scope and distributed in execution, requiring no network wide
coordination among nodes. Each node can independently decide its role in the restoration process, and where to move if needed. RIM
requires each node to be aware of the location of its 1-hop neighbors. Each entry in the table contains two parameters {Node_ID,
Relative position}, where Node_ID is a unique identification assigned to each node in the WSN. RIM was designed to recover from a
single node failure. Simultaneous node failure may cause conflicting conditions for RIM to converge successfully. As mentioned
earlier, the probability for multiple nodes to fail at the same time is very small and would not be a concern for RIM. Finally, RIM
tends to shrink the network inward around the failure node. While such a recovery scheme increases the network connectivity, it may
negatively affect the node coverage.
The Connected Dominating Set (CDS) [7] process was based on distributed recovery of node failures in Wireless Sensor and Actor
Networks. The replacement is done through a cascaded movement where all the nodes from the dominatee to the cut-vertex are
involved. The goal is to share the movement load so that the energy of the selected dominatees will not drain quickly due to long
mechanical motion. The idea was, for each actor node to determine whether its failure causes a partitioning or not in advanceand if so
to take the necessary precautions before the failure happens. Since partitioning was caused by the failure of a node which is serving as
a cut vertex (i.e., a gateway for multiple nodes), our approach utilized the connected dominating set (CDS) of the whole network in a
distributed manner to identify such cut-vertex nodes. Once such nodes are determined, each node designates the appropriate neighbor
to handle its failure when such thing arises in the future. In CDS the transmission range increases, surprisingly the travel distance also
increases and CDS based algorithm does not retain the similar topology management.
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
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2.2 Recovery by Placement of Relay Nodes
Relay node replacement was another approach for restore the network connectivity when failure occurred. A relay method was a more
capable node with significantly more energy reserve and longer communication range than sensors. Although they can, in principle, be
equipped with sensor circuitry, relays mainly perform data aggregation and forwarding. Unlike sensors, a relay may be mobile and has
some navigation capabilities. Relays are favored in the recovery process, because it was easier to accurately place them relative to
sensors, and their communication range was larger, which facilitates and expedites the connectivity restoration among the disjoint
segments. Intuitively, relays are more expensive. Therefore, the numberof engaged relays should be minimized.
Due to the harsh surroundings and violent nature of wireless sensor network (WSN) applications, the network sometimes suffers a
large-scale damage that involves several nodes and would thus create multiple disjoint partitions. Bio Inspired Relay node [8]
investigated a strategy for recovering from such damage through the placement of relay nodes (RNs). The deployment of relay nodes
(RNs) to restore connectivity among the disjoint partitions of a damaged WSN. Given the cost of RNs and the logistical challenges in
deploying them, it was usually desirable to minimize the count of the required nodes. 1C-Spider Web Heuristic restore 1-connectivity
and provide the desirable topology features by utilizing the way a spider creates its web. The idea behind the 1C-SpiderWeb
deployment strategy is to place the relays inward to yield better network connectivity and coverage. Bio-Inspired node replacement
does not consider energy as a metric in placing the RNs. Second, although connectivity among sensors and relays is considered, these
nodes belong to a single network, where all the RNs communicate with the same base station.
Effective QoS-Aware Relay (EQAR) Node Placement [9] Algorithm for connecting disjoint wireless sensor sub networks. EQAR, to
the best of our knowledge, was the first approach that exploits the deployment RNs in order to federate segments of disjoint sensors
subject to QoS constraints. EQAR opts to efficiently populate the least number of RNs such that the desired QoS goals are satisfied
and connectivity among the disjoint segments is established. As mentioned earlier, additive QoS requirements, e.g., bandwidth, are
assumed in order to simplify the presentation. EQAR processes one intersegment path at a time populating the number of required
relays along the found path, and refreshing the cost of the edges based on the updated residual capacity of the relays in thepath. EQAR
finally maximizes the utilization of the uncommitted resource and thus minimizes the total relay count.
2.3 Topology Management in WSANs
Topology management [10] issues are very important in the context of wireless networks such as sensor networks. Topology
management is to ensure that the various network connectivity parameters are managed so as to ensure that the parameter values are
within certain bounds. Each sensor node in a sensor network to have only a pre-defined number of neighbors. This can be a
requirement of a higher sensor information processing application in order to make sure that the sensors are distributed evenly over a
region. Topology management has been addressed from various perspectives such as energy conservation in a wireless sensor-actor
network. Topology based protocols uses the principle that every node in a network maintains topology information and that the main
process of protocol operation was based on the topology of the network.
3. PROBLEM STATEMENT
To the best of our knowledge, most recovery schemes found in the literature assumed no simultaneous faults. LeDiR algorithm
assumes that only non simultaneous node failure will take place in the network. All of these schemes have focused on reestablishing
severed links without considering the effect on the length of prefailure data paths. Some of schemes recover the network connectivity
by repositioning the existing nodes. The existing system considers the connectivity restoration problem subject to path length
constraints. A Least-Disruptive topology Repair (LeDiR) relies on the local view of nodes and ensures that no path between any pair
of affected nodes is extended relative to its prefailure status. There are some major issues in LeDiR algorithm; First, Simultaneous
node failure may cause conflicts conditions for LeDiR to converge successfully. Second, LeDiR tends to shrink the smallest block
inward the failed node; it may negatively affect the node coverage. The focus of the LeDiR is on connectivity the non simultaneous
node failure and does not factor in the impact on coverage. Topology management issues are very important for simultaneous node
recovery from a multiple node failure.
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
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4. LEAST DISRUPTIVE PILOT NODE (LDPN)
In Wireless Sensor-Actor Networks (WSANs), it is necessary to maintain a inter-node connectivity at all time. There are several
schemes was developed for network restoring and all these schemes focused on restoring a non simultaneous nodes at a time. Our
approach proposes to deploy PILOT (Pre-defined, Intelligent, Lightweight tOpology management) nodes with Least-Disruptive
algorithm to restore the network connectivity and retain the desired topology. The PILOT nodes form a logical ring network among
themselves to help address issues of topology management. These lightweight nodes that are capable of motion could be directed to
position themselves at appropriate locations. In order to active this objective, we have assumed that all nodes (both w-node and PILOT
nodes) are location aware. Wireless node and PILOT nodes have distinct functionality to support topology management. Each w-node
monitors the status of its links and in order to avoid duplicate efforts in tracking link status, each link is monitored by onlyone node.
The link failure can be detected relatively whenever an ACK message is not received in response to a repeated transmission of either a
data packet or a control messages.
Fig-3: Construction of PILOT nodes with WSANs
4.1 Simultaneous Node Recover
In LDPN algorithm, PILOT nodes that can be used whenever a link between two sensors nodes is about break. Actor nodes
periodically send hello or heartbeat messages to their neighbors to ensure that they are available and functional and also report
changes to the one-hop neighbors. Missing hello messages can be used to detect the failure of actors. Once a failure node is detected,
sends the failure messages to wireless nodes. Wireless nodes send that request to a PILOT network. PILOT node exchanges the
request information with other PILOT nodes.
PILOT node collaboratively determines the best PILOT node to assist in topology management. The selection of the PILOT node
could be based on factors such as power available with two nodes associated with a failing link, possible predicted movement in future
time, (location of PILOT node would require minimum readjustment); and distance between location of PILOT node and target nodes.
Then PILOT information is communicated back to the requesting wireless nodes. The assigned PILOT node moves to occupy a
computed position to act as a bridge and maintain the network connectivity and transmit the information. Least-Disruptive with PILOT
node algorithm, ensures that the simultaneous node recovery from multiple node failure with maintain the minimal topology changes.
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
college,TamilNadu,India,March 28-29,2014
Functions of a wireless-node
On timer // periodically computes life of a link
{
For each North-bound or East-bound link L
{ Let N1 and N2 be two w-nodes of L; Compute Link Life
(L,N1,N2); If L.Life < Threshold
{ Set R.N1 based on N1 and R.N2 based on N2 details; Determine a PILOT node p; Communicate R to p;
}
}
Compute Link Life (L, N1, N2) at a future time T;
{
Let N1.Power, N1.Speed, and N1.Direction be power and mobility related params of N1 and similarly for N2;
N1.PH, N1.SH, and N1.DH be history associated with node N1's power, speed and direction variations;
Similarly N2.PH, N2.SH, and N2.DH;
Predict and project power, speed, and direction based on history: N1.FP, N1.FS, and
N1.FD; Similarly compute N2.FP, N2.FS, and N2.FD;
Based on N1.Location, N1.FS, and N2.FD, compute N1.FLocation; Similarly, compute N2.FLocation;
Compute range based on N1.FLocation and N2.FLocation;
Based on N1.FP, N2. FP and range, determine bidirectional reach ability
If reachable return 1; else return 0;
Functions of a PILOT node
On receiving request (R) from a wireless-
node { Set R.Source PILOT Id = Self.Id;
Compute R.Target Location based on R.N1.Location, R.N2.Location,R.N1.Power and
R.N2.Power; If Self is bridging a link, { send R to the next PILOT node; return; }
Set R.Target Location = Self.Location; Set R.Target PILOT Id = Self. Id; Send R to the next PILOT node;
}
On receiving request (R) from a PILOT node
{ If R.Source PILOT Id == Self.Id and R.Target PILOT Id = NULL, { Send NACK to R.N1.Id;
} If Self.Bridging is TRUE, { Send R to the next PILOT Node; Return;}
If R.Target PILOT Id == Self.Id, // Self is the best PILOT node to bridge between N1 and N2
{ Set Self.Briding = TRUE; Move to R.Target Location; Send Hello message to R.N1 and
R.N2; Establish links with N1 and N2; Return;
}
Compute Dist1 based on Self.Location and R.Target Location;
Compute Dist2 based on R.Target PILOT Location and R.Target.Location;
If Dist1 < Dist2 // Self is a better target PILOT node
{ R.Target PILOT Id = Self.Id; R.Target PILOT Location = Self.Location;}
Send R to the next node in the PILOT network; Return;
}
Fig-4: Functions of LDPN algorithm
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
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5. PERFORMANCE ANALYSIS
Least Disruptive PILOT Node algorithm is validated through simulation. This section discusses the simulation environment and
results. The experiments are performed on a Wireless Sensor Actor Networks (WSAN) simulator developed in Tool Command
Language (TCL). In the experiments, we have created connected topologies consisting of varying number of actors (20 to 100) with
fixed transmission range (r= 100 m). All nodes are assumed to transmit at the maximum power set for the individual experiment, and
thus, the detection of failure of a critical node would justify the invocation of LDPN.
The following parameters are used to vary the characteristics of the topology in the different experiments:
∑ Number of deployed actors (N): This parameter affects the node density and the WSAN connectivity. Increasing N makes the
WSAN topology highly connected.
∑ Communication range (r): All actors are assumed to have the same communication range r. The value of raffects the initial
WSAN topology. While a small rcreates a sparse topology, a large rboosts the overall connectivity.
The following metrics are used to measure the performance of LDPN in terms of recovery overhead.
∑ Total travelled distance: reports the distance that the involved nodes collectively travel during the recovery. This can been
visioned as a network-wide assessment of the efficiency of the applied recovery scheme.
∑ Number of relocated nodes: reports the number of nodes that moved during the recovery. This metric assesses the scope of
the connectivity restoration within the network.
∑ Number of exchanged messages: tracks the total number of messages that have been exchanged among nodes. This metric
captures the communication overhead.
Table-1: Comparison of analytical results of LDPN, RIM, and DARA algorithms
Property LDPN RIM DARA
Maximum number of nodes [1/2(N-1)] N-1 N-3
involved
Maximum messages send [3/2(N-1)] 2N-1 5N-3
Maximum distance travelled by R r/2 R
a node
Maximum distance travelled by [r/2(N-1] [r/2(N-1] r(N-3)
all engaged nodes
As we mentioned earlier, LDPN strives to restore network connectivity while minimizing the recovery overhead and maintaining the
shortest path lengths at their prefailure value. The above table-1 shows the comparison between LDPN, RIM and DARA algorithms.
We group the results into two sets: 1) overhead related metrics and 2) path length validation metrics. We compare the performance of
LDPN with RIM [6] and DARA [4], which are the most effective published solutions for the tolerance of a single node failure in
WSAN. The first set compares LDPN, which runs in a distributed manner, to a centralized version that provides the least traveled
distance. We also compare LeDiR to RIM in terms of recovery overhead. LDPN selects the smallest partition and tries to maintain the
existing communication links between nodes within the block that will perform the recovery. The movement technique and operation
are closer to RIM; in other words, RIM can achieve the same objective while DARA cannot guarantee it. Therefore, in the firstset, we
compare LDPN with RIM and not DARA. In addition to the centralized version of LeDiR and RIM the second set of simulation
experiments compares LeDiR to DARA. The reason is that both RIM and DARA are designed particularly to restore network
connectivity. However, RIM and DARA do not care whether a prefailure shortest path gets extended or not. Therefore, the path length
validation metrics would assess how frequent the shortest paths are affected by contemporary schemes and LDPN contribution to
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
college,TamilNadu,India,March 28-29,2014
sustaining the prefailure path lengths.
6. CONCLUSION AND FUTURE ENHANCEMENTS
In recent years, wireless sensor and actor (actuator) networks (WSANs) have started to receive growing attention due to their potential
in many real-life applications. In our project has discussed an important problem in mission critical WSANs, that is, reestablishing
network connectivity after multiple node failure at contemporarily without extending the length of data paths. The proposed a new
LDPN algorithm that restores connectivity by use of PILOT nodes. Least-Disruptive with PILOT node algorithm ensured that the
simultaneous node recovery from multiple node failure with maintain the minimal topology changes. LDPN also works very well in
dense networks and yields close to optimal performance even when nodes are partially aware of the network topology. Least -
Disruptive with PILOT Node algorithm investigated only contemporary node recovery from a failure but not discussed about nodes
life time. Our future plan includes considering the energy consumption of PILOT nodes for large scale networking environment.
ACKNOWLEDGEMENT
It’s my immense pleasure to thank my Guide and Mentor for his earnest efforts and incessant support throughout my project
accomplishment.
REFERENCES
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[3] Ameer A. Abbasi, Mohamed F. Younis and Uthman A. Baroudi, Jan. 2013, “Recovery from a node failure in wireless
sensor-actor networks with minimal topology changes,”. IEEE Transactions on vehicular technology, vol. 62, no. 1.
[4] A.Abbasi, M. Younis, and K. Akkaya, Sep. 2009, Movement-assisted connectivity restoration in wireless sensor and actor
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[5] K. Akkaya, A. Thimmapuram, F. Senel, and S. Uludag, Mar. 2008 “Distributed recovery of actor failures in wireless sensor
and actor networks,” in Proc.IEEE WCNC, Las Vegas, NV, pp. 2480–2485.
[6] M. F. Younis, Sookyoung Lee, and Ameer Ahmed Abbasi, Dec. 2010, “A Localized algorithms for restoring internode
connectivity in networks of movable sensor,” IEEE Trans. On computers, vol. 59, no. 12.
[7] K. Akkaya, F. Senel, A. Thimmapuram, and S. Uludag, Feb. 2010, “Distributed recovery from network partitioning in
movable sensor/actor networks via controlled mobility,” IEEE Trans. Comput., vol. 59, no. 2, pp. 258–271.
[8] F. Senel, M. Younis, and K. Akkaya, May 2011, “Bio-inspired relay node placement heuristics for repairing damaged
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[9] S. Lee and M. Younis, Jun. 2009, “QoS-aware relay node placementin a segmented wireless sensor network,” in Proc. IEEE
ICC, Dresden, Germany, pp. 1–5.
[10]Nikolaos A. Pantaziz, Stefanos A. Nikolodakis and Dimitrios D. Vergados, Jun.2010, “Topology Management wireless
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
college,TamilNadu,India,March 28-29,2014
ALRID: ANONYMOUS LOCATION BASED ROUTING WITH
INTRUSION DETECTION IN MANET
1 2 3
R.Ramkumar , N.Sivabalasubramanian , T.Parameswaran
1
PG Student, CSE, Anna University Regional Centre, Coimbatore, Tamil Nadu, India, [email protected]
2
PG Student, CSE, Anna University Regional Centre, Coimbatore, Tamil Nadu, India, [email protected]
3
Assistant Professor, CSE, Anna University Regional Centre, Coimbatore, Tamil Nadu, India, [email protected]
Abstract
Nowadays security is important thing in MANET especially in the Military side. An Attacker can monitor or analyze the network to
identify the source and destination. So MANET uses Anonymous Routing Protocols to hide the original identity of the nodes in the
network. It provides the anonymity protection for the source and Destination from attackers. Existing anonymous routing protocols
relying on either hop-by-hop encryption or redundant traffic either generates high cost or cannot provide full anonymity protection to
data sources, destinations, and routes. Here we propose the ALRID: anonymous location based routing with intrusion detection in
MANET. This protocol provides high anonymity for sources, destinations and routes with intrusion detection at low cost. To provide
the strength for source and destination anonymity here we are hiding the initiators and receivers among many initiators and receivers.
It partitions the network field into zones and randomly chooses intermediate relay nodes, which is used to form the untraceable route
between source and destination nodes. This ALRID provides the security for intersection attacks and timing attacks. Here we use the
trust based approach to detect the misbehaviour node in the network, to avoid the packet routing via misbehaviour nodes or selfish
nodes. This trust based approach uses principle of flow conservation mechanism for misbehaviour detection. By using this we can also
avoid the routing attacks and packet dropping attacks. This protocol provides the more security when compare to the other
anonymous routingprotocols with high efficient and low cost.
Index Terms: Mobile Ad Hoc Networks, Anonymity, Routing Protocol, Geographical Routing
1. INTRODUCTION
Rapid development of Mobile Ad Hoc Networks (MANETs) has stimulated numerous wireless applications that can be used
in a wide number of areas such as armed forces, education, commerce, emergency services, and entertainment. MANETs has
infrastructure less property and also the nodes are decentralised so the MANET is a network which can be susceptible to many kind of
attacks. An attacker can easily attack the nodes and also makes the nodes to misbehave. Nodes in MANETs are moving anywhere in
the network due to the mobility of the nodes, so an attacker also easily comes into the network to make some attack in thenetwork. In
defence oriented applications needs anonymity for the each and every nodes of the network at the time of packet transmissions.
Although anonymity may not be a necessity in civil oriented applications, it is most essential in defence applications. Consider a
MANET deployed in a battleground. Through traffic study, enemies may interrupt transmitted packets, track our soldiers, and attack
the commanding officer nodes and chunk the data transmission by comprising relay nodes thus putting us at a deliberate drawback.
Anonymous routing protocols are essential in MANETs to provide safe interactions by hiding node identities and preventing
traffic study attacks from external observers. Anonymity in MANETs includes identity and location anonymity of data sources and
destinations, as well as route anonymity. Already existing anonymous routing protocols in MANETs are mainly classified into two
categories: hop-by-hop encryption [4],[6],[9],[12],[13], and redundant traffic [2],[3],[7] Most of the recent approaches are restricted by
focusing on enforcing anonymity at a heavy cost to precious resources. Because that used public-key-based encryption and high traffic
produce significantly high cost. In addition, many existing approaches cannot provide all of the abovementioned anonymity
protections. For example, ALARM[3] cannot protect the position anonymity of source and target, SDDR[15] cannot provide direction
anonymity, and ZAP[2] only focuses on target anonymity. Many anonymity routing algorithms are based on the geographic routing
protocol (e.g., Greedy Perimeter Stateless Routing (GPSR[8])), that acquisitively forwards a packet to the node closest to the
destination. However, the protocol’s harsh relay node selection makes it easy to expose the source and target and to analyze traffic.
IJESAT | Mon-Mon 2012
Available online @ http://www.ijesat.org 1
In order to provide high anonymity defence (for sources, target, and route) with low cost, we offer a protocol ALRID: Anonymous
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Proceedings of 2nd International Conference on Science,Engineering and Management,Srinivasan Engineering
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Location-based Routing with Intrusion Detection in MANET. It partitions the network field into zones and randomly chooses
intermediary relay nodes, which is used to form the untraceable route between source and target nodes. Intrusion detection techniques
[5],[10],[15] is used to find the malicious nodes in the network, it avoids selecting the attacker as a relay node. Principle of flow
conservation [5] is used as intrusion detection technique. It then randomly chooses a node in the other zone as the subsequent relay
node and uses the GPSR [8] algorithm to send the information to the relay node. In the final step, the data is broadcasted to k nodes in
the target zone, provided that k-anonymity to the target. In addition, this anonymous routing has a approach to hide the data initiator
among a many number of initiators to strengthen the anonymity protection of the source. This anonymous routing is also flexible to
intersection attacks, timing attacks and packet dropping attacks. In summary, the involvement of this work includes:
∑ Intrusiondetection: ALRID uses principle of flow conservation mechanism to find malicious nodes in the network.
∑ Anonymous routing: ALRID provides route anonymity, identity, and location anonymity of source and destination.
∑ Low cost: This routing protocol achieves low cost. This protocol does not rely on hop-by-hop encryption and redundant
traffic, this routing protocol mainly uses randomized routing of one message copy to provide anonymity protection.
∑ Resilience to intersection attacks and timing attacks: ALRID has a strategy to avoid intersection attacks and timing attacks. It
can also avoid timing attacks because of its non fixed routing paths for a source and destination
2.ANONYMOUS LOCATION BASED ROUTING WITH INTRUSION DETECTION
2.1 Location service and node identities
As preceding works we assume that the public key and position of the target node in the network of a data transmission can
be known by others, but its real identity needs protection. We can utilize a secure location service to provide the information of each
every node’s location and public key information. Such a location service enables a source node securely obtain the location and
public key of the target node. The public key is used to establish symmetric key Ks for secure communication between source and
destination. The destination location enables a node to determine the subsequent hop in geographic routing. Specifically, trusted
standard nodes or dedicated service provider nodes are used to provide location service. When a node wants to know the location and
public key of another node, then it will sign the request containing that node’s identity using its own identity to the location server.
Then, the location server will return an encrypted position of B and its public key, which can be decrypted by A using the pre
distributed shared key between A and its location server. When node A moves, it will update its position to its location server at
regular interval.
The presence of the location servers are contrasting to the ad hoc characteristics of MANETs, and it is not essential to use
location servers in a MANET without security consideration. However, anonymous communication needs third-party servers to
reliably collect and transmit secret information, and this resolution was already used in many of previously proposed works such as
with the advance of wireless access point (AP), the deployment of location server can be conducted by placing several APs in the
whole WIMAX network of civil use at a practical cost. It is difficult to protect location servers in a battle field, but since the location
servers are not essentially be functional all the time and each node only needs to have usable location server, the location server can be
hidden under the ground where anonymous communication is necessary. In an interaction of node communication, a source node S
sends data to a destination node D and the destination reply’s with data. A transmission session is the time period that S and D interact
with each other continuously until they stop. In this anonymous location based routing, each node uses a encrypted id as its node
identifier rather than using its real MAC address, which can be used for hiding the real identities of the nodes. This will provide the
anonymity for the source and destination nodes.
2.2 Principle of flow conservation for intrusion detection
Our work provides a novel approach to securing the data forwarding functionality in mobile ad hoc networks. We recommend an
approach that takes advantage of the principle of flow conservation [5] in the network. This states that every bytes/packets sent to a
node, and not intended for that node, are likely to exit the node. In this segment we present the theoretical point view of principle of
flow conservation, and then we demonstratethat by making some practical assumptions and adaptations, our algorithm can handle
with the practical problems that are encountered in real MANETs. We formally begin the principle of flow conservation over anideal
static network model:
online @ http://www.ijesat.org 2
∑ Let v be a node such that v V, where V= {v , v , v ... v } is the set of all nodes in the network, N is the count of total
j j 1 2 3 N
number of nodes in the network, and j= 1, 2 ... N.
∑ Let U be the subset of nodes ∈in the network which are neighbours of v, i.e. U is the neighbourhood of v. It follows that v ∉
j j j j j
U and also U ⊂V.
j j
∑ Let ∆t be the period of time elapsed between two points in time to and t1 such that ∆t = t1 –t0.
∑ Let T be the total number of packets that node v has successfully sent to node v for v to forward to a further node; v U,
ij i j j i j
v U, i≠ j and T (t0) = 0
j i ij
∈
∈
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