CoCoWa: A Collaborative Contact-Based Watchdog for
Detecting Selfish Nodes
ABSTRACT:
Mobile ad-hoc networks (MANETs) assume that mobile
nodes voluntary cooperate in order to work properly. This cooperation is a
cost-intensive activity and some nodes can refuse to cooperate, leading to selfish
node behaviour. Thus, the overall network performance could be seriously
affected. The use of watchdogs is a well-known mechanism to detect selfish
nodes. However, the detection process performed by watchdogs can fail,
generating false positives and false negatives that can induce to wrong operations.
Moreover, relying on local watchdogs alone can lead to poor performance when
detecting selfish nodes, in term of precision and speed. This is specially
important on networks with sporadic contacts, such as delay tolerant networks
(DTNs), where sometimes watchdogs lack of enough time or information to detect
the selfish nodes. Thus, we propose collaborative contact-based watchdog
(CoCoWa) as a collaborative approach based on the diffusion of local selfish
nodes awareness when a contact occurs, so that information about selfish nodes
is quickly propagated. As shown in the paper, this collaborative approach
reduces the time and increases the precision when detecting selfish nodes.
EXISTING SYSTEM:
The impact of node selfishness on MANETs has been
studied in credit-payment scheme. In credit-payment scheme it is shown that
when no selfishness prevention mechanism is present, the packet delivery rates
become seriously degraded, from a rate of 80 percent when the selfish node
ratio is 0, to 30 percent when the selfish node ratio is 50 percent. The number
of packet losses is increased by 500 percent when the selfish node ratio
increases from 0 to 40 percent. A more detailed study shows that a moderate
concentration of node selfishness (starting from a 20 percent level) has a huge
impact on the overall performance of MANETs, such as the average hop count, the
number of packets dropped, the offered throughput, and the probability of
reachability. In DTNs, selfish nodes can seriously degrade the performance of
packet transmission. For example, in two-hop relay schemes, if a packet is
transmitted to a selfish node, the packet is not re-transmitted, therefore
being lost.
DISADVANTAGES
OF EXISTING SYSTEM:
·
Increase the selfish nodes
·
Increase the packet loss
·
Reduce the throughput
·
Increase overhead
·
In DTNs, selfish nodes can seriously
degrade the performance of packet transmission. For example, in two-hop relay
schemes, if a packet is transmitted to a selfish node, the packet is not
re-transmitted, therefore being lost.
PROPOSED SYSTEM:
v This
project introduces Collaborative Contact-based Watchdog (CoCoWa) as a new
scheme for detecting selfish nodes that combines local watchdog detections and
the dissemination of this information on the network. If one node has
previously detected a selfish node it can transmit this information to other
nodes when a contact occurs. This way, nodes have second hand information about
the selfish nodes in the network.
v The
goal of our approach is to reduce the detection time and to improve the
precision by reducing the effect of both false negatives and false positives.
In general, the analytical evaluation shows a significant reduction of the
detection time of selfish nodes with a reduced overhead when comparing CoCoWa
against a traditional watchdog.
v The
impact of false negatives and false positives is also greatly reduced. Finally,
the pernicious effect of malicious nodes can be reduced using the reputation
detection scheme. We also evaluate CoCoWa with real mobility scenarios using
well known human and vehicular mobility traces.
ADVANTAGES
OF PROPOSED SYSTEM:
ü Reduce
the selfish nodes
ü Increase
the throughput
ü Decrease
the overhead
SYSTEM ARCHITECTURE:
BLOCK DIAGRAM:
MODULES:
·
Network Topology
·
Local Watchdog
·
Diffusion module
·
Detection of Selfish Nodes
·
Performance Evaluation
MODULES DESCRIPTION:
Network Topology
The sensor nodes are
randomly distributed in a sensing field. We are using mobile ad hoc network
(MANET). This is the infra-structure-less network and a node can move
independently. In a MANET, each node not only works as a host and also acts as
a router. We can find the communication range for all nodes. Every node
communicates only within the range. If suppose any node out of the range, node
will not communicate those nodes or drop the packets. The network is modelled
as a set of N wireless mobile nodes, with C collaborative nodes,M malicious
nodes and S selfish nodes (N=C+M+ S). Our goal is to obtain the time and overhead
that a set of D<=C nodes need to detect the selfish nodes in the network.
The overhead is the number of information messages transmitted up to the
detection time.
Local Watchdog
The
Local Watchdog has two functions: the detection of selfish nodes and the
detection of new contacts. The local watchdog can generate the following events
about neighbor nodes: PosEvt (positive event) when the watchdog detects a
selfish node, NegEvt (negative event) when the watchdog detects that a node is
not selfish, and NoDetEvt (no detection event) when the watchdog does not have enough
information about a node (for example if the contact time is very low or it
does not overhear enough messages). The detection of new contacts is based on neighbourhood
packet overhearing; thus, when the watchdog overhears packets from a new node
it is assumed to be a new contact, and so it generates an event to the network
information module.
Diffusion module
The
Diffusion module has two functions: the transmission as well as the reception
of positive (and negative) detections. A key issue of our approach is the
diffusion of information. As the number of selfish nodes is low compared to the
total number of nodes, positive detections can always be transmitted with a low
overhead. However, transmitting only positive detections has a serious
drawback: false positives can be spread over the network very fast. Thus, the transmission
of negative detections is necessary to neutralize the effect of these false
positives, but sending all known negative detections can be troublesome,
producing excessive messaging or the fast diffusion of false negatives.
The
diffusion module can generate indirect events when a contact with neighbour
nodes occurs. Nevertheless, a contact does not always imply collaboration.
Finally,
the probability of generating the indirect events are the following: PosEvt
event & NegEvt event.
Detection of Selfish Nodes
In
this module, we introduce an analytical model for evaluating the performance of
CoCoWa. The goal is to obtain the detection time (and overhead) of a selfish
node in a network. This model takes into account the effect of false negatives.
False positives do not affect the detection time of the selfish node.
The
first transition is when a intermediate collaborative node changes from NoInfo
state to a Positive state. The rate of change depends on the updating parameters.
The
second transition is when a intermediate collaborative node changes. This means
that an intermediate collaborative node changes to a Negative state (a false
negative). We can derive a similar expression for the rate of change to a
(false) Negative state. In this case, when a node contacts with the selfish
node, the reputation is decreased with rate, and also by indirect events with
rate.
Performance Evaluation
In this section, we can
evaluate the performance of simulation. We are using the xgraph for evaluate
the performance. We choose the three evaluation metrics: Packet delivery ratio
– it is the ratio of the number of packet received at destination and number of
packet sent by the source, End-to-End delay – the average time taken for a
packet to be transmitted from the source to destination, Throughput – number of
data received by the destination without any losses, Impact of False Negatives,
Impact of False Positives, Impact of Malicious Nodes.
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø
System : Pentium IV 2.4 GHz.
Ø
Hard Disk :
40 GB.
Ø
Floppy Drive : 1.44
Mb.
Ø
Monitor : 15
VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
Ø Operating system : Windows
XP/7/LINUX.
Ø Implementation : NS2
Ø NS2 Version : NS2.2.28
Ø Front
End : OTCL (Object Oriented
Tool Command Language)
Ø Tool : Cygwin (To simulate in Windows OS)
REFERENCE:
Enrique Hern_andez-Orallo, Member, IEEE, Manuel
David Serrat Olmos, Juan-Carlos Cano, Carlos T. Calafate, and Pietro Manzoni,
Member, IEEE, “CoCoWa: A Collaborative Contact-Based Watchdog for Detecting
Selfish Nodes”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 14, NO. 6, JUNE
2015.
