Friday, August 28, 2015

CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes

CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes

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.
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.

·        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.

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.

ü Reduce the selfish nodes
ü Increase the throughput
ü Decrease the overhead



·        Network Topology
·        Local Watchdog
·        Diffusion module
·        Detection of Selfish Nodes
·        Performance Evaluation

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.


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.


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)
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.