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Network Security

In: Computers and Technology

Submitted By parthapratim89
Words 1065
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EXECUTIVE SUMMARY

Selective forwarding or dropping of packets is a serious threat to multi hop communication in a Wireless Sensor Network (WSN). There are various schemes to induce cooperation in a WSN to overcome this problem. In this paper, we have introduced a novel adversary model and have proposed an incentive based scheme to inspire cooperation among nodes in a Geometric Structure based WSN. The scheme has been formally analyzed. The efficacy of the scheme is also established through various simulation experiments.

INTRODUCTION

Nodes in mobile ad-hoc networks are arbitrarily deployed without relying on any fixed network infrastructure. In a multi-hop wireless network, many pairs of nodes cannot communicate directly and must forward data to each other via one or more intermediate forwarding nodes.
Multi-hop communication is not an issue where nodes faithfully forward packets according to a global algorithm. Selfish nodes may like to send their own packets but may not be ready to relay packets for others since relaying packets for others consumes bandwidth and energy. This, in turn, decreases both individual and system throughput and might even lead to loss of connectivity in a network.
Hence, cooperation among the nodes needs to be enforced. The basic aim of any such mechanism is to force nodes to forward packets sent to it by other nodes. There are many proposed solutions which use game theoretic and graph theoretic notions to examine whether cooperation can exist in multi-hop communication while many solutions are proposed based on providing incentives. Incentives can be positive or negative. That is, a node can be made to cooperate within a network either by providing some incentive or by taking punitive actions against a node when its rate of packet forwarding falls below a particular value.

WORK DESCRIPTION

Ad hoc networks (also referred to as packet radio networks) consist of nodes that move freely and communicate with other nodes via wireless links. One way to support efficient communication between nodes is to develop wireless backbone architecture.
In all these papers, nodes are classified in two categories: TRUSTED those who forward packets and MALICIOUS, those who do not like to forward others’ packets. Moreover, malicious nodes, according to these papers are content by dropping packets to conserve their resources. In this paper, we have introduced two further dimensions to this misbehavior model. First, we introduced a ‘rational adversary’ category of nodes. ‘Rational adversary nodes’ do not mind dropping packets if they are not penalized for that. Second, we have incorporated an idea by which ‘malicious’ nodes inspire their neighboring nodes to drop packets.

In order to design backbone network Trusted nodes are elected to form the backbone. These nodes are called clusterheads and gateways. Clusterheads are nodes that are vested with the responsibility of routing messages for all the nodes within their cluster. Cluster heads may form a second tier network, i.e. making another level of hierarchy or they may just pass on the data to the base station. Gateway nodes are nodes at the fringe of a cluster and typically communicate with gateway nodes of other clusters. The wireless backbone can be used either to route packets, or to disseminate routing information, or both. Due to the mobility of nodes in an ad hoc network, the backbone must be continuously reconstructed in a timely fashion, as the nodes move away from their associated clusterheads.

For Leader election algorithm, I want to use generalized clustering heuristics like “Max-Min D-Cluster Formation” made cluster head formation with nodes at most D hops away from a cluster head. These were distributed leader election heuristic for an ad hoc network, guaranteeing that no node is more than D hops away from a leader, where D is a value selected for the heuristic. Thus, this heuristic extends the notion of cluster formation. Existing 1-hop clusters are an instance of the generic D-hop clusters. The proposed heuristic provides load balancing among clusterheads to insure a fair distribution of load among clusterheads. Additionally, the heuristic elects clusterheads in such a manner as to favor their re-election in future rounds, thereby reducing transition overheads when old clusterheads give way to new clusterheads. However, it is also fair as a large number of nodes equally share the responsibility for acting as clusterheads. Furthermore, this heuristic has time complexity of O (d) rounds which compares favorably to O (n) for earlier heuristics for large mobile networks. This reduction in time complexity is obtained by increasing the concurrency in communication.

BASIC IDEA

The heuristic runs for 2d rounds of information exchange. Each node maintains three arrays, WINNER, MAX-MIN-PAN and SENDER, each of size 2d node ids: one id per round of information exchange.

Every node transmits a MYID signal to all nodes in all direction. The MYID signal contains the Node Id of a node. This signal will get transmitted to all the nodes that are in the vicinity of the transmission range of that node. As the nodes complete sending MYID signal it will start receiving the MYID signal from the other nodes. As this is a distributed system, all nodes will be behave as a transmitter first for sending their own MYID signal, and next behave as a receiver to receive the MYID signal from the other nodes. For a time interval the nodes will wait to hear the MYID signal sent by others and once the interval is over, assimilate the node ids from which it has received the MYID signals. The node id list, from which it has received the MYID signal, becomes its neighbour node list. This way the number of neighbours for a node can be determined. Next every node computes a weighted value called PAN for itself.
PAN value can be calculated as follows.

PAN= [(Weight A) X (Power)] + [(Weight B) X Neighbours];

Power = residual battery energy
Neighbours = number of neighbours within the transmission distance of a node.
Weight A = 1% – 99%
Weight B = 99% - 1%

FLOW CHART

Propagate MYID and get the number of Neighbour

Calculate the PAN value for the individual node

Now simulate rounds of flooding algorithm FLOODMAX

If the power of the entire network falls below a threshold, we go for re-clustering.

One of the cluster is below threshold, we select successor

The Linking of Clusters

The determination of the Cluster Head

Now simulate rounds of flooding algorithm FLOODMIN

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