A Trigger Identification Service for Defending Reactive Jammers in Wireless Sensor Network

ABSTRACT
During the last decade, Reactive Jamming Attack has emerged as a great security threat to wireless sensor networks, due to its mass destruction to legitimate sensor communications and difficulty to be disclosed and defended. Considering the specific characteristics of reactive jammer nodes, a new scheme to deactivate them by efficiently identifying all trigger nodes, whose transmissions invoke the jammer nodes, has been proposed and developed. Such a trigger-identification procedure can work as an application-layer service and benefit many existing reactive-jamming defending schemes. In this paper, on the one hand, we leverage several optimization problems to provide a complete trigger-identification service framework for unreliable wireless sensor networks. On the other hand, we provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios. Theoretical analysis and simulation results are included to validate the performance of this framework.
EXISTING SYSTEM:
Reactive Jamming Attack has emerged as a great security threat to wireless sensor networks, due to its mass destruction to legitimate sensor communications and difficulty to be disclosed and defended. Among the various attacks, jamming attack where a jammer node disrupts the message delivery of its neighboring sensor nodes with interference signals, has become a critical threat to WSNs.
PROPOSED SYSTEM:
We present a simulation based trigger-identification service for reactive-jamming in wireless sensor networks, which promptly provides the list of trigger-nodes using a lightweight decentralized algorithm, without introducing neither new hardware devices, nor significant message overhead at each sensor node.

MODULES:
ü Network Model
ü Attacker Model
ü Jamming range
ü Triggering range
ü Jammer distance

MODULES DESCRIPTION:
Network Model
We consider a wireless sensor network consisting of n sensor nodes and one base station (larger networks with multiple base stations can be split into small ones to satisfy the model). Each sensor node is equipped with a globally synchronized time clock, omnidirectional antennas, m radios for in total k channels throughout the network, where k > m. For simplicity, the power strength in each direction is assumed to be uniform, so the transmission range of each sensor can be abstracted as a constant rs and the whole network as a unit disk graph (UDG) G ¼ ðV ;EÞ, where any node pair i; j is connected iff the Euclidean distance between i; j: _ði; jÞ _ rs. We leave asymmetric powers and polygonal transmission area for further study.
Attacker Model
We consider both a basic attacker model and several advanced attacker models in this paper. Specifically, we provide a solution framework toward the basic attacker model, and validate its performance toward multiple advanced attacker models theoretically and experimentally

Jamming range
R. Similar to the sensors, the jammers are equipped with omnidirectional antennas with uniform power strength on each direction. The jammed area can be regarded as a circle centered at the jammer node, with a radius R, where R is assumed greater than rs, for simulating a powerful and efficient jammer node. All the sensors within this range will be jammed during the jammer wake-up period. The value of R can be approximated based on the positions of the boundary sensors (whose neighbors are jammed but themselves not), and then further refined.
Triggering range
On sensing an ongoing transmission, the decision whether or not to launch a jamming signal depends on the power of the sensor signal Ps, the arrived signal power at the jammer Pa with distance r from the sensor, and the power of the background noise Pn.


Jammer distance
Any two jammer nodes are assumed not to be too close to each other, i.e., the distance between jammer J1 and J2 is _ðJ1; J2Þ > R. The motivations behind this assumptions are three-fold: 1) the deployment of jammers should maximize the jammed areas with a limited number of jammers, therefore large overlapping between jammed areas of different jammers lowers down the attack efficiency; 2) _ðJ1; J2Þ should be greater than R, since the transmission signals from one jammer should not interfere the signal reception at the other jammer. Otherwise, the latter jammer will not able to correctly detect any sensor transmission signals, since they are accompanied with high RF noises, unless the jammer spends a lot of efforts in denoising or embeds jammer-label in the jamming noise for the other jammers to recognize. Both ways are infeasible for an efficient attack; 3) the communications between jammers are impractical, which will expose the jammers to anomaly detections at the network authority.

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:

•         System                 : Pentium IV 2.4 GHz.
•         Hard Disk            : 40 GB.
•         Floppy Drive       : 1.44 Mb.
•         Monitor                : 15 VGA Colour.
•         Mouse                  : Logitech.
•         Ram                     : 512 Mb.


SOFTWARE REQUIREMENTS:

•         Operating system           : - Windows XP.
•         Coding Language :  JAVA



REFERENCE:
Ying Xuan, Yilin Shen, Nam P. Nguyen, and My T. Thai, “A Trigger Identification Service for Defending Reactive Jammers in WSN”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 11, NO. 5, MAY 2012