enhancing techniques for detection and avoidance of congestion in wireless sensor networks

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Enhancing Techniques for Detection and Avoidance of Congestion in Wireless Sensor Networks Scholar C. Ram Kumar Assistant Professor SNS College of Engineering Guide Dr S Karthik Dean - CSE SNS College of Technology

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Enhancing Techniques for Detection and Avoidance of Congestion in Wireless Sensor Networks. Scholar C. Ram Kumar Assistant Professor SNS College of Engineering Guide Dr S Karthik Dean - CSE SNS College of Technology. Introduction. - PowerPoint PPT Presentation

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Brain tumour detection using canny filter with multi-parameter MRI images

Enhancing Techniques for Detection and Avoidance of Congestion in Wireless Sensor NetworksScholar

C. Ram Kumar Assistant ProfessorSNS College of Engineering

Guide

Dr S KarthikDean - CSESNS College of TechnologyIntroductionWireless Sensor Networks are networks that consists of sensors which are distributed in an ad hoc manner.

These sensors work with each other to sense some physical phenomenon and then the information gathered is processed to get relevant results.

Wireless sensor networks consists of protocols and algorithms with self-organizing capabilities.22Example of WSN

33Objectives The main objective is to detect the congestion and also to avoid that using WSN.

Parameters:To reduce packet loss. To improve energy efficiency. To reduce delay.

Congestion which comprises three mechanisms Use dual buffer thresholds and weighted buffer difference for congestion detection, Flexible Queue Scheduler for packets scheduling, A bottleneck-node-based source sending rate control scheme.

Network topology

7Wireless Sensor Network(WSN) vs. Mobile Ad Hoc Network (MANET)WSNMANETSimilarityWirelessMulti-hop networkingSecurity Symmetric Key CryptographyPublic Key CryptographyRoutingSupport specialized traffic pattern. Cannot afford to have too many node states and packet overheadSupport any node pairsSome source routing and distance vector protocol incur heavy control trafficResourceTighter resources (power, processor speed, bandwidth)Not as tight.Route Requests in DSRBASEFHCGIRepresents a node that has received RREQ for D from SDRoute Requests in DSRBASEFHCGIRepresents transmission of RREQBroadcast transmissionDRoute Requests in DSRBASEFHCGI RREQ keeps a list of nodes on the path from the sourceDRoute Reply in DSRSEFDRepresents links on path taken by RREP BAHCGIAd Hoc On-Demand Distance Vector Routing (AODV)Now RFC 3561, based on DSDVDestination sequence numbers provide loop freedomSource sends Route Request Packet (RREQ) when a route has to be foundRoute Reply Packet (RREP) is sent back by destinationRoute Error messages update routesRoute Requests in AODVBASEFHCGIRepresents a node that has received RREQ for D from SDRoute Requests in AODVBASEFHCGIRepresents transmission of RREQBroadcast transmissionDRoute Requests in AODVBASEFHCGI Represents links on Reverse PathDReverse Path Setup in AODVBASEFHDCGI Node C receives RREQ from G and H, but does not forward it again, because node C has already forwarded RREQ onceRoute Reply in AODVBASEFHDCGIRepresents links on path taken by RREP Congestion Detection Congestion Detection can be found by using Buffer State.Buffer state contains1. Accept state,2. Filter state,3. Reject state.Buffer state If 0NQmin (Accept State), If QminNQmax ( Filter State), If QmaxNQ (Reject State).

Flexible Queue SchedulerIn this method, it will dominate the low priority packet when high priority packet arrives in queue.When the queue overflows, high priority data may be dropped.Dynamically select the next packet to send based on the Round Robin algorithm.In order to overcome the disadvantage in this method, Bottleneck node based source data sending rate control is used. Bottleneck method Determine routing path status from a certain node to sink. Bottleneck node detection and source data sending rate control. Using this scheme, source data sending rate can be regulated more accurately.Determination of routing path status from a certain node to sinkIts child node overhears this information and compares its own forwarding delay D (i) with its parent ps data forwarding delay D (p) and does the following calculation: Dmax (i) =MAX {D (p), D (i)}Where,Dmax (i) is the path status from node i to sink. This process is recursively computed up to the final source node.Bottleneck node detection and data sending rate controlWhen source node s overhears data from its parent p, it extracts the delay information piggybacked in the data packets and set its data sending rate Gs as: Gs=1/Dmax (p)Energy Efficiency The drawbacks of packet drop and improves the energy efficiency as well as, if the energy level is reduced to the particular child node during transmission of packets, it informs the parent node to change the transmission to another child node which is nearest to it for preventing the packet drop.Routing challenges and design issuesNode deploymentData routing methodsNode/link heterogeneityFault tolerance CoverageTransmission mediaConnectivityData aggregationQuality of ServiceData MuleData Mule a mobile entity present in the environment that will pick up data from the node when in range, buffer it, and drop off the data at base stationex: People, Vehicles, Livestock

Data MuleData Mule

Base Station

Leaf Node

Data Mule

Data Mule

Base Station

Data Mule - ApplicationsCollecting a data in a sparse sensor networkTracking movement of mobile elementsVehiclesLivestockWild AnimalsData Mule

Base Station

Habitat Monitoring on Great Duck Islandhttp://www.greatduckisland.net/Intel Research Laboratory at Berkeley initiated a collaboration with the College of the Atlantic in Bar Harbor and the University of California at Berkeley to deploy wireless sensor networks on Great Duck Island, Maine (in 2002)Monitor the microclimates in and around nesting burrows used by the Leach's Storm PetrelGoal : habitat monitoring kit for researchers worldwide

Fire BugWildfire Instrumentation System Using Networked SensorsAllows predictive analysis of evolving fire behaviorFirebugs: GPS-enabled, wireless thermal sensor motes based on TinyOS that self-organize into networks for collecting real time data in wild fire environmentsSoftware architecture: Several interacting layers (Sensors, Processing of sensor data, Command center)A project by University of California, Berkeley CA.

33Sensors : Thermal, light, pressure, GPS, Accelerometer More can be done here (overview of the protocol)

Preventive Maintenance on an Oil Tanker in the North Sea: The BP Experiment

Collaboration of Intel & BP Use of sensor networks to support preventive maintenance on board an oil tanker in the North Sea.A sensor network deployment onboard the ship System gathered data reliably and recovered from errors when they occurred.The project was recognized by InfoWorld as one of the top 100 IT projects in 2004,

Rumor RoutingBasic schemeEach node maintainA lists of neighborsAn event table

When a node detects an eventGenerate an agentLet it travel on a random pathThe visited node form a gradient to the event

When a sink needs an eventTransmit a query The query meets some node which lies on the gradientRoute establishment

Schemes to be usedDCAR Mechanism

Water drop Algorithm

Ant Algorithm

LEACH Low Energy Adaptive Clustering Hierarchy THANKING YOU