Wireless Sensor Network Simulation NS2

Wireless Sensor Network Simulation NS2

Wireless sensor network projects only also carry by electronic and also computer science students. They choose this simulation ns2 as their final year project.We are also providing our services to other country students via online execution. It is (like Team Viewer, and also Skype) not only in India.

Sensor Network Simulator and Emulator

   An efficient and powerful sensor network simulator is known as SENSE (Sensor Network Simulator and Emulator), which is also easy to use. The design of SENSE has the three most critical factors as extensibility, reusability, and scalability. We also distinguish three types of users as high-level users, network builders, and also component designers.

   The design decisions and implementations represent that center on these design factors, and also that take full consideration of the needs of all three types of users on Wireless simulation ns2 because of all of the user’s mobiles in a network.

Implementing wireless sensor network simulation using ns2 simulator

CDL in WSN simulation ns2

   Initially, the Virtual-hop localization computes the node locations that also the first phase of CDL. An enhanced version of hop count based localization is named as CDL in simulation ns2. The issue of nonuniform deployment is a practical address by the virtual-hop when compared to the DV-hop scheme on simulation ns2.

   In such contexts, the localization accuracy is improved on this virtual-hop. The subsequent localization process in CDL (filtration and also calibration), expect to achieve higher accuracy and also the efficiency of iteration based on the output of virtual-hop localization, which is also referred to in Wireless sensor network simulation ns2.

   The Wireless simulation ns2 is used CDL filtration during the simulation. In CDL, filtration is a very important concept. The CDL carries out an experiment to examine the efficacy of location calibration without different good nodes and also bad nodes before calibration is illustrated and signify in order.

   In the model-based calibration is indiscriminate calibration, which is straightforwardly called by the CDL. Based on its neighbors’ distance, every node’s location is adjusted directly that is convert from RSSI by using such calibration. The log-normal shadowing model is also used in our Wireless ns2.

RSSI Wireless ns2

   The localization errors of nodes before and after indiscriminate calibration are compared by it. Surprisingly, the output of indiscriminate calibration to be even worse than before is finding out by our developers. The estimated localization error and also irregular RSSI is considered in model-based filtration, which is infeasible.

   The first step to identify whether it is a bad node as matching hop-count neighborhood, which is took by every node. The local connectivity information is mainly utilized by Wireless sensor network simulation ns2. Note that hop-count is indeed a rough estimation of the distance between two nodes.

   Actually, Hop counts offer relatively limited information to the filtration of the distance between two nodes. As a result, it only identifies a small portion of bad nodes with apparently wrong. And coordinate in a neighborhood hop-count matching.

   All the sifted good nodes do have satisfaction location accuracy in order to ensure simulation ns2. They need to further filter bad nodes. As mention, the nonuniform deployment in the wild area should be mention.

Live Tasks
Technology Ph.D MS M.Tech
NS2 75 117 95
NS3 98 119 206
OMNET++ 103 95 87
OPNET 36 64 89
QULANET 30 76 60
MININET 71 62 74
MATLAB 96 185 180
LTESIM 38 32 16
COOJA SIMULATOR 35 67 28
CONTIKI OS 42 36 29
GNS3 35 89 14
NETSIM 35 11 21
EVE-NG 4 8 9
TRANS 9 5 4
PEERSIM 8 8 12
GLOMOSIM 6 10 6
RTOOL 13 15 8
KATHARA SHADOW 9 8 9
VNX and VNUML 8 7 8
WISTAR 9 9 8
CNET 6 8 4
ESCAPE 8 7 9
NETMIRAGE 7 11 7
BOSON NETSIM 6 8 9
VIRL 9 9 8
CISCO PACKET TRACER 7 7 10
SWAN 9 19 5
JAVASIM 40 68 69
SSFNET 7 9 8
TOSSIM 5 7 4
PSIM 7 8 6
PETRI NET 4 6 4
ONESIM 5 10 5
OPTISYSTEM 32 64 24
DIVERT 4 9 8
TINY OS 19 27 17
TRANS 7 8 6
OPENPANA 8 9 9
SECURE CRT 7 8 7
EXTENDSIM 6 7 5
CONSELF 7 19 6
ARENA 5 12 9
VENSIM 8 10 7
MARIONNET 5 7 9
NETKIT 6 8 7
GEOIP 9 17 8
REAL 7 5 5
NEST 5 10 9
PTOLEMY 7 8 4

Workflow

YouTube Channel

Unlimited Network Simulation Results available here.