Comparison of Gray Hole Attack in Manet in OLSR Protocol
In this era of wireless devices, Mobile Ad-hoc Network (MANET) has become an indivisible part for communication for mobile devices. Therefore, interest in research of Mobile Ad-hoc Network has been growing since last few years. In this paper we have discussed GRAY Hole attack in OLSR routing protocols in MANET. Security is a big issue in MANETs as they are infrastructure-less and autonomous. Main objective of writing this paper is to apply gray Hole attack in MANET& know How its effect on the MANET Environment. This article would be a great help for the people conducting research on real world problems in MANET security
Published by: Rohit Katoch, Anuj Gupta
Author: Rohit Katoch
Paper ID: V2I5-1156
Paper Status: published
Published: September 23, 2016
A Review on Traffic Classification Methods in WSN
In a wireless network it is very important to provide the network security and quality of service. To achieve these parameters there must be proper traffic classification in the wireless network. There are many algorithms used such as port number, deep packet inspection as the earlier methods and now days KISS, nearest cluster based classifier (NCC), SVM method and used to classify the traffic and improve the network security and quality of service of a network.
Published by: Jaskirat Singh, Harpreet Kaur Saini
Author: Jaskirat Singh
Paper ID: V2I5-1154
Paper Status: published
Published: September 20, 2016
A Novel Algorithm in 2 Level Aggregations for WSN in Multi Interface Multichannel Routing Protocol
Energy efficiency is an important metric in resource constrained wireless sensor networks (WSN). Multiple approaches such as duty cycling, energy optimal scheduling, energy aware routing and data aggregation can be availed to reduce energy consumption throughout the network. This thesis addresses the data aggregation during routing since the energy expended in transmitting a single data bit is several orders of magnitude higher than it is required for a single 32 bit computation. Therefore, in the first paper, a novel nonlinear adaptive pulse coded modulation-based compression (NADPCMC) scheme is proposed for data aggregation. A rigorous analytical development of the proposed scheme is presented by using Lyapunov theory. Satisfactory performance of the proposed scheme is demonstrated when compared to the available compression schemes in NS-2 environment through several data sets. Data aggregation is achieved by iteratively applying the proposed compression scheme at the cluster heads. The second paper on the other hand deals with the hardware verification of the proposed data aggregation scheme in the presence of a Multi-interface Multi-Channel Routing Protocol (MMCR). Since sensor nodes are equipped with radios that can operate on multiple non-interfering channels, bandwidth availability on each channel is used to determine the appropriate channel for data transmission, thus increasing the throughput. MMCR uses a metric defined by throughput, end-to-end delay and energy utilization to select Multi-Point Relay (MPR) nodes to forward data packets in each channel while minimizing packet losses due to interference. Further, the proposed compression and aggregation are performed to further improve the energy savings and network lifetime
Published by: Luv Kumar Pal, Anil Panday
Author: Luv Kumar Pal
Paper ID: V2I5-1151
Paper Status: published
Published: September 13, 2016
Railway Bridge Track Surveying System for Accident Reduction
Abstract -As railroad bridges and tracks are very important infrastructures, which has direct effect on railway transportation, there safety is utmost priority for railway industry. This project aims at monitoring the tracks on the bridges along with structural health condition of the bridge for accidents reduction. In this paper we introduces railway tracks and bridge monitoring system using wireless sensor networks based on ARM processor. We designed the system including sensor nodes arrangement , collecting data, transmission method and emergency signal processing mechanism of the wireless sensor network.. The proposed system reduces the human intervention, which collects and transmit data . The desired purpose of the proposed system is to monitor railway infrastructure for accident reduction and its safety.
Published by: Vinod Bolle, Santhosh kumar banoth, Puja Khangar
Author: Vinod Bolle
Paper ID: V2I5-1150
Paper Status: published
Published: September 12, 2016
Hybrid Algorithm for Cluster Head Selection Based on Energy in MANET
— In the existing system is only find in cluster head using cluster based routing protocol algorithm. This algorithm is not based in energy level of cluster head selection. The cluster head can communicate with other cluster heads, member nodes and gateways. That time the cluster head energy level is low. So the cluster head can’t communicate with other nodes. That the same time the congestion will be occurs and packet can’t be transfer in the nodes. It will take more time to complete the packet transmission. This approach illustrates that the proposed method is a routing protocol. The proposed research we have used no of connection in a group or cluster. Every cluster has a cluster head and the cluster head directly interconnect with the improper place. The results of proposed method are comparison with existing Leach Protocol. Here base connection is located to equal distance of a cluster and it’s directly communicating to the cluster connection. When a cluster or group is selected after that it’s force level is not considered. This method is increasing to life instance of network. As compare LEACH and Proposed method, we have noticed proposed methodhave better force, life time, less delay, better transmission and consumed less time. LEACH Protocol is based on the cluster to make comparison of native parameters so that we design the proposed methodl cluster based. Cluster used no of group to increase the performance. This consist many advantages which are listed below. Existing Number of groups are low. We can analyses more number of groups here. Every group’s stage check in this proposed research. Proposed method routing protocol have better result as compared to LEACH protocol. As cluster-head dies, series is rebuilt to bypass the deceased node. So the initial topology is not affected. Head node receives all the aggregated data moreover transmits further to cluster-head.
Published by: Updesh Gangwar, Ravi Shankar Shukla
Author: Updesh Gangwar
Paper ID: V2I5-1148
Paper Status: published
Published: September 9, 2016
Mammogram Image Nucleus Segmentation and Classification using Convolution Neural Network Classifier
Breast Cancer is one of the dangerous diseases which lead in resulting deaths among women. This is due to the presence of cancerous cells that are produced in extra amount of proportion which can replace the neighboring non-cancerous cells or it can infect all over the body. As the breast cancer concerns women mostly at the age of 40, they are asked to attain the regular mammographic screening, since mammography is most reliable method for cancer detection at early stages. Mammogram is the most common method used for breast imaging. It helps in examine the presence of cancer at early stages and help in reducing the mortality rate by 25-30% in screened women. There occur many different types of breast cancer such as: mass, micro calcification clusters, architectural distortion and asymmetry breast tissue. This dissertation carries the masses problem and deals with its shape and texture feature for classification. Various type of techniques and methodologies are present in mammography which helps to find out the presence of cancer and also multiple ways to detect it in its early stage so that the patient affected by it could not lead to death. Mammography is the most common, safe and inexpensive methodology suggested whose standard image database could be used for training the learning machine. In this dissertation nucleus segmentation is used to find out the region of interest (ROI). The result of ROI is further used for extracting the valuable shape and textural features by using geometrical features, GLCM and GLDM for classifying the cancer through the machine learning approach i.e. CNN (Convolution neural networks). CNN remove the overlapping of features obtained after segmentation. Hence, CNN is used to evaluate the performance through defining accuracy, precision, and recall and also compare the results with existing logistic regression and neural network classification technique.
Published by: Prabhjot kaur
Author: Prabhjot kaur
Paper ID: V2I5-1147
Paper Status: published
Published: September 9, 2016
