Energy Efficient Location Aided Routing Protocol for Wireless MANETs

A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any centralized access point, infrastructure, or centralized administration. In this paper we introduce an Energy Efficient Location Aided Routing (EELAR) Protocol for MANETs that is based on the Location Aided Routing (LAR). EELAR makes significant reduction in the energy consumption of the mobile nodes batteries by limiting the area of discovering a new route to a smaller zone. Thus, control packets overhead is significantly reduced. In EELAR a reference wireless base station is used and the network's circular area centered at the base station is divided into six equal sub-areas. At route discovery instead of flooding control packets to the whole network area, they are flooded to only the sub-area of the destination mobile node. The base station stores locations of the mobile nodes in a position table. To show the efficiency of the proposed protocol we present simulations using NS-2. Simulation results show that EELAR protocol makes an improvement in control packet overhead and delivery ratio compared to AODV, LAR, and DSR protocols.Comment: 9 Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.423, http://sites.google.com/site/ijcsis

NetworkMonitoring System (NMS)

Due to rapid changes and consequent new threats to computer networks there is a need for the design of systems that enhance network security. These systems make network administrators fully aware of the potential vulnerability of their networks. This paperdesigns a Network Monitoring System (NMS) which is an active defense and complex network surveillance platform designed for ISPs to meet their most rigorous security requirements. This system is motivated by the great needof government agencies, ecommerce companies and Web development organizations to secure their computer networks. The proposed system is also used by network administrators to enable them understand the vulnerabilities affecting computer networks. This enables these administrators to improve network security. The proposed system is a lawful network traffic (Internet Service Provider IP trffic) interception system with the main task of obtaining network communications, giving access to intercepted traffic to lawful authorities for the purpose of data analysis and/or evidence. Such data generally consist of signaling, network management information, or the content of network communications. The intercepted IP traffic is gathered and analyzed for network vulnerability in real time. Then, the corresponding TCP/UDP traffic (Web page, email message, VOIP calls, DHCP traffic, files transferred over the LAN such as HTML files, images, and video files, etc.) is rebuilt and displayed. Based on the results of the analysis of the rebuilt TCP/UDP an alarm could be generatedif amalicious behavior is detected. Experimental results show that the proposed system has many

SOMvisua: Data Clustering And Visualization Based on SOM And GHSOM

Text in web pages is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects which make extracting information from text very difficult. Search in text usually finds text similarities between paragraphs in documents. This paper proposes a framework for data clustering and visualization called SOMvisua. SOMvisua is based on a graph representation of data input for Self-Organizing Map (SOM) and Growing Hierarchically Self-Organizing Map (GHSOM) algorithms. In SOMvisua sentences from an input article are represented as graph model instead of vector space model. SOM and GHSOM clustering algorithms construct knowledge from this article. SOMvisua provides a visual animation for eight famous graph algorithms execution with animation speed control. It also presents six types of visualization. For visualization of similarity lists, we use well-known methods that take a similarity list as input and according to the used similarity measure an adjustable number of most similar sentences are arranged in visual form. In addition, this paper presents a wide variety of text searching. We conducted experiments on the SOMvisua using a large document dataset. Then we compared the performance with that of hierarchal clustering with automated topology based SOM and GHSOM clustering to prove the superiority of SOMvisua

Visualizing text similarities from a graph-based SOM

Text in articles is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. This paper introduced how to utilize the capabilities of a modified graph-based Self-Organizing Map (SOM) in showing text similarities. Text similarities are extracted from an article using Google's PageRank algorithm. Sentences from an input article are represented as graph model instead of vector space model. The resulted graph can be shown in a visual animation for eight famous graph algorithms execution with animation speed control. The resulted graph is used as an input to SOM. SOM clustering algorithm is used to construct knowledge from text data. We used a visual animation for eight famous graph methods with animation speed control and according to similarity measure; an adjustable number of most similar sentences are arranged in visual form. In addition, this paper presents a wide variety of text searching. We had compared our project with famous clustering and visualization project in term of purity, entropy and F measure. Our project showed accepted results and mostly superiority over other projects