Research
Performance Analysis of MANET Routing Protocols – DSDV, DSR, AODV, AOMDV Using Ns-2
A Mobile Ad hoc Network (MANET) eliminates the complexity of an infrastructure configuration and allows wireless devices to communicate with each other independent of central infrastructure. It does not rely on a base station to coordinate the flow of messages to nodes in the network. A primary challenge for each device is to maintain the information to route traffic and data packets. Here, in our paper we analyze the performances of Destination Sequenced Distance Vector Routing (DSDV), Dynamic Source Routing (DSR), Ad hoc On-demand Distance Vector (AODV), Ad hoc On-demand Multi-path Distance Vector (AOMDV) protocols based on the Quality of Service metrics i.e., Packet Delivery Ratio, Packet Loss, Delay, Control Packet Overhead and Throughput using the Network Simulator (ns-2). In this paper we are presenting functionality, benefits, limitations and simulation results for the above mentioned routing protocols.
Application Layer Multicasting Overlay Protocol a NARADA Protocol
The conventional wisdom has been that Network Layer Internet protocol(IP) is the natural protocol layer for implementing multicast related functionality but it is still plagued with concerns pertaining to scalability, network management, deployment and support for higher layer functionality such as error, flow and congestion control. In this context, an alternative architecture is, Application layer multicast (End Systems Multicasting), where at Application layer, implements all multicast related functionality including membership management and packet replication. This shifting of multicast support from routers to end systems has the potential to address the most problems associated with IP multicast. In Application-layer multicast, applications arrange themselves as a logical overlay network and transfer data within the overlay network (between end hosts). In this context, we study these performance concerns in the context of the NARADA protocol (an application layer multicasting protocol). In Narada, end systems self-organize into an overlay structure using a fully distributed protocol. We present details of NARADA and evaluate it using NS-2 simulations. Our results indicate that the performance penalties are low both from the application and the network perspectives. We believe the potential benefits of transferring multicast functionality from routers to end systems, significantly outweigh the performance penalty incurred.
Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach
Data mining based information processing in Wireless Sensor Network (WSN) is at its preliminary stage, as compared to traditional machine learning and WSN. Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN. Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences represent a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary. One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime. This paper will survey Data Mining in WSN application from two perspectives, namely the Network associated issue and Application associated issue. In the Network associated issue, different machine learning algorithms applied in WSNs to enhance network performance will be discussed. In Application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.
