Mobile ad hoc networks(MANET) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self organize into arbitrary and temporary adhoc network topologies, allowing people and devices to seamlessly internet work in areas with no preexisting communication infrastructure e.g., disaster recovery environments. An ad-hoc network is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad-hoc paradigm. Recently the introduction of new technologies such as Bluetooth, IEEE 802.11 and hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent revolutions have been generating a renewed and growing interest in the research and development of MANET. To facilitate communication within the network a routing protocol is used to discover routes between nodes. The goal of the routing protocol is to have an efficient route establishment between a pair of nodes, so that messages can be delivered in a timely manner. Bandwidth and power constraints are the important factors to be considered in current wireless network because multi-hop ad-hoc wireless relies on each node in the network to act as a router and packet forwarder. This dependency places bandwidth, power computation demands on mobile host to be taken into account while choosing the protocol. Routing protocols used in wired network cannot be used for mobile ad-hoc networks because of node mobility. The ad-hoc routing protocols are divided into two classes: table driven and demand based. This paper reviews and discusses routing protocol belonging to each category.

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on-demand routing protocols for mobile ad hoc networks: dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV). A detailed simulation model with medium access control (MAC) and physical layer models is used to study the interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share similar on-demand behavior, the differences in the protocol mechanisms can lead to significant performance differentials. In this paper, we examine both on-demand routing protocols AODV and DSR based on packet delivery ratio, normalized routing load, normalized MAC load, average end-to-end delay by varying the node density, network loading, and mobility variations for reference point group mobility and random waypoint models. This framework aims to evaluate the effect of mobility models on the performance of mobile ad hoc networks (MANETs) routing protocols. Our results show that the protocol performance may vary drastically across mobility models and performance rankings of protocols may vary with the mobility models used. This effect can be explained by the interaction of the mobility characteristics with the connectivity graph properties.

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. We compare the performance of two prominent on-demand routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR), Ad Hoc On-demand distance Vector Routing (AODV). A detailed simulation model with MAC and physical layer models is used to study the interlayer interactions and their performance implications. We demonstrate that even though DSR and AODV share similar ondemand behavior, the differences in the protocol mechanisms can lead to significant performance differentials. In this paper we examine two on demand routing protocols AODV and DSR based on packet delivery ratio, normalized routing load, normalized MAC load, average end to end delay by varying the number of sources, speed and pause time.

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance. At first, Mahalanobis distance should be calculated for the entire sample, then using T 2-statistic fix a UCL. Above the UCL are treated as outliers which are grouped as outlier cluster and repeat the same procedure for the remaining inliers, until the variance-covariance matrix for the variables in the last cluster achieved singularity. At each iteration, multivariate test of mean used to check the discrimination between the outlier clusters and the inliers. Moreover, multivariate control charts also used to graphically visualizes the iterations and outlier clustering process. Finally multivariate test of means helps to firmly establish the cluster discrimination and validity. This paper employed this procedure for clustering 275 customers of a famous twowheeler in India based on 19 different attributes of the two wheeler and its company. The result of the proposed technique confirms there exist 5 and 7 outlier clusters of customers in the entire sample at 5% and 1% significance level respectively.

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