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Broadcasting in MANETs has traditionally based on flooding, which simply swamps the network with large number of rebroadcast messages in order to reach all network nodes. Although probabilistic flooding has been one of the earliest suggested schemes to broadcasting, there has not been so far any attempt to analyze its performance behavior in a MANET environment. In an effort to fill this gap, this paper investigates using extensive NS-2 simulations the effects of a number of important system parameters in a typical MANET, including node speed, pause time, and node density and packet reach ability on the performance of probabilistic flooding. The results reveal that most of these parameters have a critical impact on the reach ability and the number of saved rebroadcast messages achieved by probabilistic flooding on FSR (Fisheye State Routing) Protocol. The Random way Point Mobility model is selected as the mobile ad hoc network mobility model in NS2 Simulation.

Abstract-Several Agent Oriented Software Engineering (AOSE) methodologies were proposed to build open, heterogeneous and complex internet based systems. AOSE methodologies offer different conceptual frameworks, notations and techniques, thereby provide a platform to make the system abstract, generalize, dynamic and autonomous. Lifecycle coverage is one of the important criteria for evaluating an AOSE methodology. Most of the existing AOSE methodologies focuses only on analysis, design, implementation and disregarded testing, stating that the testing can be done by extending the existing objectoriented testing techniques. Though objects and agents have some similarities, they both differ widely. Role is an important attribute of an agent that has a huge scope and support for the analysis, design and implementation of Multi-Agent System (MAS). The main objective of the paper is to extend the scope and support of role towards testing, thereby the vacancy for software testing perception in the AOSE series will be filled up. This paper presents an overview of role based testing based on the V-Model in order to add the next new component as of AgentOriented Software testing in the agent oriented development life cycle.

Anomaly detection is one of the major requirements of the current age that witnesses a huge increase in online transactions. Data imbalance also poses a huge challenge in the detection process. This paper presents a hybrid metaheuristic algorithm that performs effective anomaly detection on highly imbalanced data. Particle Swarm Optimization is used as the operating algorithm. This algorithm is hybridized by modifying the probabilistic selection using Simulated Annealing. A comparison study was carried out and it was observed that the simulated annealing based PSO showed much prominence when operated on both dominant and submissive data.