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Wireless networks have become so popular in recent years that now a day almost every electronic gadget can be operated in wireless. Wireless ad hoc network is more vulnerable to security threats than wired network due to inherent characteristics and system constraints. This paper mainly addresses attacks due to misbehaving or malicious nodes. We have examined the effect of Black Hole attack on AODV routing and its detection method. I have simulated this attack and determined effect of this attack on network performance by different network scenario. I have also implemented detection method that help to isolates the malicious node in the network. General TermsAODV routing protocol, Attacks, MANET.

SummaryThis paper aims towards probabilistic reasoning and Bayesian‐based recommendations to predict the next movement of a person. The proposed model in this work observes the behavior and movement patterns of humans for a day both at home and at their office to predict their future activities. To achieve this, an efficient model has been designed that provides the probable context‐based location of a person and predicts his next movement based on his behavior on some particular day at a particular time. The proposed model allows ubiquitous services to adapt to uncertain situations in today's world using different mechanisms such as monitoring the human behavior patterns and evaluating the user preferences and profiles. A case study of the office activity chart has been provided, and based on the experimentation performed on the related events, the probability in evaluating some “N”chained events of a person in a consecutive order using the proposed model has been found to be 0.002, which infers that there are fewer chances that the person will perform the same particular sequence of events.

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