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This work proposes a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. It also demonstrates an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. Simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. Minimizing router overhead taken as a main goal.

The standard methods of identification such as PIN Numbers (Personal Identification Number), Passwords, smart cards can be easily stolen and can be misused easily. To overcome this, biometric is introduced, as it will be unique to each individual. In this modern world, security has become a serious threat. So many biometric securities have also come up to secure. In biometrics, the iris has become more popular and widely used biometric because of its stability. This iris is researched for the past two decades along with computer technology. This paper will tell about basic concepts of iris, fingerprint, and securing iris with Cancellable Biometrics. Often we get iris templates and store in server or database. There is a chance of losing the data as well as security is a big question here. Here we are proposing a system for protecting iris templates using bloom filters with feature fusion of iris with fingerprint to provide security. Bloom filters are a useful asset in the fields of computer science. In feature-level fusion, the function units originating from more than one biometric assets are consolidated into a single feature set by the appliance of acceptable feature standardization, transformation, and reduction schemes. We can combine bloom filters with feature level fusion for more secure iris templates., Comment: Volume 8, Issue 10, 2020, Aegaeum Journal