MOBILE DATA COLLECTOR FOR SECURE TIME SYNCHRONIZATION IN CLUSTERED WIRELESS SENSOR NETWORK

Secure time synchronization is a key requirement for many sophisticated application running on these networks. Most of the existing secure time synchronization protocols incur high communication and storage costs and are subject to a few known security attacks. In wireless sensor network (WSN), lifetime of the network is determined by the amount of energy consumption by the nodes. To improve the lifetime of the network, nodes are organized into clusters, in which the cluster head (CH) collects and aggregates the data. A special node called mobile data collector (MDC) is used to collect the data from the CH and transfer it to the base station (BS) By using proposed method MDC authenticated to CH by computing shared secret keys on the fly. Once the MDC and CH are authenticated, all the sensor nodes in the cluster are synchronized, time synchronization reduce the communication and storage requirements of each CH. Security analysis of this proposed system shows that it is highly robust against different attacks namely compromised CH, reply attack, message manipulation attack as well as pulse delay attack

Solution Development to Prevent Crime Rate

Crime is one of the major problems encountered in a society. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. Preventive are taken to reduce the increasing number of cases of crime. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analysing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyse crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyse, visualize and predict crime using crime data set. We analyse data objects using machine learning techniques. Dataset is classified on the basis of tree based algorithm. In this prediction is done using random forest algoritm according to various types of crimes taking place in different states and cities. Crime mapping will help the administration to plan strategies for prevention of crime, further using Random forest algorithm technique data can be predicted and visualized in various form in order using leaflet and shiny package to provide better understanding of crime patterns