ఇంటర్నేషనల్ జర్నల్ ఆఫ్ సెన్సార్ నెట్‌వర్క్స్ అండ్ డేటా కమ్యూనికేషన్స్

Mobile Ad-Hoc Networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station. MANET is more vulnerable to different types of attacks and security threats because of its characteristics of mobility and dynamic nature. Intrusion means any set of action that attempts to compromise the integrity, confidentiality, availability of resources. We are implementing EAACK secure acknowledge based IDS to remove the drawbacks of Watchdog. By using the schemes of EAACK we propose a cooperative, dynamic hierarchical intrusion detection architecture that addresses these challenges while finding specific and conventional attacks in MANET. The structural design is organized as a dynamic hierarchy in which data is acquired at the leaves and is incrementally aggregated, reduced, and analyzed as it flows upward toward the root. To maintain communications effectiveness, the hierarchy is automatically reconfigured as desired using clustering techniques in which cluster heads are selected based on topology and other criteria. The usefulness of the architecture is demonstrated via black hole attack scenarios in which attack is detected and removed. Reactive routing protocol is used as it provide better efficiency and hence to reduce the network overhead.

This paper presents a method for an indoor pedestrian localization, based on the data that solely are measured by a foot-mounted Inertial Measurement Unit (IMU). To locate the user accurately, a comprehensive Extended Kalman Filter (EKF) with five states is developed. Five different error reduction methods are employed to estimate the errors of all five states. These error reduction methods feed EKF independently, at stance phases or different time intervals of swing phases. The navigation system is developed using the accelerometer and gyroscope measurements and without magnetometer, thus it is insensitive to the presence of metal and magnetic fields, and it is able to estimate the user’s tracked trajectory with the same accuracy in both indoor and outdoor environments. The system does not rely on the measurement from external infrastructure (e.g., RFID). To evaluate the accuracy of the system, several experimental tests are carried out over the known trajectories. Results demonstrate that the error of the estimated tracked trajectory is less than 1% of the total traveled distance.