Search Results (4)

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios. Full article

The global navigation satellite system (GNSS) has been applied to many areas, e.g., the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city, and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades. Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmental interference. The vector tracking (VT) technique is promising to enhance the robustness in high dynamics as well as improve the sensitivity against the weak environment of the GNSS receiver. However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loop can decrease the accuracy of the navigation solution. There are few works present dealing with this issue. In this work, the Allan variance is accordingly exploited to specify a model which is expected to account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is used for proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore, the proposed system model makes use of the innovation sequence so that the process covariance matrix can be adaptively adjusted and updated. The field tests demonstrate the performance of the proposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF). When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VT loop, the real-time kinematic (RTK) positioning and code-based differential global positioning system (DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the other hand, the RTK positioning performance has been increased by maximum 21.40% when compared with the results obtained from the commercial low-cost U-Blox receiver. Full article

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions. Full article

In this paper a quaternion-based Variable-State-Dimension Extended Kalman Filter (VSD-EKF) is developed for estimating the three-dimensional orientation of a rigid body using the measurements from an Inertial Measurement Unit (IMU) integrated with a triaxial magnetic sensor. Gyro bias and magnetic disturbances are modeled and compensated by including them in the filter state vector. The VSD-EKF switches between a quiescent EKF, where the magnetic disturbance is modeled as a first-order Gauss-Markov stochastic process (GM-1), and a higher-order EKF where extra state components are introduced to model the time-rate of change of the magnetic field as a GM-1 stochastic process, namely the magnetic disturbance is modeled as a second-order Gauss-Markov stochastic process (GM-2). Experimental validation tests show the effectiveness of the VSD-EKF, as compared to either the quiescent EKF or the higher-order EKF when they run separately. Full article