Fuad A. Ghaleb

Mobility pattern based misbehavior detection to avoid collision in vehicular adhoc networks

Vehicular Ad-hoc Network (VANET) can improve road safety through collision avoidance. False or bogus information is a real threat in VANET’s safety applications. Vehicles or drivers may react to false information and cause serious problems. In VANETs drivers’ behavioral tendencies can be reflected in the mobility patterns of the vehicles. Monitoring mobility patterns of the vehicles within their transmission range helps them in earlier detection of the correctness of the received message. This paper presents a misbehavior detection scheme (MDS) and corresponding framework based on the mobility patterns analysis of the vehicles in the vicinity of concerned vehicles. Initial simulation results demonstrate the potential of the proposed MDS and framework in message’s correctness detection, hence its corresponding applications in collision avoidance.

Improved vehicle positioning algorithm using enhanced innovation-based adaptive Kalman filter

Accurate positioning is a key factor for enabling innovative applications to properly perform their tasks in various areas including: Intelligent Transportation Systems (ITS) and Vehicular Ad Hoc Network (VANET). Vehicle positioning accuracy depends heavily on positioning techniques and the measurements condition in its surroundings. Several approaches which can be used for improving vehicle positioning accuracy have been reported in literature. Although some positioning techniques have achieved high accuracy in a controlled environment, they suffer from dynamic measurement noises in real environments leading to low accuracy and integrity for some VANET applications. To solve this issue, some existing positioning approaches assume the availability of prior knowledge concerning measurement noises, which is not practical for VANET. The aim of this paper is to propose an algorithm for improving accuracy and integrity of positioning information under dynamic and unstable measurement conditions. To do this, a positioning algorithm has been designed based on the Innovation-based Adaptive Estimation Kalman Filter (IAE_KF) by integrating the positioning measurements with vehicle kinematic information. Following that, the IAE_KF algorithm is enhanced in terms of positioning accuracy and integrity (EIAE_KF) in order to meet VANET applications requirements. This enhancement involves two stages which are: a switching strategy between dead reckoning and the Kalman Filter based on the innovation property of the optimal filter; and the estimation of the actual noise covariance based on the Yule–Walker method. An online error estimation model is then proposed to estimate the uncertainty of the EIAE_KF algorithm to enhance the integrity of the position information. Next Generation Simulation dataset (NGSIM) which contains real world vehicle trajectories is used as ground truth for the evaluation and testing procedure. The effectiveness of the proposed algorithm is demonstrated through a comprehensive simulation study. The results show that the EIAE_KF algorithm is more effective than existing solutions in terms of enhancing positioning information accuracy and integrity so as to meet VANET applications requirements.

Fairness of Channel Assignment Algorithm Based on Weighted Links Ranking Scheme for Wireless Mesh Network

Wireless Mesh Networks (WMNs) are promising means to provide inexpensive deployment, flexible and fast broadband access. Recent WMNs use multiple-radio and multiple channels to provide high performance. However, interference between channels is considered the key challenge for WMN performance. In WMN, the data flows are directed from/to the gateways. Thus, the quality of the critical links close to the gateways should be properly considered during channel assignments. Otherwise, network fragmentation and bottleneck problem may occur which affect the performance of the network due to congestions, and unfair channel distribution. Unfortunately, the existing channel assignments focusing only on the links close to the gateways and neglecting other critical links. This paper proposes a fair channel assignment algorithm based on weighted link ranking scheme in order to minimize the interference and thus improve the capacity of the network. The links are fairly ranked based on multiple criteria obtained from traffic and network topology such as distance from the gateways, interference index, and, traffic load. The results from numerical simulation demonstrate that the proposed channel assignment algorithm has reduced the interference, improving the network capacity, and achieves the fairness of channel distribution.

Motion Artifact Reduction Algorithm Using Sequential Adaptive Noise Filters and Estimation Methods for Mobile ECG

Electrocardiogram (ECG) is beneficial to diagnose various heart diseases. Recently, mobile ECG has received the attention of researchers and practitioners for real time monitoring and automatic diagnosis. However, the performance of ECG’s monitoring system is degraded because of many disturbances created by subject movement, which leads to wrong diagnosis. Several attempts have been performed to remove the noise from clinical ECG signal using various digital signal processing techniques. Those techniques are not directly appropriate to be used for the mobile ECG environment and expected noises. Ultimately, motion artifact still is an open issue in mobile ECG. In this paper, an algorithm is proposed to reduce motion artifact using three sequential adaptive noise filters based on least mean square errors and reference noise estimation methods. The proposed algorithm is evaluated using real dataset that was collected while the subject performing different activities. Results show promising enhancement in the ECG signal quality.

Two Stage Integration of GPS, Kinematic Information, and Cooperative Awareness Messages Using Cascaded Kalman Filters

Availability of accurate and continuous positioning information is a fundamental requirement for Vehicular Ad Hoc Networks. However, existing positioning approaches does not fulfill the required accuracy of many VANET’s applications and services. Integrating GPS with vehicles’ kinematic information (GPS/DR) is widely suggested for vehicular positioning. In many cases where the GPS is unavailable or instable for long time, this integration resulted in inaccurate positioning. Recently, cooperative positioning (CP) based on vehicle-to-vehicle (V2V) communication have been proposed as an alternative for GPS/DR in many ad hoc networks. Even though, CP needs high communication to achieve the required accuracy, which is not guaranteed in VANET harsh environment. In this paper, two-stage integration algorithm is proposed to ensure continuous and accurate positioning information. The proposed algorithm integrates GPS and kinematic sensors measurements, as well as neighboring mobility information based on two cascaded Kalman filters (KF). The first KF is used to integrate GPS information with kinematic sensors measurements (dead reckoning). Meanwhile, the second KF is used to integrate the results of the first KF with the neighboring mobility information. Results show that the proposed algorithm outperformed the conventional integration algorithm in terms of positioning accuracy under the tested scenario.

Mobility information estimation algorithm using Kalman-filter for vehicular ad hoc networks

In vehicular networks, the exchange of mobility information is considered as the basis for many applications. The quality of this information affects applications and networks performance. False mobility information leads applications, network services and security services to make wrong decisions. Accordingly, many verification approaches were proposed as a security countermeasure. However, most of these approaches assume accurate mobility information regardless of localisation errors occurrence. Therefore, estimating the mobility information is an important security procedure to enhance the verification performance. In this paper, mobility information estimation algorithm MIEA was proposed based on Kalman filter to estimate the actual mobility information from noisy measurements. To evaluate the algorithm effectiveness, the study used next generation simulation dataset NGSIM. White noises were injected into vehicle trajectories to simulate localisation errors. Results show that the root mean square error RMSE is reduced which implies the effectiveness of the proposed algorithm.