Location-Based Lattice Mobility Model for Wireless Sensor Networks

Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node’s moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM.

Application of head flexion detection for enhancing eye gaze direction classification

Extensive research has been conducted on the tracking and detection of the eye gaze and head movement detection as these aspects of technology can be applied as alternative approaches for various interfacing devices. This paper proposes enhancements to the classification of the eye gaze direction. Viola Jones face detector is applied to first declare the region of the eye. Circular Hough Transform is then used to detect the iris location. Support Vector Machine (SVM) is applied to classify the eye gaze direction. Accuracy of the system is enhanced by calculating the flexion angle of the head through the utilization of a microcontroller and flex sensors. In case of rotated face images, the face can be rotated back to zero degrees through the flexion angle calculation. This is while Viola Jones face detector is limited to face images with very little or no rotation angle. Accuracy is initiated by enhancing the effectiveness of the system in the overall procedure of classifying the direction of the eye gaze. Therefore, the head direction is a main determinant in enhancing the control method. Different control signals are enhanced by the eye gaze direction classification and the head direction detection.

Eye Tracking and Head Movement Detection: A State-of-Art Survey

Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated.