As demand increases for ubiquitous network facilities, infrastructure-less and self-configuring systems like Mobile Ad hoc Networks (MANET) are gaining popularity. MANET routing security however, is one of the most significant challenges to wide scale adoption, with wormhole attacks being an especially severe MANET routing threat. This is because wormholes are able to disrupt a major component of network traffic, while concomitantly being extremely difficult to detect. This paper introduces a new wormhole detection paradigm based upon Traversal Time and Hop Count Analysis (TTHCA), which in comparison to existing algorithms, consistently affords superior detection performance, allied with low false positive rates for all wormhole variants. Simulation results confirm that the TTHCA model exhibits robust wormhole route detection in various network scenarios, while incurring only a small network overhead. This feature makes TTHCA an attractive choice for MANET environments which generally comprise devices, such as wireless sensors, which possess a limited processing capability.
Background: Several factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients’ exercises and reduce clients’ need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge. Objectives: The main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided. Methods: This paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation. Results: Current pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations. Conclusions: Even though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.
This paper presents a contemporary review of communication architectures and topographies for MANETconnected Internet-of-Things (IoT) systems. Routing protocols for multi-hop MANETs are analyzed with a focus on the standardized Routing Protocol for Low-power and Lossy Networks. Various security threats and vulnerabilities in current MANET routing are described and security enhanced routing protocols and trust models presented as methodologies for supporting secure routing. Finally, the paper identifies some key research challenges in the emerging domain of MANET-IoT connectivity.
Extending state-of-the-art machine learning algorithms to highly scalable (big data) analysis environments is crucial for the handling of authentic datasets in Intrusion Detection Systems (IDS). Traditional supervised learning methods are considered to be too slow for use in these environments. Therefore, we propose the use of Extreme Learning Machine (ELM) for detecting network intrusion attempts. We show they hold great promise for the field by employing a MapReduce based variant evaluated on the open source tool Hadoop.
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