CMD: A Multi-Channel Coordination Scheme for Emergency Message Dissemination in IEEE 1609.4

In the IEEE 1609.4 legacy standard for multi-channel communications in vehicular ad hoc networks(VANETs), the control channel (CCH) is dedicated to broadcast safety messages while the service channels (SCH's) are dedicated to transmit infotainment service content. However, the SCH can be used as an alternative to transmit high priority safety messages in the event that they are invoked during the service channel interval (SCHI). This implies that there is a need to transmit safety messages across multiple available utilized channels to ensure that all vehicles receive the safety message. Transmission across multiple SCH's using the legacy IEEE 1609.4 requires multiple channel switching and therefore introduces further end-to-end delays. Given that safety messaging is a life critical application, it is important that optimal end-to-end delay performance is derived in multi-channel VANET scenarios to ensure reliable safety message dissemination. To tackle this challenge, three primary contributions are in this article: first, a channel coordinator selection approach based on the least average separation distance (LAD) to the vehicles that expect to tune to other SCH's and operates during the control channel interval (CCHI) is proposed. Second, a model to determine the optimal time intervals in which CMD operates during the CCHI is proposed. Third, a contention back-off mechanism for safety message transmission during the SCHI is proposed. Computer simulations and mathematical analysis show that CMD performs better than the legacy IEEE 1609.4 and a selected state-of-the-art multi-channel message dissemination schemes in terms of end-to-end delay and packet reception ratio.Comment: 15 pages, 10 figures, 7 table

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Hand-eye calibration enables proper perception of the environment in which a vision guided robot operates. Additionally, it enables the mapping of the scene in the robots frame. Proper hand-eye calibration is crucial when sub-millimetre perceptual accuracy is needed. For example, in robot assisted surgery, a poorly calibrated robot would cause damage to surrounding vital tissues and organs, endangering the life of a patient. A lot of research has gone into ways of accurately calibrating the hand-eye system of a robot with different levels of success, challenges, resource requirements and complexities. As such, academics and industrial practitioners are faced with the challenge of choosing which algorithm meets the implementation requirements based on the identified constraints. This review aims to give a general overview of the strengths and weaknesses of different hand-eye calibration algorithms available to academics and industrial practitioners to make an informed design decision, as well as incite possible areas of research based on the identified challenges. We also discuss different calibration targets which is an important part of the calibration process that is often overlooked in the design process

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Wearable inertial measurement unit (IMU) sensors are powerful enablers for acquisition of motion data. Specifically, in human activity recognition (HAR), IMU sensor data collected from human motion are categorically combined to formulate datasets that can be used for learning human activities. However, successful learning of human activities from motion data involves the design and use of proper feature representations of IMU sensor data and suitable classifiers. Furthermore, the scarcity of labelled data is an impeding factor in the process of understanding the performance capabilities of data-driven learning models. To tackle these challenges, two primary contributions are in this article: first; by using raw IMU sensor data, a spectrogram-based feature extraction approach is proposed. Second, an ensemble of data augmentations in feature space is proposed to take care of the data scarcity problem. Performance tests were conducted on a deep long term short term memory (LSTM) neural network architecture to explore the influence of feature representations and the augmentations on activity recognition accuracy. The proposed feature extraction approach combined with the data augmentation ensemble produces state-of-the-art accuracy results in HAR. A performance evaluation of each augmentation approach is performed to show the influence on classification accuracy. Finally, in addition to using our own dataset, the proposed data augmentation technique is evaluated against the University of California, Irvine (UCI) public online HAR dataset and yields state-of-the-art accuracy results at various learning rates

CMD: A Multichannel Coordination Scheme for Emergency Message Dissemination in IEEE 1609.4

The IEEE 1609.4 legacy standard for multichannel communications in vehicular ad hoc networks (VANETs), specifies that the control channel (CCH) is dedicated to broadcast safety messages, while the service channels (SCHs) are dedicated to transmit infotainment service content. However, the SCHs can be used as an alternative to transmit high priority safety messages in the event that they are invoked during the service channel interval (SCHI). This implies that there is a need to transmit safety messages across multiple available utilized channels to ensure that all vehicles receive the safety message. Transmission across multiple SCHs using the legacy IEEE 1609.4 requires multiple channel switching and therefore introduces further end-to-end delays. Given that safety messaging is a life critical application, it is important that optimal end-to-end delay performance is derived in multichannel VANET scenarios to ensure reliable safety message dissemination. To tackle this challenge, three primary contributions are in this article: first, a cooperative multichannel coordinator (CMD) selection approach based on the least average separation distance (LAD) to the vehicles that expect to tune to other SCHs and operates during the control channel interval (CCHI) is proposed. Second, a model to determine the optimal time intervals in which CMD operates during the CCHI is proposed. Third, a contention back-off mechanism for safety message transmission during the SCHI is proposed. Computer simulations and mathematical analysis show that CMD performs better than the legacy IEEE 1609.4 and a selected state-of-the-art multichannel message dissemination scheme in terms of end-to-end delay and packet reception ratio

An Indoor Position-Estimation Algorithm Using Smartphone IMU Sensor Data

Position-estimation systems for indoor localization play an important role in everyday life. The global positioning system (GPS) is a popular positioning system, which is mainly efficient for outdoor environments. In indoor scenarios, GPS signal reception is weak. Therefore, achieving good position estimation accuracy is a challenge. To overcome this challenge, it is necessary to utilize other position-estimation systems for indoor localization. However, other existing indoor localization systems, especially based on inertial measurement unit (IMU) sensor data, still face challenges such as accumulated errors from sensors and external magnetic field effects. This paper proposes a position-estimation algorithm that uses the combined features of the accelerometer, magnetometer, and gyroscope data from an IMU sensor for position estimation. In this paper, we first estimate the pitch and roll values based on a fusion of accelerometer and gyroscope sensor values. The estimated pitch values are used for step detection. The step lengths are estimated by using the pitching amplitude. The heading of the pedestrian is estimated by the fusion of magnetometer and gyroscope sensor values. Finally, the position is estimated based on the step length and heading information. The proposed pitch-based step detection algorithm achieves 2.5% error as compared with acceleration-based step detection approaches. The heading estimation proposed in this paper achieves a mean heading error of 4.72° as compared with the azimuth- and magnetometer-based approaches. The experimental results show that the proposed position-estimation algorithm achieves a high position accuracy that significantly outperforms that of conventional estimation methods used for validation in this paper

A Link Fabrication Attack Mitigation Approach (LiFAMA) for Software Defined Networks

In software defined networks (SDNs), the controller is a critical resource, yet it is a potential target for attacks as well. The conventional OpenFlow Discovery Protocol (OFPD) used in building the topological view for the controller has vulnerabilities that easily allow attackers to poison the network topology by creating fabricated links with malicious effects. OFDP makes use of the link layer discovery protocol (LLDP) to discover existing links. However, the LLDP is not efficient at fabricated link detection. Existing approaches to mitigating this problem have mostly been passive approaches that depend on observing unexpected behaviour. Examples of such behaviour include link latency and packet patterns to trigger attack alerts. The problem with the existing solutions is that their implementations cause longer link discovery time. This implies that a dense SDN would suffer from huge delays in the link discovery process. In this study, we propose a link fabrication attack (LFA) mitigation approach (LiFAMA), which is an active mitigation approach and one that minimises the link discovery time. The approach uses LLDP packet authentication together with keyed-hash-based message authentication code (HMAC) and a link verification database (PostgreSQL) that stores records of all known and verified links in the network. This approach was implemented in an emulated SDN environment using Mininet and a Python-based open-source OpenFlow (POX) controller. The results show that the approach detects fabricated links in an SDN in real time and helps mitigate them. Additionally, the link discovery time of LiFAMA out-competes that of an existing LFA mitigation approach

A Link Fabrication Attack Mitigation Approach (LiFAMA) for Software Defined Networks

In software defined networks (SDNs), the controller is a critical resource, yet it is a potential target for attacks as well. The conventional OpenFlow Discovery Protocol (OFPD) used in building the topological view for the controller has vulnerabilities that easily allow attackers to poison the network topology by creating fabricated links with malicious effects. OFDP makes use of the link layer discovery protocol (LLDP) to discover existing links. However, the LLDP is not efficient at fabricated link detection. Existing approaches to mitigating this problem have mostly been passive approaches that depend on observing unexpected behaviour. Examples of such behaviour include link latency and packet patterns to trigger attack alerts. The problem with the existing solutions is that their implementations cause longer link discovery time. This implies that a dense SDN would suffer from huge delays in the link discovery process. In this study, we propose a link fabrication attack (LFA) mitigation approach (LiFAMA), which is an active mitigation approach and one that minimises the link discovery time. The approach uses LLDP packet authentication together with keyed-hash-based message authentication code (HMAC) and a link verification database (PostgreSQL) that stores records of all known and verified links in the network. This approach was implemented in an emulated SDN environment using Mininet and a Python-based open-source OpenFlow (POX) controller. The results show that the approach detects fabricated links in an SDN in real time and helps mitigate them. Additionally, the link discovery time of LiFAMA out-competes that of an existing LFA mitigation approach

Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data

Localization is one of the current challenges in indoor navigation research. The conventional global positioning system (GPS) is affected by weak signal strengths due to high levels of signal interference and fading in indoor environments. Therefore, new positioning solutions tailored for indoor environments need to be developed. In this paper, we propose a deep learning approach for indoor localization. However, the performance of a deep learning system depends on the quality of the feature representation. This paper introduces two novel feature set extractions based on the continuous wavelet transforms (CWT) of the received signal strength indicators’ (RSSI) data. The two novel CWT feature sets were augmented with additive white Gaussian noise. The first feature set is CWT image-based, and the second is composed of the CWT PSD numerical data that were dimensionally equalized using principal component analysis (PCA). These proposed image and numerical data feature sets were both evaluated using CNN and ANN models with the goal of identifying the room that the human subject was in and estimating the precise location of the human subject in that particular room. Extensive experiments were conducted to generate the proposed augmented CWT feature set and numerical CWT PSD feature set using two analyzing functions, namely, Morlet and Morse. For validation purposes, the performance of the two proposed feature sets were compared with each other and other existing feature set formulations. The accuracy, precision and recall results show that the proposed feature sets performed better than the conventional feature sets used to validate the study. Similarly, the mean localization error generated by the proposed feature set predictions was less than those of the conventional feature sets used in indoor localization. More particularly, the proposed augmented CWT-image feature set outperformed the augmented CWT-PSD numerical feature set. The results also show that the Morse-based feature sets trained with CNN produced the best indoor positioning results compared to all Morlet and ANN-based feature set formulations