Performance analysis of adaptive OFDM modulation scheme in VLC vehicular communication network in realistic noise environment

  Optical wireless communications (OWC) has emerged as a strong candidate for wireless communications, due to the capacity limitation in the radio frequency (RF) spectrum. Especially visible light communication (VLC) has great potential for short-range outdoor vehicular communications, as vehicle LED lights also transmit data. However, outdoor VLC channels vary fast and, experience multipath scattering and reflection resulting in time domain dispersion. Outdoor VLC links are also subjected to high levels of ambient noise, especially from the sun. Orthogonal frequency-division multiplexing (OFDM), which has proven robustness to multi path fading and noise effects in RF links can also be deployed in VLC links. In this paper, optical OFDM (O-OFDM) along with adaptive modulation scheme is investigated in VLC for vehicle to vehicle (V2V) communications. A (2 x 2) multiple input multiple output (MIMO) channel, with multiple polarimetric bidirectional reflections and realistic sunlight interference is considered. Two schemes of O-OFDM; direct current biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are investigated. Simulation results of the proposed model show increase in data rates up to 50 Mbps along with reduced bit error rate (BER) under both line of sight (LOS) and non-LOS and high noise conditions.  </p

A Survey of Machine Learning for Indoor Positioning

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.</p

MARINE: man-in-the-middle attack resistant trust model in connected vehicles

Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and 'F' -score, respectively

Significant predictors associated with two COVID-19 related questions for the combined sample of Australian born and refugee background participants: Adjusted odds ratios (aORs) with 95% confidence interval (CI) from Stepwise Multiple Logistic Regression Analysis (n = 650).

Significant predictors associated with two COVID-19 related questions for the combined sample of Australian born and refugee background participants: Adjusted odds ratios (aORs) with 95% confidence interval (CI) from Stepwise Multiple Logistic Regression Analysis (n = 650).</p

Association of COVID-19 related difficulties with prevalence (%) of Common mental disorders (CMDs) and Relative Risk (RR) with 95% Confidence Interval (95% CI) for Australian born and refugee background participants.

Association of COVID-19 related difficulties with prevalence (%) of Common mental disorders (CMDs) and Relative Risk (RR) with 95% Confidence Interval (95% CI) for Australian born and refugee background participants.</p

Percentage distribution (95% CI) for Australian born and refugee background participants by COVID-19 related measures.

Percentage distribution (95% CI) for Australian born and refugee background participants by COVID-19 related measures.</p

a. Association of socio-demographic characteristics with percentage of Australian Born and Refugee Background participants who reported ‘a problem/a very serious problem’ for ‘Hardship related to COVID-19’.

b. Association of socio-demographic characteristics with percentage of women who reported ‘a problem/a very serious problem’ related to ‘Fear or stress associated with COVID-19’ for Australian Born and Women from Refugee Background.</p

Percentage distribution (95% CI) of Australian born and refugee background participants who completed COVID-19 questionnaires by socio-demographic characteristics.

Percentage distribution (95% CI) of Australian born and refugee background participants who completed COVID-19 questionnaires by socio-demographic characteristics.</p