Sensor Data Fusion for Improving Traffic Mobility in Smart Cities

The ever-increasing urban population and vehicular traffic without a corresponding expansion of infrastructure have been a challenge to transportation facilities managers and commuters. While some parts of transportation infrastructure have big data available, so many other locations have sparse data. This has posed a challenge in traffic state estimation and prediction for efficient and effective infrastructure management and route guidance. This research focused on traffic prediction problems and aims to develop novel spatial-temporal and robust algorithms, that can provide high accuracy in the presence of both big data and sparse data in a large urban road network. Intelligent transportation systems require the knowledge of current traffic state and forecast for effective implementation. The actual traffic state has to be estimated as the existing sensors do not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data pose challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle-based approach with Kriging interpolation is proposed. The performance of the particle-based Kriging interpolation for different missing data ratios was investigated for a large road network. A particle-based framework for dealing with missing data is also proposed. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm. In the commonly used Kriging approaches, the covariance function depends only on the separation distance irrespective of the traffic at the considered locations. A key limitation of such an approach is its inability to capture well the traffic dynamics and transitions between different states. This thesis proposes a Bayesian Kriging approach for the prediction of urban traffic. The approach can capture these dynamics and model changes via the covariance matrix. The main novelty consists in representing both stationary and non-stationary changes in traffic flows by a discriminative covariance function conditioned on the observation at each location. An advantage is that by considering the surrounding traffic information distinctively, the proposed method is very likely to represent congested regions and interactions in both upstream and downstream areas

An agent based approach for improvised explosive device detection, public alertness and safety

One of the security challenges faced by our contemporary world is terror threats and attacks, and this is no doubt posing potential threats to lives, properties and businesses all around us; affecting the way we live and also travel. Terror attacks have been perpetrated in diverse ways whether from organized terror networks through coordinated attacks or by some lone individuals such that it is now a major concern to people and government. Indeed, there are numerous forms of terror attacks. In this proposal, we look at how the explosive substance kind of threats can be perceived and taken care of prior to potential attacks using intelligent agent systems requirement analysis. Thus, the paper demonstrates using an agent-oriented system analysis and design methodology to decompose. Through defined percepts, goals and plans, agents possess capabilities to observe and perform actions. This proposal demonstrates: how agents can be situated in our cities, goal refinement for agents in the detection and rescue of potential terror attacks, and inter-agent communication for the prevention of chemical terror attack

Smart Security Implementation for Wireless Sensor Network Nodes

In the territory of concurrent systems such as wireless sensor networks (WSN), the computational nodes being used in wireless sensor networks faces challenges with security applications. Many different security protocols have been proposed that allow some form of security enhancement but not implemented. This article investigates and implements a number of smart security techniques appropriate for WSN nodes with various trade-off such as power consumption and scalability. We provide a brief survey of the major approaches to security prerogative and methods that could reduce if not eliminate algorithmic complexity and denial of service attacks to sensor nodes

Traffic estimation for large urban road network with high missing data ratio

Intelligent transportation systems require the knowledge of current and forecasted traffic states for effective control of road networks. The actual traffic state has to be estimated as the existing sensors does not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data poses challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle based approach with Kriging interpolation is proposed. The performance of the particle based Kriging interpolation for different missing data ratios was investigated for a large road network comprising 1000 segments. Results indicate that the effect of missing data in a large road network can be mitigated by the Kriging interpolation within the particle filter framework

Efficacy and safety of two neutralising monoclonal antibody therapies, sotrovimab and BRII-196 plus BRII-198, for adults hospitalised with COVID-19 (TICO): a randomised controlled trial

We aimed to assess the efficacy and safety of two neutralising monoclonal antibody therapies (sotrovimab [Vir Biotechnology and GlaxoSmithKline] and BRII-196 plus BRII-198 [Brii Biosciences]) for adults admitted to hospital for COVID-19 (hereafter referred to as hospitalised) with COVID-19. In this multinational, double-blind, randomised, placebo-controlled, clinical trial (Therapeutics for Inpatients with COVID-19 [TICO]), adults (aged ≥18 years) hospitalised with COVID-19 at 43 hospitals in the USA, Denmark, Switzerland, and Poland were recruited. Patients were eligible if they had laboratory-confirmed SARS-CoV-2 infection and COVID-19 symptoms for up to 12 days. Using a web-based application, participants were randomly assigned (2:1:2:1), stratified by trial site pharmacy, to sotrovimab 500 mg, matching placebo for sotrovimab, BRII-196 1000 mg plus BRII-198 1000 mg, or matching placebo for BRII-196 plus BRII-198, in addition to standard of care. Each study product was administered as a single dose given intravenously over 60 min. The concurrent placebo groups were pooled for analyses. The primary outcome was time to sustained clinical recovery, defined as discharge from the hospital to home and remaining at home for 14 consecutive days, up to day 90 after randomisation. Interim futility analyses were based on two seven-category ordinal outcome scales on day 5 that measured pulmonary status and extrapulmonary complications of COVID-19. The safety outcome was a composite of death, serious adverse events, incident organ failure, and serious coinfection up to day 90 after randomisation. Efficacy and safety outcomes were assessed in the modified intention-to-treat population, defined as all patients randomly assigned to treatment who started the study infusion. This study is registered with ClinicalTrials.gov, NCT04501978. Between Dec 16, 2020, and March 1, 2021, 546 patients were enrolled and randomly assigned to sotrovimab (n=184), BRII-196 plus BRII-198 (n=183), or placebo (n=179), of whom 536 received part or all of their assigned study drug (sotrovimab n=182, BRII-196 plus BRII-198 n=176, or placebo n=178; median age of 60 years [IQR 50–72], 228 [43%] patients were female and 308 [57%] were male). At this point, enrolment was halted on the basis of the interim futility analysis. At day 5, neither the sotrovimab group nor the BRII-196 plus BRII-198 group had significantly higher odds of more favourable outcomes than the placebo group on either the pulmonary scale (adjusted odds ratio sotrovimab 1·07 [95% CI 0·74–1·56]; BRII-196 plus BRII-198 0·98 [95% CI 0·67–1·43]) or the pulmonary-plus complications scale (sotrovimab 1·08 [0·74–1·58]; BRII-196 plus BRII-198 1·00 [0·68–1·46]). By day 90, sustained clinical recovery was seen in 151 (85%) patients in the placebo group compared with 160 (88%) in the sotrovimab group (adjusted rate ratio 1·12 [95% CI 0·91–1·37]) and 155 (88%) in the BRII-196 plus BRII-198 group (1·08 [0·88–1·32]). The composite safety outcome up to day 90 was met by 48 (27%) patients in the placebo group, 42 (23%) in the sotrovimab group, and 45 (26%) in the BRII-196 plus BRII-198 group. 13 (7%) patients in the placebo group, 14 (8%) in the sotrovimab group, and 15 (9%) in the BRII-196 plus BRII-198 group died up to day 90. Neither sotrovimab nor BRII-196 plus BRII-198 showed efficacy for improving clinical outcomes among adults hospitalised with COVID-19. US National Institutes of Health and Operation Warp Spee