The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.

The advent of microsimulation in the transportation sector has created the need for extensive disaggregated data concerning the population whose behavior is modeled. Because of the cost of collecting this data and the existing privacy regulations, this need is often met by the creation of a synthetic population on the basis of aggregate data. Although several techniques for generating such a population are known, they suffer from a number of limitations. The first is the need for a sample of the population for which fully disaggregated data must be collected, although such samples may not exist or may not be financially feasible. The second limiting assumption is that the aggregate data used must be consistent, a situation that is most unusual because these data often come from different sources and are collected, possibly at different moments, using different protocols. The paper presents a new synthetic population generator in the class of the Synthetic Reconstruction methods, whose objective is to obviate these limitations. It proceeds in three main successive steps: generation of individuals, generation of household type's joint distributions, and generation of households by gathering individuals. The main idea in these generation steps is to use data at the most disaggregated level possible to define joint distributions, from which individuals and households are randomly drawn. The method also makes explicit use of both continuous and discrete optimization and uses the χ² metric to estimate distances between estimated and generated distributions. The new generator is applied for constructing a synthetic population of approximately 10,000,000 individuals and 4,350,000 households localized in the 589 municipalities of Belgium. The statistical quality of the generated population is discussed using criteria extracted from the literature, and it is shown that the new population generator produces excellent results.

Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities.

Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.

Mipfp: An R Package for Multidimensional Array Fitting and Simulating Multivariate Bernoulli Distributions