Automated Discovery of Network Cameras in Heterogeneous Web Pages

Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks. Network cameras offer real-time visual data that can be used for studying traffic patterns, emergency response, security, and other applications. Although many sources of Network Camera data are available, collecting the data remains difficult due to variations in programming interface and website structures. Previous solutions rely on manually parsing the target website, taking many hours to complete. We create a general and automated solution for aggregating Network Camera data spread across thousands of uniquely structured web pages. We analyze heterogeneous web page structures and identify common characteristics among 73 sample Network Camera websites (each website has multiple web pages). These characteristics are then used to build an automated camera discovery module that crawls and aggregates Network Camera data. Our system successfully extracts 57,364 Network Cameras from 237,257 unique web pages.

Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.

Self-Driving Robotic Cars

Even the behavior of a single driver can have a dramatic impact on hundreds of cars, making it more difficult to manage traffic. While the attempts to analyze and correct the traffic patterns that lead to congestion began as early in the 1930s, it wasn't until recently that scientists developed simulation techniques and advanced algorithms to create more realistic visualizations of traffic flow. In experiments conducted by Alexandre Bayen and the Liao-Cho, which included several dozen cars in a small-scale closed circuit, a single autonomous vehicle could eliminate traffic jams by moderating the speed of every car on the road. In larger simulations, the research showed that once their number rises to 5-10% of all cars in the traffic, they can manage localized traffic even in complex environments, such as merging multiple lanes of traffic into two or navigating extremely busy sections.

A System for Measurement and Analysis of Aircraft Noise Impacts

The Metroplex Overflight Noise Analysis (MONA) project seeks to measure, analyze, and archive the ground noise generated by aircraft overflights and to provide accurate and actionable data for a variety of different purposes. On the one hand, experimental datasets collected and processed by the MONA system can serve as an openly-available database for validation and verification (V&V) of improved noise prediction methods. On the other, study conclusions derived from both the experimental and computational data can serve to inform technical discussions and options involving aircraft noise, aircraft routes, and the potential impacts of the FAA’s NextGen procedure changes on overflown communities at varying distances from the airport. Given the complex interdependencies between the noise levels perceived on the ground and the air-traffic patterns that generate the aircraft noise, a secondary goal of the MONA project is to share, through compelling visualizations, key results with broad communities of stakeholders to help generate a common understanding and reach better decisions more quickly. In this paper, we focus on the description of the MONA system architecture, its design, and its current set of capabilities. Subsequent publications will focus on the results we are obtaining though the use of the MONA system.

SYNTHESIS OF THE FLEET MANAGEMENT SYSTEM SMALL SHIPPING COMPANY

Within the framework of the methodological support being developed for the planning and management of the fleet of a small shipping company, the task of synthesizing a management system has been solved. The structure and parameters of the system are determined based on the specified requirements for the conditions of its operation, as well as ways to ensure the goals of the functioning of the fleet management system of a small shipping company. The concept of a small, medium or large shipping company is defined by the number of vessels under operational management. It is noted that due to the variability of the economic situation in the transportation market in modern market conditions, permanent "rigid" connections between the elements of the transport system are not formed and the problem of coordinating the work of these elements exists only at the level of operational planning and regulation for small shipping companies. Therefore, it is necessary and sufficient to limit the system under study to the framework of flight planning. The fleet management system at this level in trump shipping traditionally consists of two consecutive subprocesses: the formation of vessel traffic patterns and the assignment of specific vessels to these schemes. The main result of the study is that a new management system structure was formed on the basis of a system analysis of processes in the conditions of working with a small fleet and identified addition-al tasks and requirements for the system. Distinctive features of the new structure from the prototype: the process of arranging transport vessels has been replaced by the process of determining the optimal composition of the leased fleet; two auxiliary subprocesses have been added to manage additional operations if necessary to unload cargo on an unequipped shore.

Comparative Analysis and Statistical Optimization of Fuel Economy for Sustainable Vehicle Routings

In this 21st century, there has been an increase in the usage of renewable products for the economic drifting of vehicle transportations systems. Furthermore, due to recent trends in climate change, researchers have started focusing on statistical optimization techniques for sustainable vehicle routings. However, until now, a major gap has been noticed in the multidomain statistical analysis for optimizing the parametric levels of the vehicle fuel economy. Therefore, in this research work, two widely utilized cars (Toyota and GMC Yukon) are considered on a particular route of Jeddah for the collection of the fuel economy data under the realistic conditions of air conditioner temperature, traffic patterns, and tire pressure. The outcomes of the factorial design of the experiment highlight that the fuel economy is optimal under the low air conditioner temperature, light traffic patterns, and 34 PSI tire pressure. Three replications of the fuel economy have been considered, and the statistical significance of the correlated variables has been justified by implementing the analysis of variance (ANOVA) approach on the various levels of fuel economy. During the analysis, the statistical hypothesis for random exogenous factors has been developed by incorporating a multivariate regression model. The outcomes highlight that both air conditioner temperature and traffic patterns in Jeddah have a significant negative effect on fuel economy. Results also depict that the effect of air conditioner temperature, traffic patterns, and tire pressure is substantially higher for heavy-engine automobiles such as the GMC Yukon compared to light-engine cars (Toyota Corolla). Furthermore, a normality test has also been considered to validate the outcomes of the proposed model. Therefore, it is highly recommended to utilize the proposed methodology in optimizing the trends of fuel economy for sustainable vehicle routings. Based on the findings of multidomain statistical analysis, it is also highly recommended the utilization of the Toyota Corolla car model for investigating the correlation of external undeniable factors (braking frequency, metrological conditions, etc.) with the trends of vehicle fuel economy.

A Survey of 802.15.4 TSCH Schedulers for a Standardized Industrial Internet of Things

Concepts such as Industry 4.0 and Cyber-Physical Systems may bring forward a new industrial revolution. These concepts require extensive connectivity far beyond what is provided by traditional industrial networks. The Industrial Internet of Things (IIoT) bridges this gap by employing wireless connectivity and IP networking. In order for wireless networks to meet the strict requirements of the industrial domain, the Time Slotted Channel Hopping (TSCH) MAC is often employed. The properties of a TSCH network are defined by the schedule, which dictates transmission opportunities for all nodes. We survey the literature for these schedulers, describe and organize them according to their operation: Centralized, Collaborative, Autonomous, Hybrid, and Static. For each category and the field as a whole, we provide a holistic view and describe historical trends, highlight key developments, and identify trends, such as the attention towards autonomous mechanisms. Each of the 76 schedulers is analyzed into their common components to allow for comparison between schedulers and a deeper understanding of functionality and key properties. This reveals trends such as increasing complexity and the utilization of centralized principles in several collaborative schedulers. Further, each scheduler is evaluated qualitatively to identify its objectives. Altogether this allows us to point out challenges in existing work and identify areas for future research, including fault tolerance, scalability, non-convergecast traffic patterns, and hybrid scheduling strategies.

Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach

The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.

Cerberus

The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency ''tax''. To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: e.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional.

On detecting and identifying faulty internet of things devices and outages

As internet of things (IoT) devices play an integral role in our everyday life, it is critical to monitor the health of the IoT devices. However, fault detection in IoT is much more challenging compared with that in traditional wired networks. Traditional observing and polling are not appropriate for detecting faults in resource-constrained IoT devices. Because of the dynamic feature of IoT devices, these detection methods are inadequate for IoT fault detection. In this paper, we propose two methods that can monitor the health status of IoT devices through monitoring the network traffic of these devices. Based on the collected traffic or flow entropy, these methods can determine the health status of IoT devices by comparing captured traffic behavior with normal traffic patterns. Our measurements show that the two methods can effectively detect and identify malfunctioned or defective IoT devices.