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Distributed Architecture Proposal for Efficient Energy Management of Road Lighting in Urban Environments
Abstract
The energy management in urban and interurban lighting is currently, mainly, based on a centralised or clustered model. The control is mainly based on the level of brightness needed to circulate, without taking into account the presence or not of pedestrians or vehicles. This thesis proposes to review the solutions implemented and to use the Industry 4.0 paradigm as a basis for the design of a highly distributed architecture that efficiently controls the lighting of the roads of urban environments, and is extensible to interurban environments. As results it is expected to be able to verify the hypothesis of, how the distribution of the intelligence at the level of control node, together with the communication between nearby control nodes, allows to optimise the consumption in front of the current solutions.
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Traffic and transportation are main contributors to the global CO2 emissions and resulting climate change. Especially in urban areas, traffic flow is not optimal and thus offers possibilities to reduce emissions. The concept of a Green Wave, i.e., the coordinated switching of traffic lights in order to favor a single direction and reduce congestion, is often discussed as a simple mechanism to avoid breaking and accelerating, thereby reducing fuel consumption. On the other hand, making car use more attractive might also increase emissions. In this study, we use an agent-based model to investigate the benefit of a Green Wave in order to find out whether it can outweigh the effects of increased car use. We find that although the Green Wave has the potential to reduce emissions, there is also a high risk of heaving a net increase in emissions, depending on the specifics of the traffic system.
This paper presents a road lighting control system that uses a light-dependent resistor sensor cooperating with an Internet protocol camera to the lower energy consumption during unnecessary use of a lighting system. A microcontroller was used as a control circuit to automatically control the brightness of a light-emitting diode (LED) luminaire, increasing or decreasing the brightness depending on traffic density. The proposed lighting control system was integrated into a nanogrid solar road lighting system and analysed through an experimental setup. Furthermore, nanogrid solar road lighting systems in LED solar stand-alone and grid-connected operations, with and without the proposed lighting control, were investigated and compared with a conventional existing road lighting system in terms of economic feasibility, based on the following indicators: discounted payback period, net present value, internal rate of return, and profitability index. The results indicate that the use of the Internet protocol camera with the LED sensor can automatically control the on/off state or illuminance levels of the LED luminaire, thereby lowering the energy consumption of the road lighting system when lighting is not required. The economic assessment results indicate that the nanogrid solar road lighting system in LED solar stand-alone and grid-connected road lighting modes exhibit feasibility for investment; the latter provides more economic feasibility. However, when the proposed lighting control is included, the nanogrid solar road lighting system in both modes have lower initial investment costs and save more energy. Consequently, the economic results are improved. The use of the proposed lighting control is thus economically feasible for road lighting systems.
The Vehicle Ad hoc networks (VANETs), which provide protection and warning for drivers and passengers, are playing a major role in wireless and connectivity technology and are becoming one of the major research areas in intelligent transport systems (ITS) technology. The features, problems, and deployment of VANETs are very distinct from those of mobile ad hoc networks (MANETs). The early detection of incidents provides information about traffic incidents classified by the Intelligent Transportation System of Traffic Monitoring Centre. In this review paper, we will discuss the concept of a traffic monitoring center and its techniques. Also, we will explain the concept of early incidents detection and show limitations and strong points for each approach used.
Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on VISSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.
Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.
This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We conducted an experimental evaluation to compare the predictive performance of four popular algorithms. We performed a simulation study based on data from semiconductor fabrication to demonstrate the utility and superiority of the proposed method. The experimental results showed that AVSs with the proposed approach outperformed the existing approach in terms of delivery time, transfer time, and queuing time. We found that adopting machine learning-based traffic control can enhance the performance of existing AVSs and reduce the burden on the human experts who monitor and control AVSs.
In recent years there has been a substantial increase in the number of outdoor lighting installations, the energy management of this has not been greatly improved and electricity consumption has skyrocketed. Most of it does not come from renewable energies with all the negative effects that this entails. With all this, public lighting can represent up to a total of 54% of the energy consumption of a municipality and up to 61% of its electricity consumption. This work focuses on the analysis of the factors to consider in the implementation and application of a lighting control system in a real environment for energy saving. The system should be based on the collection of data by the different sensors installed in the luminaries of the route oriented to the environment of the Smart Cities and the Intelligent Transport Systems (ITS). The main objective is to try to reduce the consumption of electrical energy as much as possible while maintaining the comfort that the road user feels in it. For this, the weak points of these systems will be searched and their elimination will be sought. A study will be made of the situation of the systems available today. The characteristics of these systems will be analysed. Based on the characteristics of the systems analysed, the necessary requirements of the system presented will be determined. The characteristics that will make this project different from the rest will be established. An architecture proposal that seeks to optimise the parameters analysed will be presented.
Originally initiated in Germany, Industry 4.0, the fourth industrial revolution, has attracted much attention in recent literatures. It is closely related with the Internet of Things (IoT), Cyber Physical System (CPS), information and communications technology (ICT), Enterprise Architecture (EA), and Enterprise Integration (EI). Despite of the dynamic nature of the research on Industry 4.0, however, a systematic and extensive review of recent research on it is has been unavailable. Accordingly, this paper conducts a comprehensive review on Industry 4.0 and presents an overview of the content, scope, and findings of Industry 4.0 by examining the existing literatures in all of the databases within the Web of Science. Altogether, 88 papers related to Industry 4.0 are grouped into five research categories and reviewed. In addition, this paper outlines the critical issue of the interoperability of Industry 4.0, and proposes a conceptual framework of interoperability regarding Industry 4.0. Challenges and trends for future research on Industry 4.0 are discussed.
In this paper, we propose the conjunction of the Smart City Initiative and the concept of Industry 4.0. The term smart city has been a phenomenon of the last years, which is very inflected especially since 2008 when the world was hit by the financial crisis. The main reasons for the emergence of the Smart City Initiative are to create a sustainable model for cities and preserve quality of life of their citizens. The topic of the smart city cannot be seen only as a technical discipline, but different economic, humanitarian or legal aspects must be involved as well.
In the concept of Industry 4.0, the Internet of Things (IoT) shall be used for the development of so–called smart products. Sub-components of the product are equipped with their own intelligence. Added intelligence is used both during the manufacturing of a product as well as during subsequent handling, up to continuous monitoring of the product lifecycle (smart processes). Other important aspects of the Industry 4.0 are Internet of Services (IoS), which includes especially intelligent transport and logistics (smart mobility, smart logistics), as well as Internet of Energy (IoE), which determines how the natural resources are used in proper way (electricity, water, oil, etc.). IoT, IoS, IoP and IoE can be considered as an element that can create a connection of the Smart City Initiative and Industry 4.0 – Industry 4.0 can be seen as a part of smart cities.
Interconnection of these systems can be expected to change -transport processes from design logistic processes through to their online optimization with respect to the chosen objective function and the latest information from the transport infrastructure. Linking information from process-based Industry 4.0 with intelligent transport systems of the smart city could create very effective, demand-oriented and higher productivity of manufacturing enterprises as well as sustainable development of society.
