An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control.

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment

IOT Based Intelligent Bin for Smart Cities

The method of connecting the objects or things through wireless connectivity, Internet called Internet Of Things. Nowadays a variety of tasks are based on IOT. Cities in the world are becoming smarter by implementing the things around using IOT. This is a new trend in technology. Smart cities include obstacle tracking, object sensing, traffic control, tracking of our activities, examining the baby, monitoring home lights and so on. One of the objective of smart cities is keeping the environment clean and neat. This aim is not fulfilled without the garbage bin management system. Hence the paper “IOT Based Intelligent Bin for Smart Cities” has been developed. Bin management is one of the major applications of IOT. Here sensors are connected to the all the bins at different areas. It senses the level of garbage in bin. When it reaches threshold a message is sent via GSM to the concerned person to clean it as soon as possible. The completed task is done in LabVIEW environment

Adaptive intelligent traffic control systems for improving traffic quality and congestion in smart cities

A systematic review was undertaken to examine the solutions available for traffic congestion and associated problems in smart cities. Google Scholar and Google were used as search engines, leading to the final selection of 35 eligible papers for inclusion in this review, after a serious of screening based on definite criteria. Intelligent transport systems were found to be the most suitable solution to traffic congestion and associated problems in smart cities. Certain models and frameworks of smart cities include smart mobility and transport management systems. These can be approximated to intelligent transport systems. True intelligent transport systems are infrastructure-based or intelligent vehicle based or more preferably, a combination of both. The Internet of Things and cloud computing should be built into the system as they enable the operation of smart transport networks. Some methods of designing traffic control systems combining both Eulerian and Lagrangian approaches have been discussed for the possibility of using any of them to design a new automatic traffic monitoring and control system for smart cities. The practical implication of this research is that it can improve quality of life of people by minimizing traffic congestion. Limitations of this paper include this being a systematic review, availability of very few papers and not considering adaptive intelligent traffic control systems. Explanations for these limitations have been provide

Smart IoT Network Based Convolutional Recurrent Neural Network with Element-Wise Prediction System

© Copyright 2021, The Author(s). An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network

Traffic Road Congestion System using by the internet of vehicles (IoV)

Traffic problems have increased in modern life due to a huge number of vehicles, big cities, and ignoring the traffic rules. Vehicular ad hoc network (VANET) has improved the traffic system in previous some and plays a vital role in the best traffic control system in big cities. But due to some limitations, it is not enough to control some problems in specific conditions. Now a day invention of new technologies of the Internet of Things (IoT) is used for collaboratively and efficiently performing tasks. This technology was also introduced in the transportation system which makes it an intelligent transportation system (ITS), this is called the Internet of vehicles (IOV). We will elaborate on traffic problems in the traditional system and elaborate on the benefits, enhancements, and reasons to better IOV by Systematic Literature Review (SLR). This technique will be implemented by targeting needed papers through many search phrases. A systematic literature review is used for 121 articles between 2014 and 2023. The IoV technologies and tools are required to create the IoV and resolve some traffic rules through SUMO (simulation of urban mobility) which is used for the design and simulation the road traffic. We have tried to contribute to the best model of the traffic control system. This paper will analysis two vehicular congestion control models in term of select the optimized and efficient model and elaborate on the reasons for efficiency by searching the solution SLR based questions. Due to some efficient features, we have suggested the IOV based on vehicular clouds. These efficient features make this model the best and most effective than the traditional model which is a great reason to enhance the network system.Comment: pages 16, figures

Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments

[EN] Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.This paper was supported in part by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional within the project Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software under Grant TIN2014-57991-C3-1-P, in part by the Ministerio de Educacion, Cultura y Deporte, through the Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2015) under Grant FPU15/06837, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento within the Project TIN2017-84802-C2-1-P. (Corresponding author: Jaime Lloret.)Egea, S.; Rego Mañez, A.; Carro, B.; Sánchez-Esguevillas, A.; Lloret, J. (2018). Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast Based-Correlation Feature Selection in Industrial Environments. IEEE Internet of Things. 5(3):1616-1624. https://doi.org/10.1109/JIOT.2017.2787959S161616245

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.EC/H2020/825496/EU/5G for cooperative & connected automated MOBIility on X-border corridors/5G-MOBI

Optimization procedure for intelligent Internet of Things applications

Internet of Things (IoT) is basically the concept and terminology of vehicular communications for Vehicles are becoming increasingly available in the literature. Applications for these are efficient traffic management, driving safety, and information services, which offer new service functionalities to extend efficiency for the network. In IoV, the vehicles carry advanced components such as actuators, sensors, and controllers to provide vehicle control functions and intelligent decision-making. The connectivity among vehicles is through intercommunication between devices, intelligent systems in the environment, and sensors [3]. There are three elements for a network model of IoV: client, connection, and cloud. The optimization technique finds the optimal result for sophisticated challenges by diminishing or exploiting objective functions that stand on one or more decision parameters that achieve the objective function value. This paper proposes a GA-based energy optimization procedure and assesses the performance over existing optimization techniques for intelligent IoT applications