Machine learning driven intelligent and self adaptive system for traffic management in smart cities

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

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ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466841 ◽

2021 ◽

Vol 48 (4) ◽

pp. 41-44

Author(s):

Dena Markudova ◽

Martino Trevisan ◽

Paolo Garza ◽

Michela Meo ◽

Maurizio M. Munafo ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Real Time ◽

Language Processing ◽

Traffic Management ◽

Quality Of Experience ◽

Broadband Internet ◽

Management Policies ◽

Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

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Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽

10.3390/s21113658 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3658

Author(s):

Qingfeng Zhu ◽

Sai Ji ◽

Jian Shen ◽

Yongjun Ren

Keyword(s):

Traffic Control ◽

Traffic Management ◽

Location Privacy ◽

Vehicular Ad Hoc Networks ◽

Intelligent Transportation System ◽

Smart Cities ◽

Security Analysis ◽

Geographical Location ◽

Experimental Simulation ◽

Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

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Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

Sustainability ◽

10.3390/su13094716 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4716

Author(s):

Moustafa M. Nasralla

Keyword(s):

Machine Learning ◽

Time Series ◽

Virtual Reality ◽

Time Series Analysis ◽

Smart City ◽

Smart Cities ◽

Rehabilitation Services ◽

Simulation Scenario ◽

Common Problems ◽

Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

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Machine learning techniques based on security management in smart cities using robots

Work ◽

10.3233/wor-203423 ◽

2021 ◽

pp. 1-12

Author(s):

Zhang Mengqi ◽

Wang Xi ◽

V.E. Sathishkumar ◽

V. Sivakumar

Keyword(s):

Machine Learning ◽

Information And Communication Technologies ◽

Smart City ◽

Smart Cities ◽

Security Management ◽

Machine Learning Techniques ◽

Quality Of Services ◽

Learning Techniques ◽

Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

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Road Traffic Congestion Management Based on a Search-Allocation Approach

Transport and Telecommunication Journal ◽

10.1515/ttj-2017-0003 ◽

2017 ◽

Vol 18 (1) ◽

pp. 25-33 ◽

Cited By ~ 4

Author(s):

Jamal Raiyn

Keyword(s):

Travel Time ◽

Real Time ◽

Traffic Congestion ◽

Traffic Management ◽

Road Traffic ◽

Smart Cities ◽

Self Regulation ◽

Congestion Management ◽

Shortest Route ◽

Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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