scholarly journals Average Response Time (ART):Real-Time Traffic Management in VFC Enabled Smart Cities

Author(s):  
Shabana ◽  
Sallauddin Mohmmad ◽  
Mohammed Ali Shaik ◽  
K Mahender ◽  
Ranganath Kanakam ◽  
...  
2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shunfu Jin ◽  
Wuyi Yue ◽  
Xiaofei Zhang

We consider the sleep mode with multimedia application in WiMAX 2 networks, where the real-time traffic includes the real-time and the best-effort traffic mixed. We present a queueing model with multiple heterogeneous vacations to characterize the system probability behavior in the networks with multimedia application. Taking into account the correlation of the real-time traffic, we assume the arrival process as a discrete-time Markovian arrival process (D-MAP) and analyze this queueing model by using the method of an embedded Markov chain. Then, we present the probability distribution for the number of data packets. Accordingly, we give the formulas for the performance measures in terms of the average response time of data packets, the energy saving ratio, and the standard deviation of the number of data packets. We also develop a cost function to determine the optimal length of the sleep cycle in order to maximize the energy saving ratio while satisfying the Quality of Service (QoS) constraint on the average response time of data packets. Finally, we provide numerical results to investigate the influence of the system parameters on the system performance.


Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 556
Author(s):  
Lucia Lo Bello ◽  
Gaetano Patti ◽  
Giancarlo Vasta

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.


2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn

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.


Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>


2019 ◽  
Vol 01 (03) ◽  
pp. 139-147
Author(s):  
Wang Haoxiang ◽  
Smys S

The developments in the means of transportation along with the communication advancements has made the automotives to step into its next level of innovation by providing a safe, convenient and well-timed transportation. This is made possible by the introduction of the frame work that is particularly designed to establish connectivity between vehicles on road without any previous structure to support with. This paradigm formed particularly in organizing communication between vehicles is the vehicular Adhoc network (VANET) that causes a vehicles to vehicle connection for proper managing of the traffic flow to make the travel more safe and comfortable. The paper proposes a dynamic mapping of real time traffic with the acquisition of digital map by crowd mapping with clustering to offer path optimization to minimize the delay in the responses, for having an efficient traffic managing. The evaluation of the proposed methodology ensures the minimization of the delay in the communication and the improved delivery ratio incurred, when compared with the carry-forward based routings methods that cause more delay resulting in imperfect traffic management.


The industrial revolution 4.0 demands the convenience of a human life facility. Not to forget also in the cleaning service. When we will dispose of trash, we do not need to look for the trash can, it is precisely the trash can that will approach us. This smartphone-based application uses the A * (A star) algorithm as the basis for its work, while for communication between smartphones with the trash can system using blue tooth. The smartphone sends its coordinate position through the Global Positioning System facility, then the trash can system will search for the sender's location. The experimental results show that the average stopping distance indoors without barrier is 7.03 meters with an average time response of 25.3 seconds, the average stopping distance in the room with a barrier of 7.2 meters with the average response time 3.6 seconds average, stopping distance outdoor without a barrier of 5.7 meters with an average response time of 258.3 seconds, and the average outdoor stopping distance with a barrier of 2.73 meters with a response an average time of 141.3 seconds.


Author(s):  
Veljko Aleksić ◽  
Olga Ristić

Determining and understanding the user experience in gamified educational environments is a contemporary challenge, especially when analyzing the flow experience (balance of challenge and skills, conscious actions, clear goals, clear feedback, sense of control, etc.). The reason for this lies in the assessment tools that most often created and implemented to separate the user from the experience of flow and/or cannot be applied en masse.The paper presents the results of a study in which flow experience was modeled based on data logs (e.g. number of mouse actions or average response time) in gamified educational environment on a sample of 31HE students. The results indicate the existence of correlations between data logs and flow experience dimensions.


Author(s):  
Chen Jin ◽  
Saba Sehrish ◽  
Wei-keng Liao ◽  
Alok Choudhary ◽  
Karen Schuchardt

Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.


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