Social Networks as Real-time Data Distribution Platforms for Smart Cities

Refining and petrochemical processing facilities utilize various process control applications to raise productivity and enhance plant operation. Client–server communication model is used for integrating these highly interacting applications across multiple network layers utilized in distributed control systems. This paper presents an optimum process control environment by merging sequential and regulatory control, advanced regulatory control, multivariable control, unit-based process control, and plant-wide advanced process control into a single collaborative automation platform to ensure optimum operation of processing equipment for achieving maximum yield of all manufacturing facilities. The main control module is replaced by a standard real-time server. The input/output racks are physically and logically decoupled from the controller by converting them into distributed autonomous process interface systems. Real-time data distribution service middleware is used for providing seamless cross-vendor interoperable communication among all process control applications and distributed autonomous process interface systems. Detailed performance analysis was conducted to evaluate the average communication latency and aggregate messaging capacity among process control applications and distributed autonomous process interface systems. The overall performance results confirm the viability of the new proposal as the basis for designing an optimal collaborative automation platform to handle all process control applications. It also does not impose any inherent limit on the aggregate data messaging capacity, making it suitable for scalable automation platforms.

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Mining Iot Data for Next Generation Smart Cities

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e7537.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 259-262

Keyword(s):

Real Time ◽

Crucial Role ◽

Data Analytics ◽

Data Science ◽

Smart Cities ◽

Classification Algorithms ◽

Time Data ◽

New Era ◽

Real Time Data ◽

Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

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Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽

10.3390/s18092994 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2994 ◽

Cited By ~ 28

Author(s):

Bhagya Silva ◽

Murad Khan ◽

Changsu Jung ◽

Jihun Seo ◽

Diyan Muhammad ◽

...

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Smart City ◽

Data Analytics ◽

Smart Cities ◽

Big Data Analytics ◽

Time Data ◽

Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

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Novel Design of Heterogeneous Automation Controller Based on Real-Time Data Distribution Service Middleware to Avoid Obsolescence Challenges

Journal of Circuits System and Computers ◽

10.1142/s0218126616501115 ◽

2016 ◽

Vol 25 (09) ◽

pp. 1650111 ◽

Cited By ~ 1

Author(s):

Sadiq M. Sait ◽

Ghalib A. Al-Hashim

Keyword(s):

Real Time ◽

Data Distribution ◽

Partial Replacement ◽

Limiting Factor ◽

Input Output ◽

Process Automation ◽

Time Data ◽

Automation Systems ◽

Real Time Data ◽

Data Distribution Service

Oil and gas processing facilities utilize various process automation systems with proprietary controllers. As the systems age; older technologies become obsolete resulting in frequent premature capital investments to sustain their operation. This paper presents a new design of automation controller to provide inherent mechanisms for upgrades and/or partial replacement of any obsolete components without obligation for a complete system replacement throughout the expected life cycle of the processing facilities. The input/output racks are physically and logically decoupled from the controller by converting them into distributed autonomous process interface systems. The proprietary input/output communication between the conventional controller CPU and the associated input/output racks is replaced with standard real-time data distribution service middleware for providing seamless cross-vendor interoperable communication between the controller and the distributed autonomous process interface systems. The objective of this change is to allow flexibility of supply for all controller’s subcomponents from multiple vendors to safeguard against premature automation obsolescence challenges. Detailed performance analysis was conducted to evaluate the viability of using the standard real-time data distribution service middleware technology in the design of automation controller to replace the proprietary input/output communication. The key simulation measurements to demonstrate its performance sustainability while growing in controller’s size based on the number of input/output signals are communication latency, variation in packets delays, and communication throughput. The overall performance results confirm the viability of the new proposal as the basis for designing cost effective evergreen process automation solutions that would result in optimum total cost of ownership capital investment throughout the systems’ life span. The only limiting factor is the selected network infrastructure.

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Simulating Reliable Real-Time Data Distribution for UAV-aided Tactical Networks

2011 International Conference on Complex, Intelligent, and Software Intensive Systems ◽

10.1109/cisis.2011.110 ◽

2011 ◽

Cited By ~ 1

Author(s):

Jongryool Kim ◽

JongWon Kim ◽

KeyongTae Kim ◽

Soo-Hyung Lee ◽

Won-Tae Kim ◽

...

Keyword(s):

Real Time ◽

Data Distribution ◽

Time Data ◽

Real Time Data ◽

Tactical Networks

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Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Electronics ◽

10.3390/electronics9071101 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1101 ◽

Cited By ~ 1

Author(s):

Iván García-Magariño ◽

Moustafa M. Nasralla ◽

Shah Nazir

Keyword(s):

Data Management ◽

Real Time ◽

Business Intelligence ◽

Smart Cities ◽

Online Information ◽

Smart Meter ◽

Time Data ◽

Real Time Analysis ◽

Real Time Data ◽

Historic Data

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).

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