scholarly journals Big Data and development of smart city: System architecture and practical public safety example

2020 ◽  
Vol 17 (3) ◽  
pp. 337-355
Author(s):  
Mirko Simic ◽  
Miroslav Peric ◽  
Ilija Popadic ◽  
Dragana Peric ◽  
Milos Pavlovic ◽  
...  
Keyword(s):  
Big Data ◽  
Smart City ◽  
Public Safety ◽  
Imaging System ◽  
Distributed Processing ◽  
Smart Device ◽  
Business Environments ◽  
Processing Power ◽  
Private Data ◽  
Thermal Imagers

The concept of Smart City started its development path around two to three decades ago; it has been mainly influenced and driven by radical changes in technological, social and business environments. Big Data, Internet of Things and Networked Cyber-Physical Systems, together with the concepts of Cloud, Fog and Edge Computing, have tremendous impact on the development of Smart City, reforming its frame and tasks and redefining its requirements and challenges. We consider feasible architectures of the IT infrastructure and signal processing, taking into account aspects of Big Data, followed by summary of benefits and main challenges, like security of infrastructure and private data. As a practical example we present a public safety application of multi-sensor imaging system: a smart device with target detection subsystem based on artificial intelligence used for activation of target tracking. The experiments have been performed in the cities of Abu Dhabi and Belgrade, which have very different environment. The experiments have shown the effects of videostreaming compression on thermal imagers and the importance of distributed processing power that optimizes requirements for amount of transmitted data and delay.

scholarly journals Corrigendum on: Big Data and development of smart city: System architecture and practical public safety example [Serb. Jour. of Elect. Eng., Vol. 17, No. 3, Oct. 2020, pp. 337 - 355]

2021 ◽  
Vol 18 (1) ◽  
pp. 137-137
Author(s):  
E Editorial
Keyword(s):  
Big Data ◽  
System Architecture ◽  
Smart City ◽  
Public Safety ◽  
The Third ◽  
City System ◽  
Font Color

The authors of the paper entitled "Big Data and Development of Smart City: System Architecture and Practical Public Safety Example", Mirko Simic, Miroslav Peric, Ilija Popadic, Dragana Peric, Milos Pavlovic, Miljan Vucetic and Milos S. Stankovic, informed the Editor about the error in the position of the author Miljan Vucetic, who should be at the third position. The authors have requested for this error to be corrected. Therefore, the journal is publishing this Corrigendum. The authors of this article should be listed as follows: Mirko Simic, Miroslav Peric, Miljan Vucetic, Ilija Popadic, Dragana Peric, Milos Pavlovic, Milos Stankovic. <br><br><font color="red"><b> Link to the corrected article <u><a href="http://dx.doi.org/10.2298/SJEE2003337S">10.2298/SJEE2003337S</a></b></u>

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals Computational storage: an efficient and scalable platform for big data and HPC applications

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mahdi Torabzadehkashi ◽  
Siavash Rezaei ◽  
Ali HeydariGorji ◽  
Hosein Bobarshad ◽  
Vladimir Alves ◽  
...  
Keyword(s):  
Big Data ◽  
High Performance ◽  
Distributed Processing ◽  
Data Access ◽  
Distributed Applications ◽  
Process Data ◽  
Storage Devices ◽  
Hadoop Mapreduce ◽  
Big Data Applications ◽  
Application Processor

AbstractIn the era of big data applications, the demand for more sophisticated data centers and high-performance data processing mechanisms is increasing drastically. Data are originally stored in storage systems. To process data, application servers need to fetch them from storage devices, which imposes the cost of moving data to the system. This cost has a direct relation with the distance of processing engines from the data. This is the key motivation for the emergence of distributed processing platforms such as Hadoop, which move process closer to data. Computational storage devices (CSDs) push the “move process to data” paradigm to its ultimate boundaries by deploying embedded processing engines inside storage devices to process data. In this paper, we introduce Catalina, an efficient and flexible computational storage platform, that provides a seamless environment to process data in-place. Catalina is the first CSD equipped with a dedicated application processor running a full-fledged operating system that provides filesystem-level data access for the applications. Thus, a vast spectrum of applications can be ported for running on Catalina CSDs. Due to these unique features, to the best of our knowledge, Catalina CSD is the only in-storage processing platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and HPC applications in-place without any modifications on the underlying distributed processing framework. For the proof of concept, we build a fully functional Catalina prototype and a CSD-equipped platform using 16 Catalina CSDs to run Intel HiBench Hadoop and HPC benchmarks to investigate the benefits of deploying Catalina CSDs in the distributed processing environments. The experimental results show up to 2.2× improvement in performance and 4.3× reduction in energy consumption, respectively, for running Hadoop MapReduce benchmarks. Additionally, thanks to the Neon SIMD engines, the performance and energy efficiency of DFT algorithms are improved up to 5.4× and 8.9×, respectively.

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