Abstract
A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.
Highlights
We are entering an era where city issues are problems faced by the entire world
What is noticeable is the difficulty in combining the advances in many domains (IoT, big data, cloud computing, machine learning), in order to provide the services needed in a smart city at reasonable parameters [15]
It can be suitable for implementing iterative algorithms, machine learning algorithms like graph processing, page ranking, logistic regression, Artificial Neural Networks (ANNs) or Bayesian Network Classifiers (BNCs), as tested in [68]
Summary
We are entering an era where city issues are problems faced by the entire world. The United. A smart city is a city that uses ICT infrastructure in a flexible, reliable, scalable, affordable, secure and safe way, in order to improve the quality of life of its citizens It can provide stable economic growth through higher standards of living and job opportunities, welfare, and access to better education [2,3]. The main disadvantage is its limited support and the notorious security problems of most NoSQL databases and Hadoop ecosystem products Solutions to offset these problems exist, e.g., client to node encryption, the use of external products like Kerberos, etc. What is noticeable is the difficulty in combining the advances in many domains (IoT, big data, cloud computing, machine learning), in order to provide the services needed in a smart city at reasonable parameters [15]. The final section presents the results obtained when testing the proposed architecture on several data sets, and some discussions, taking into account the processing speed, code reusability, scalability and fault tolerance
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