Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Technology capabilities for an automated and connected earthwork roadmap

Purpose: The development of communication and artificial intelligence technologies has raised interest in connectivity and increased autonomy of automated earthmoving equipment for earthwork. These changes are motivating work to reduce uncertainties, in terms of improving equipment object detection capability and reducing strikes and accidents on site. The purpose of this study is to illustrate industrial drivers for automated earthwork systems; identify the specific capabilities which make the transformation happen; and finally determine use cases that create value for the system. These three objectives act as components of a technology roadmap for automated and connected earthwork and can guide development of new products and services. Design/methodology/approach: This paper used a text mining approach in which the required data was captured through a structured literature review, and then expert knowledge was used for verification of the results. Findings: Automated and connected earthwork can enhance construction site and its embraced infrastructure, resilience by avoiding human faults during operations. Automating the monitoring process can lead to reliable anticipation of problems and facilitate real-time responses to unexpected situation via connectedness capabilities. Research findings are presented in three sections: industrial perspectives, trends and drivers for automated and connected earthwork; capabilities which are met by technologies; and use cases to demonstrate different capabilities. Originality/value: This study combines the results of disintegrated and fragmented research in the area of automated and connected earthwork and categorises them under new capability levels. The identified capabilities are classified in three main categories including reliable environmental perception, single equipment decision-making toward safe outcomes and fleet-level safety enhancement. Finally, four different levels of automation are proposed for earthwork technology roadmap

Smart Buildings

This talk presents an efficient cyberphysical platform for the smart management of smart buildings http://www.deepint.net. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart building is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study at Salamanca - Ecocasa. This platform could enable smart building to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques

Smart territories

The concept of smart cities is relatively new in research. Thanks to the colossal advances in Artificial Intelligence that took place over the last decade we are able to do all that that we once thought impossible; we build cities driven by information and technologies. In this keynote, we are going to look at the success stories of smart city-related projects and analyse the factors that led them to success. The development of interactive, reliable and secure systems, both connectionist and symbolic, is often a time-consuming process in which numerous experts are involved. However, intuitive and automated tools like “Deep Intelligence” developed by DCSc and BISITE, facilitate this process. Furthermore, in this talk we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems, as well as the use of edge platforms or fog computing

AIoT for Achieving Sustainable Development Goals

Artificial Intelligence of Things (AIoT) is a relatively new concept that involves the merging of Artificial Intelligence (AI) with the Internet of Things (IoT). It has emerged from the realization that Internet of Things networks could be further enhanced if they were also provided with Artificial Intelligence, enhancing the extraction of data and network operation. Prior to AIoT, the Internet of Things would consist of networks of sensors embedded in a physical environment, that collected data and sent them to a remote server. Upon reaching the server, a data analysis would be carried out which normally involved the application of a series of Artificial Intelligence techniques by experts. However, as Internet of Things networks expand in smart cities, this workflow makes optimal operation unfeasible. This is because the data that is captured by IoT is increasing in size continually. Sending such amounts of data to a remote server becomes costly, time-consuming and resource inefficient. Moreover, dependence on a central server means that a server failure, which would be imminent if overloaded with data, would lead to a halt in the operation of the smart service for which the IoT network had been deployed. Thus, decentralizing the operation becomes a crucial element of AIoT. This is done through the Edge Computing paradigm which takes the processing of data to the edge of the network. Artificial Intelligence is found at the edge of the network so that the data may be processed, filtered and analyzed there. It is even possible to equip the edge of the network with the ability to make decisions through the implementation of AI techniques such as Machine Learning. The speed of decision making at the edge of the network means that many social, environmental, industrial and administrative processes may be optimized, as crucial decisions may be taken faster. Deep Intelligence is a tool that employs disruptive Artificial Intelligence techniques for data analysis i.e., classification, clustering, forecasting, optimization, visualization. Its strength lies in its ability to extract data from virtually any source type. This is a very important feature given the heterogeneity of the data being produced in the world today. Another very important characteristic is its intuitiveness and ability to operate almost autonomously. The user is guided through the process which means that anyone can use it without any knowledge of the technical, technological and mathematical aspects of the processes performed by the platform. This means that the Deepint.net platform integrates functionalities that would normally take years to implement in any sector individually and that would normally require a group of experts in data analysis and related technologies [1-322]. The Deep Intelligence platform can be used to easily operate Edge Computing architectures and IoT networks. The joint characteristics of a well-designed Edge Computing platform (that is, one which brings computing resources to the edge of the network) and of the advanced Deepint.net platform deployed in a cloud environment, mean that high speed, real-time response, effective troubleshooting and management, as well as precise forecasting can be achieved. Moreover, the low cost of the solution, in combination with the availability of low-cost sensors, devices, Edge Computing hardware, means that deployment becomes a possibility for developing countries, where such solutions are needed most

IoT and Blockchain for Smart Cities

Blockchain is a Distributed Ledger Technology (DLT) that makes it possible to secure any type of transaction. This is because the information stored on the Blockchain is immutable, impeding any type of fraud or modification of the data. It was first created for Bitcoin transactions; however, the research community has realized its potential quickly, and started using it for purposes other than cryptocurrency transactions. Blockchain may even be used to secure and provide reliability to the data being transmitted between computational systems, ensuring their immutability. Given the amount of data produced within a smart city, the use of Blockchain is imperative in smart cities, as it protects them from cyberattacks and fraud. Moreover, the transparency of the information stored on Blockchain means that it helps create a more just and democratic society