Natural Computing Applied to the Underground System: A Synergistic Approach for Smart Cities

Algorithm and Distributed Computing for the Internet of Things

The ongoing generalization of Internet of Things and its presence and application in multiple fields is generating a large amount of data that can be used to extract knowledge, among other purposes. In this context, algorithmic techniques and efficient computer systems provide an opportunity to successfully address efficient data processing and intelligent data analysis. As a result, multiple services can be improved, resources can be optimized and real-world problems of interest can be solved. This Special Issue on Algorithm and Distributed Computing for the Internet of Things gives the opportunity to know recent advances in the application of modern technologies hardware and software to the Internet of Things.

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability.

AI-Based Sensor Information Fusion for Supporting Deep Supervised Learning

In recent years, artificial intelligence (AI) and its subarea of deep learning have drawn the attention of many researchers. At the same time, advances in technologies enable the generation or collection of large amounts of valuable data (e.g., sensor data) from various sources in different applications, such as those for the Internet of Things (IoT), which in turn aims towards the development of smart cities. With the availability of sensor data from various sources, sensor information fusion is in demand for effective integration of big data. In this article, we present an AI-based sensor-information fusion system for supporting deep supervised learning of transportation data generated and collected from various types of sensors, including remote sensed imagery for the geographic information system (GIS), accelerometers, as well as sensors for the global navigation satellite system (GNSS) and global positioning system (GPS). The discovered knowledge and information returned from our system provides analysts with a clearer understanding of trajectories or mobility of citizens, which in turn helps to develop better transportation models to achieve the ultimate goal of smarter cities. Evaluation results show the effectiveness and practicality of our AI-based sensor information fusion system for supporting deep supervised learning of big transportation data.

Multi-Layer Problems and Solutions in VANETs: A Review

The Dedicated Short Range Communication (DSRC) technology supports the vehicular communications through Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication, by operating at 5.9 GHz band (U.S. Standard). The Physical (PHY) and Medium Access Control (MAC) Layer are defined by the IEEE 802.11p, while the IEEE 1609 family of standards define the Wireless Access in Vehicular Environment (WAVE); a suite of communication and security standards in the Vehicular Area Networks (VANETs). There has been a lot of research regarding several challenges in VANETs, from spectrum utilization to multichannel operation and from routing to security issues. The aim of all is to improve the performance of the network and support scalability in VANETs; which is defined as the ability of the network to handle the addition of vehicles (nodes) without suffering noticeable degradation of performance or administrative overhead. In this paper, we aim to highlight multilayer challenges concerning the performance of the VANETs, the already proposed solutions, and the possible future work.