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

Smart textile waste collection system – Dynamic route optimization with IoT

Increasing textile production is associated with an environmental burden which can be decreased with an improved recycling system by digitalization. The collection of textiles is done with so-called curbside bins. Sensor technologies support dynamic-informed decisions during route planning, helping predict waste accumulation in bins, which is often irregular and difficult to predict. Therefore, dynamic route-optimization decreases the costs of textile collection and its environmental load. The existing research on the optimization of waste collection is not based on real-world data and is not carried out in the context of textile waste. The lack of real-world data can be attributed to the limited availability of tools for long-term data collection. Consequently, a system for data collection with flexible, low-cost, and open-source tools is developed. The viability and reliability of such tools are tested in practice to collect real-world data. This research demonstrates how smart bins solution for textile waste collection can be linked to a dynamic route-optimization system to improve overall system performance. The developed Arduino-based low-cost sensors collected actual data in Finnish outdoor conditions for over twelve months. The viability of the smart waste collection system was complemented with a case study evaluating the collection cost of the conventional and dynamic scheme of discarded textiles. The results of this study show how a sensor-enhanced dynamic collection system reduced the cost 7.4% compared with the conventional one. We demonstrate a time efficiency of −7.3% and that a reduction of 10.2% in CO2 emissions is achievable only considering the presented case study.publishedVersionPeer reviewe

Desarrollo de un sistema inteligente para la gestión de la recolección de desechos sólidos urbanos basado en comunicación inalámbrica

El presente trabajo de titulación tuvo como objetivo desarrollar un sistema inteligente para la gestión de la recolección de desechos sólidos urbanos basado en comunicación inalámbrica, para lograrlo se definieron como requerimientos la generación de un modelo de red inalámbrica que considere a los basureros como nodos que se enlazan a un interfaz gráfica remota, la codificación de un algoritmo de visión artificial que identifique el nivel de llenado de los contenedores de basura y la obtención de su ubicación, como resultado se obtuvo un sistema inteligente, codificado en Python y procesado en una Raspberry Pi 4 Model B que mediante un modelo de visión artificial basado en el reconocimiento óptico de caracteres (OCR) detecta el nivel de llenado de los contenedores de basura y envía esta información a una interfaz gráfica remota creada en la plataforma Blynk IoT que monitorea el estado de los contenedores de basura, muestra su ubicación, el contenedor lleno más cercano y le permite al usuario controlar el acceso a los mismos, los prototipos tienen acoplado un servomotor MG996R que bloquea la puerta cuando están llenos y un sensor infrarrojo FC-51 que activa una alerta de voz cuando se intenta acceder a estos. Con las pruebas de funcionalidad se determinó que el algoritmo de visión artificial tiene una eficiencia del 86,36% cuando es usado con desperdicios de colores oscuros, la función del basurero más cercano es útil únicamente si se emplea en zonas residenciales, el tiempo de respuesta del sistema de bloqueo de la puerta es de 28,19 segundos, mientras que el del sistema de alarma es de 20,11 segundos. Para trabajos futuros se recomienda mantener una iluminación constante y probar el prototipo con una red de internet estable.The objective of this degree work was to develop an intelligent system for the management of urban solid waste collection based on wireless communication. To achieve this, some requirements have been defined, such as the generation of a wireless network model that considers the dumpsters as nodes connected to a remote graphical interface, the coding of an artificial vision algorithm that identifies the fill level of the dumpsters, and the determining of their location. As a result, an intelligent system coded in Python and processed on a Raspberry Pi 4 Model B was obtained, which by means of an artificial vision model based on optical character recognition (OCR) detects the filling level of the garbage containers and sends this information to a remote graphic interface created on the Blynk IoT platform. The remote graphical interface monitors the status of dumpsters, displays their location and the closest filled bin, as well as allows the user controls access to them. The prototypes are coupled with an MG996R servo motor that locks the door when full and an FC-51 infrared sensor that triggers a voice alert when access is attempted. Functional tests showed that the image processing algorithm has an efficiency of 86.36% when used with dark-colored waste, and the "closest bin" function only proved useful when used in residential areas. Finally, the response time of the door locking system is 28.19 seconds, while that of the alarm system is 20.11 seconds. For future work, it is recommended to maintain constant lighting and test the prototype with a stable internet network