Abstract: With the rapid advancement of intelligent agriculture, the application of IoT technology in aquaculture is becoming more and more widespread. In this process, there's a lot of structured, semi-structured, unstructured data. On the one hand, traditional relational database management systems cannot store this data flexibly and scalably. On the other hand, the stream data generated by the sensor usually requires a flow processing operation with high throughput, low latency and high performance.Therefore, based on Flink, MongoDB and Kafka, this paper proposes and implements an aquaculture monitoring system. Among them, Flink platform provides high throughput, low latency and high performance stream processing as a stream data processing platform. Kafka, as a distributed publish-subscribe message system, acquires different sensor data and builds reliable pipelines for transmitting real-time data between application programs. MongoDB stores sensor data in different formats. Finally,as a highly reliable and high-performance column database, HBase is often used in sensor data storage schemes. So, this paper presents a performance evaluation on how efficiently MongoDB and HBase insertions and queries perform. The experimental results show that the efficiency of MongoDB was much higher than that of HBase, which provided a feasible solution for the sensor data storage and processing of aquaculture.

Abstract: Hydroponic cultivation is an agricultural method where nutrients are efficiently provided as mineral nutrient solutions. This modern agriculture sector provides numerous advantages such as efficient location and space requirements, adequate climate control, water-saving and controlled nutri-ents usage. The Internet of things (IoT) concept assumes that various “things,” which include not only communication devices but also every other physical object on the planet, are going to be connected and will be controlled across the Internet. Mobile computing technologies in general and mobile applications, in particular, can be assumed as significant meth-odologies to handle data analytics and data visualisation. Using IoT and mobile computing is possible to develop automatic systems for enhanced hydroponic agriculture environmental monitoring. Therefore, this paper presents an IoT monitoring system for hydroponics named iHydroIoT. The solution is composed of a prototype for data collection and an iOS mobile application for data consulting and real-time analytics. The collected data is stored using Plotly, a data analytics and visualisation library. The proposed system provides not only temporal changes monitoring of light, tempera-ture, humidity, CO2, pH and electroconductivity but also water level for enhanced hydroponic supervision solutions. The iHydroIoT offers real-time notifications to alert the hydroponic farm manager when the condi-tions are not favourable. Therefore, the system is a valuable tool for hydro-ponics condition analytics and to support decision making on possible in-tervention to increase productivity. The results reveal that the system can generate a viable hydroponics appraisal, allowing to anticipate technical interventions that improve agricultural productivity.

Abstract: Environmental noise pollution has a significant impact on health. The noise effects on health are related to annoyance, sleep and cognitive performance for both adults and children are reported in the literature. The smart city concept can be assumed as a strategy to mitigate the problems generated by the urban population growth and rapid urbanisation. Noise mapping is an important step for noise pollution reduction. Although, noise maps are particularly time-consuming and costly to create as they are produced with standard methodologies and are based on specific sources such as road traffic, railway traffic, aircraft and industrial. Therefore, the actual noise maps are significantly imperfect because the noise emission models and sources are extremely limited. Smartphones have incredible processing capabilities as well as several powerful sensors such as microphone and GPS. Using the resources present in a smartphone as long with participatory sensing, a crowdsourcing noise mobile application can be used to provide environmental noise supervision for enhanced living environments. Crowdsourcing techniques applied to environmental noise monitoring allow creating reliable noise maps at low-cost. This paper presents a mobile crowdsourcing solution for environmental noise monitoring named iNoiseMapping. The environmental noise data is collected through participatory sensing and stored for further analysis. The results obtained can ensure that mobile crowdsourcing offers several enhanced features for environmental noise supervision and analytics. Consequently, this mobile application is a significant decision-making tool to plan interventions for noise pollution reduction.

Abstract: Indoor environment quality (IEQ) has a significant impact on all human activities. Temperature and humidity are extremely important not only for enhanced living environments but particularly for supervising laboratory activities. On the one hand, laboratories are spaces characterised by numerous pollution sources that can lead to relevant unhealthy indoor environments. The laboratory activities such as the case of thermography experiments require real-time monitoring supervision. On the other hand, buildings are responsible for about 40% of the global energy consumption, and over 30% of the CO2 emissions; also, a considerable proportion of this energy is used for thermal comfort. The IBM Watson IoT Platform is a fully managed, cloud-hosted service for the Internet of Things (IoT) that allows data to be sent securely to the cloud using MQTT messaging protocol. This paper aims to present an IoT solution for indoor temperature and humidity real-time supervision named iTemp+. The solution is composed by a hardware prototype for ambient data collection and use IBM Watson IoT for data storing and consulting. The IBM Watson IoT Platform provides data integration, security methods, data collection, visualization, analytics, device management, artificial intelligence and blockchain functionalities which are not implemented in the concurrent IoT platforms. The results obtained reveal that IBM Watson IoT platform offers several enhanced features for device management and analytics and can be used as a powerful approach to provide IEQ supervision.