IoT big data value map : how to generate value from IoT data

Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations

IoT-BASED SMART GARDEN USING MQTT PROTOCOL WITH ADAFRUIT IO APP

During the COVID-19 pandemic, more community activities were at home with government policies to limit people's movements, especially in urban areas. During the period of restrictions on people's movements due to COVID-19, it was also followed by new habits, namely activities around the house, one of which was the increasing trend of Urban Farming to fill time. However, after the pandemic began to experience a downward trend, people no longer cared for their plants due to activities that had returned to normal. In this research, the design of Internet of Things technology was carried out to help urban communities to monitor and control plants so that people continue to carry out their daily activities without worrying about their plants running out of water. This study uses the NodeMCu V3 microcontroller as the data receiving center at the hardware layer and then a DHT 11 sensor is needed for temperature monitoring, a Soil Moisture sensor for monitoring soil moisture, a raindrop sensor for monitoring rain conditions as well as relays and a mini pump to control the water needs of plants. The results of this study are that the IoT device successfully displays temperature data, soil moisture data, rain status and automatic plant watering controls function properly if the value on the soil moisture sensor is below 25. The data communication path in this study uses the MQTT protocol using a webserver from Adafruit IO with an average packet loss of 0.20%. With the functioning of all the components on the created IoT device, the community can apply an IoT-based Smart Garden in urban farming so that plant maintenance can still be carried out

La investigación sobre curación de contenidos: análisis de la producción académica

En este artículo se pretende analizar la producción científica sobre curación de contenidos, estudiando su evolución temporal, aspectos de autoría como las colaboraciones entre autores y sus filiaciones, las revistas y congresos más relevantes así como las temáticas más tratadas. Entre los resultados, se puede destacar la multidisciplinariedad de los estudios sobre curación de contenidos, ya que encontramos ejemplos realizados desde el periodismo a la ingeniería e informática, o desde la educación a la documentación, una distribución de autorías y filiaciones en muchos investigadores y centros, así como una fuerte vinculación temática con los medios sociales, que son los canales más utilizados para su difusión

Reliable Web Service Consumption Through Mobile Cloud Computing

The mobile intermittent wireless connectivity limits the evolution of the mobile landscape. Achieving web service reliability results in low communication overhead and correct retrieval of the appropriate state response. In this chapter, we discuss and analyze two approaches based on middleware approach, Reliable Service Architecture using Middleware (RSAM), and Reliable Approach using Middleware and WebSocket (RAMWS). These approaches achieve the reliability of web services consumed by mobile devices and propose an enhanced architecture that achieves the reliability under various conditions with minimum communication data overhead. In these experiments, we covered several cases to prove the achievement of reliability. Results also show that the request size was found to be constant, the response size is identical to the traditional architecture, and the increase in the consumption time was less than 5% with the different response sizes

Real time stream processing for internet of things

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Nesnelerin İnterneti 'nin işletmeler arasında popülerliğinin artmasıyla, izleme ve analiz IoT verilerinin araştırılması ve geliştirilmesi artmıştır. Büyük veri kaynaklarından biri olan Nesnelerin interneti, veri mühendislerinden dikkat çekiyor. Asıl zorluk, büyük miktarda IoT olayının gerçek zamanlı akış işlemesidir. Veri transferini, büyük ölçekli verileri gerçek zamanlı olarak depolamayı, işlemeyi ve analiz etmeyi içerir. Milyarlarca IoT cihazı, istihbaratı gerçek zamanda elde etmek için analiz edilmesi gereken çok miktarda veri üretir. Bu tezde, IoT için gerçek zamanlı akış işlemek için birleştirilmiş bir çözüm önerilmiştir. Önerilen yöntemde, hava istasyonu verilerinin IoT olayları Apache Kafka kullanılarak üretilir ve bir konuya yayınlanır. Bu veriler Apache Spark tüketicisi tarafından tüketilmekte ve RDD'ye dönüştürülmektedir. Spark SQL'i kullanarak, verileri analiz etmek için farklı sorguların uygulandığı veri çerçeveleri oluşturulur. Veriler Cassandra'ya kaydedilir ve Zeppelin notebook verileri görselleştirmek için kullanılır. Spark'deki makine öğrenme kütüphanesini kullanarak gerçek zamanlı tahminler yapmak için bir veri kümesine Lojistik Regresyon algoritması uygulanır. Sonunda, tüm ölçüm farklı metrikleri değiştirerek ve gecikmeyi azaltarak hızlanır. Sonuçlar, bu yöntemin gerçek zamanlı olarak büyük IoT veri kümelerini işlemek için eksiksiz bir çözüm sunduğunu göstermektedir.With the increase in popularity of IoT among enterprises, the research and development in the field of monitoring and analyzing IoT data has been increased. Iot, being one of the major sources of big data is getting attention from data engineers. The main challenge is real time stream processing of large amount of IoT events. It includes data transfer, storing, processing and analyzing large scale of data in real time. Billions of IoT devices generate huge amount of data that should be analyzed for deriving intelligence in real time. In this thesis, a unified solution for real time stream processing for IoT is proposed. In the proposed method, sample IoT events of weather station data are generated using Apache Kafka and published to a topic. This data is consumed by Apache Spark consumer which converted it into RDDs. Using Spark SQL, data frames are generated, on which different queries are applied to analyze the data. Data is saved to Cassandra and Zeppelin notebook is used to visualize the data. Logistic Regression algorithm is applied on a data set to make predictions in real time using machine learning library in Spark. In the end, the whole method is speed up by altering different metrics and reducing delay. Results show that this method provides a complete solution to process large IoT data sets in real time

A cloud-based Analytics-Platform for user-centric Internet of Things domains – Prototype and Performance Evaluation

Data analytics have the potential to increase the value of data emitted from smart devices in user-centric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Software tools for conducting real-time information processing and visualization in industry: an up-to-date review

The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed.This work is funded by “FCT—Fundação para a Ciência e Tecnologia” within the R&D Units Project Scope: UIDB/00319/2020. The grants of R.S., R.M., A.M., and N.L. are supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internalization Programme (COMPETE 2020). [Project n. 039479. Funding Reference: POCI-01-0247-FEDER-039479]

Experimental Evaluation of Publish/Subscribe-based Spatio-Temporal Contents Management on Geo-Centric Information Platform

Cross-domain data fusion is becoming a key driver to growth of the numerous and diverse applications in IoT era. Nevertheless, IoT data obtained by individual devices are blindly transmitted to cloud servers. We here focus on that the IoT data which are suitable for cross-domain data fusion, tend to be generated in the proximity, and thus propose a Geo-Centric Information Platform (GCIP) for the management of Spatio-Temporal Contents (STCs) generated through the cross-domain data fusion. GCIP enables to keep STCs near the users (at an edge server). In this paper, we practically examine the fundamental functions of the GCIP from two aspects: (1) Geo-location aware data collection and (2) Publish/Subscribe-based STC production. Furthermore, we implement a proof-of-concepts (PoC) of GCIP and conduct experiments on a real IPv6 network built on our campus network. In this experiment, we showed that multiple types of IoT data generated in the proximity can be collected on the edge server and then a STC can be produced by exploiting the collected IoT data. Moreover, we demonstrated that the Publish/Subscribe model has a potential to be effective for STC management.22nd International Conference on Network-Based Information Systems(NBiS 2019), September 5-7, 2019, Oita, Japa