The Internet of Things (IoT) represents the physical world of devices and objects connected over the network using wireless sensors. This chapter gives detailed study about the different applications of IoT with the integration of WSN (wireless sensor networks) with Internet connectivity. This allows applications to communicate among themselves and users on a global scale. A large number of IoT applications, like smart home, buildings, transport, water management, healthcare, agriculture, environment and industries, together form the smart city. Along with this, various challenges in the implementation of applications are discussed related to the reliability, sustainability, and efficiency. An open architecture looking into current need of IoT is also proposed and discussed.

Integration of WSN with IoT Applications: A Vision, Architecture, and Future Challenges

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V’s challenges and envisions IoTBD as “Big Data 2.0.” Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.

A Survey on IoT Big Data: Current Status, 13 V’s Challenges, and Future Directions

The increase in industrial activities and the rapid urbanization of human populations had a terrible effect on global air quality. Thousands of factories and billions of vehicles release enormous amounts of pollutants into the air every day; dangerously affecting human health. Many epidemiological studies pointed out the responsibility of air pollution in many health issues, the reason why monitoring air quality became an obligation to prevent or limit these issues. Conventional systems based on measuring stations are expensive and offer low data granularity. As a result, researchers are increasingly targeting IoT-based systems. However, elaborating a new system for air quality monitoring requires an awareness of the state of the art and the mastery of a certain amount of specific knowledge (pollutants, their health effects, the sensing equipment, the IoT possible configuration,...). This paper aims to answer these necessities by reviewing the existing works on air quality monitoring using IoT with the focus on lasted trends and challenges.

Air Quality Monitoring Using IoT: A Survey

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study.

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Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The validation of a cloud system can be complicated by the size of the system, the number of users that can concurrently request services, and the virtualization used to give the illusion of using dedicated machines. Unfortunately, it is not feasible to use conventional testing methods with cloud systems. This article proposes a framework, called TEA- Cloud , that integrates simulation with testing methods for validating cloud system designs. Testing is applied on both functional and nonfunctional aspects of the cloud, like performance and cost. The aim of the framework is to provide a complete methodology to help users to model both software and hardware parts of cloud systems and automatically test the validity of these clouds using a cost-effective approach. Metamorphic testing is used to overcome the lack of an oracle that checks whether the behavior observed in testing is allowed. Metamorphic testing is based on metamorphic relations (MRs). We define three families of MRs, which target issues such as performance, resource provisioning, and cost. TEA- Cloud was evaluated through an empirical study that used fault seeding (mutation) and ten MRs for testing different cloud configurations. The results were promising, with TEA- Cloud finding all seeded faults.

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TEA- Cloud : A Formal Framework for Testing Cloud Computing Systems

A stream processing architecture for heterogeneous data sources in the Internet of Things

Over the last years, air pollution and air quality have received increasing attention in the scope of Internet of Things and smart cities, since they can seriously affect citizens’ health. However, current systems for air quality monitoring and notification lack essential key requirements in order to be effective as far as users’ access to the information is concerned and, particularly, the provision of context-aware notifications. This paper presents Air4People, an air quality monitoring and context-aware notification system, which submits personalized alerts to citizens based on several types of context, whenever air quality-related health risks are detected for their particular context.

Air4People: a Smart Air Quality Monitoring and Context-Aware Notification System.

A microservice architecture for real-time IoT data processing: a reusable web of things approach for smart ports

Organisations today are constantly consuming and processing huge amounts of data. Such datasets are often heterogeneous, making it difficult to work with them quickly and easily due to their format constraints or their disparate data structures. Therefore, being able to efficiently and intuitively work with such data to analyse them in real time to detect situations of interest as quickly as possible is a great competitive advantage for companies. Existing approaches have tried to address this issue by providing users with analytics or modelling tools in an isolated way, but not combining them as a one-in-all solution. In order to fill this gap, we present MEdit4CEP-SP, a model-driven system that integrates Stream Processing (SP) and Complex Event Processing (CEP) technologies for consuming, processing and analysing heterogeneous data in real time. It provides domain experts with a graphical editor that allows them to infer and define heterogeneous data domains, while also modelling, in a user-friendly way, the situations of interest to be detected in such domains. These graphical definitions are then automatically transformed into code, which is deployed in the processing system at runtime. The alerts detected by the system, in real-time, allow users to react as quickly as possible, thus improving the decision-making process. Additionally, MEdit4CEP-SP provides persistence, storing these definitions in a NoSQL database to permit their reuse by other instances of the system. Further benefits of this system are evaluated and compared with other existing approaches in this paper.

MEdit4CEP-SP: A model-driven solution to improve decision-making through user-friendly management and real-time processing of heterogeneous data streams

David Corral-Plaza

Papers

A stream processing architecture for heterogeneous data sources in the Internet of Things

Air4People: a Smart Air Quality Monitoring and Context-Aware Notification System.

A microservice architecture for real-time IoT data processing: a reusable web of things approach for smart ports

MEdit4CEP-SP: A model-driven solution to improve decision-making through user-friendly management and real-time processing of heterogeneous data streams

A sensor fusion system identifying complex events for localisation estimation