۱- Introduction

۲- IoT domains

۳- Big Data processes and life cycle

۴- Big Data approaches in different IoT domains

۵- Comparison of IoT domains from Big Data perspective

۶- Conclusion

References

Abstract

With the rapid development of the Internet of Things (IoT), Big Data technologies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is extensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data approaches in each IoT domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.

Introduction

Internet of Things (IoT) is one of the most promising technologies in the current epoch. This research paradigm is characterized by using smart and self-configuring objects that can interact with each other via global network infrastructure. Therefore, these seamless interactions between large amounts of heterogeneous objects represent IoT as a disruptive technology that enables ubiquitous and pervasive computing applications [1]. Accordingly, a wide range of industrial IoT applications [2, 3, 4] have been developed and deployed in different domains such as transportation, agriculture, energy, healthcare, food processing industry, military, environmental monitoring, or security surveillance. Since IoT connects the sensors and other devices to the Internet, it plays an important role to support the development of smart services. In other words, the dynamic things collect different kinds of data from the real-world environment. Afterwards, the extraction of relevant information from IoT data can be used to improve and enrich our daily life with context-aware applications, which can for example display contents related to the current situation of the user. Further, context can be defined as the information that is used to characterize the situation of entities (i.e. whether a person, place or object) and the situation is considered to be relevant to the real-time interaction between a user and an application, including the user and the application themselves [5]. AS context is typically featured by location, time, state of people, and environmental settings, IoT becomes an important source of contextual data with an enormous volume, variety and velocity, which makes it an interesting and challenging domain for Big Data research.