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

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.

Automated summary

Driven by core technologies like sensor-based data acquisition and cloud-based big data analysis, IoT automates intelligent actions on connected objects. However, data challenges have slowed down IoT's growth, leading to a need for systematic surveys to identify and address uncharted challenges in IoTBD management. The survey explores the state-of-the-art in IoT and proposes a taxonomy for IoTBD, while also analyzing vendor services and envisioning IoTBD as Big Data 2.0.