Kuo Zhao

In this paper, we categorize and summarize recent advances in the Internet of Things (IoT) to complement existing IoT survey papers that have not covered its very latest developments. We do this from the three perspectives: information fusion in the IoT, service-oriented IoT, and cloud-centric IoT. These three aspects are largely missing in current literature. This survey contributes to better understanding of the challenges posed by the existing IoT and its likely future developments. The IoT is in the early stages of development. Challenges for future development include network scalability, a continuously produced data stream, and methods for handling heterogeneous data. This review provides insight regarding the three aforementioned aspects. In addition, we briefly summarize the existing literature with explicit references to the corresponding survey papers, thus, providing a panoramic view of the IoT without repeating what is already available in the literature.

With the rapid development of the Internet of Things (IoT), a variety of sensor data are generated around everyone’s life. New research perspective regarding the streaming sensor data processing of the IoT has been raised as a hot research topic that is precisely the theme of this paper. Our study serves to provide guidance regarding the practical aspects of the IoT. Such guidance is rarely mentioned in the current research in which the focus has been more on theory and less on issues describing how to set up a practical system. In our study, we employ numerous open source projects to establish a distributed real time system to process streaming data of the IoT. Two urgent issues have been solved in our study that are (1) multisource heterogeneous sensor data integration and (2) processing streaming sensor data in real time manner with low latency. Furthermore, we set up a real time system to process streaming heterogeneous sensor data from multiple sources with low latency. Our tests are performed using field test data derived from environmental monitoring sensor data collected from indoor environment for system validation. The results show that our proposed system is valid and efficient for multisource heterogeneous sensor data integration and streaming data processing in real time manner.