This paper discusses present issues and future challenges of the Internet of Things (IoT). The authors define Future IoT (FIoT) as innovative techniques and requirements of the IoT that require services that are not currently supported by the existing Internet architecture. The promising directions of IoT/FIoT are indicated as twofold: system design and information analysis. This work first introduces IoT and FIoT, followed by the presentation of an integrated data management framework for IoT. It then discusses data analysis-oriented present challenges and research trends in this domain. The brief introduction of IoT includes discussions on definitions, standards, layered architecture, and popular research directions.

Then, the work starts discussing its main focus: the proposal of a data management framework for FIoT, based on computational intelligence and data mining. More precisely, the authors propose a swarm optimization-based intelligent data management framework: data management framework-based swarm optimization (DMFSO). In the proposed framework, the data source component consists of the following layers from top to bottom: social layer, application layer, network layer, and perception layer. The sensors and appliances belong to the agents in the perception layer. On the other hand, the data analysis component consists of the following layers from top to bottom: interpretation layer, management and mining layer, and extraction layer. Some scenarios are briefly discussed; for example, ten agents exchange information and make the decisions in a smart home scenario. The task of the ten agents is to control five cameras and five lights.

Overall, in this paper, the usage of swarm intelligence (SI) for IoT is not very clear or justified in detail. Also, SI may not be sufficient to solve all of the challenges in this layer. That being said, this could be a good reference for readers interested in data intelligence perspectives in IoT.