The pervasiveness of the smart Internet of Things (IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection, etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition, promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated, which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

With the rapid development of cyberspace and smart home technology, human life is changing to a new virtual dimension with several promises for improving its quality. Moreover, the heterogeneous, dynamic, and internet-connected nature of smart homes brings many privacy and security difficulties. Unauthorized access to the smart home system is one of the most harmful actions and can cause several trust problems and relationship conflicts between family members and invoke home privacy issues. Access control is one of the best solutions for handling this threat, and it has been used to protect smart homes and other Internet of Things domains for many years. This survey reviews existing access control schemes for smart homes, which concern the essential authorization requirements and challenges that need to be considered while designing an authorization framework for smart homes. Furthermore, we note the most critical challenges that other access control solutions neglect for smart homes.

In this paper, we present the design and implementation of an avatar-based interactive system that facilitates rehabilitation for people who have received total knee replacement surgeries. The system empowers patients to carry out exercises prescribed by a clinician at the home settings more effectively. Our system helps improve accountability for both patients and clinicians. The primary sensing modality is the Microsoft Kinect sensor, which is a depth camera that comes with a Software Development Kit (SDK). The SDK provides access to 3-dimensional skeleton joint positions to software developers, which significantly reduces the challenges in developing accurate motion tracking systems, especially for use at home. However, the Kinect sensor is not well-equipped to track foot orientation and its subtle movements. To overcome this issue, we augment the system with a commercial off-the-shelf Inertial Measurement Unit (IMU). The two sensing modalities are integrated where the Kinect serves as the primary sensing modality and the IMU is used for exercises where Kinect fails to produce accurate measurement. In this pilot study, we experiment with four rehabilitation exercises, namely, quad set, side-lying hip abduction, straight raise leg, and ankle pump. The Kinect is used to assess the first three exercises, and the IMU is used to assess the ankle pump exercise.