Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. HetIoT is being employed in increasing number of areas, such as smart home, smart city, intelligent transportation, environmental monitoring, security systems, and advanced manufacturing. Therefore, relaying on strong application fields, HetIoT will be filled in our life and provide a variety of convenient services for our future. The network architectures of IoT are intrinsically heterogeneous, including wireless sensor network, wireless fidelity network, wireless mesh network, mobile communication network, and vehicular network. In each network unit, smart devices utilize appropriate communication methods to integrate digital information and physical objects, which provide users with new exciting applications and services. However, the complexity of application requirements, the heterogeneity of network architectures and communication technologies impose many challenges in developing robust HetIoT applications. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. Then, the state of the art in HetIoT research and applications have been discussed. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including self-organizing, big data transmission, privacy protection, data integration and processing in large-scale HetIoT.

More recently, wind speed forecasting as an effective computing technique currently plays an important role in advancing industry informatics, while dealing with those issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this article proposes a more practical approach through the combination of extreme learning machine (ELM) method and deep learning model. ELM as a novel computing paradigm enables the neural network- (NN-) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep learning framework, which works efficiently on memory consumption decrease. In this article, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.

In recent years, more and more wearable senors have been employed in smart health applications. Wearable sensors not only can be used to collect valuable health-related data of their users, they can be also used in conjunction with other infrastructure-bound sensors, such as Microsoft Kinect sensor, to facilitate privacy-aware fine-grained activity tracking. This fusion of multi-modal data promises a new type of smart health applications that coach a user to live a healthier life style by monitoring the user in realtime and reminding him or her when he or she engages in an unhealthy activity. In this article, we investigate how to achieve fine-grained activity recognition in the context of such an application. In our scheme, the identification accuracy is improved by incorporating a nonlinear and local similarity measure, namely kernel risk-sensitive loss (KRSL), into a novel multilayer neural network (NN) learning algorithm, called as stacked extreme learning machine (S-ELM). Furthermore, to achieve a good generalization performance with minimal human intervention, Jaya as a popular optimization algorithm, is also used to adjust key parameters in our proposed approach. The experiments are conducted to verify the effectiveness of the proposed scheme.