With the development of the Internet of Things, it has been widely studied and deployed in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT makes the data density and the data dimension higher, where anomaly detection is important to ensure hardware and software security. However, the traditional anomaly detection algorithm has difficulty meeting this demand, not only in complexity but also accuracy. Sometimes the anomaly can be well reconstructed, resulting in a low reconstruction error. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which aims to use reconstruction errors to determine data anomalies. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented autoencoder TSMAE is designed for time-series data anomaly detection. Second, by adding penalties and derivable rectifier functions to loss to make the addressing vector sparse, memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Finally, through experiments on ECG and Wafer datasets, the validity of TSMAE is verified, and the rationality of hyperparameter setting is discussed through visualizing the memory module addressing vector.

With the prevalent application of Internet-of-Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for quality-of-service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study QoS prediction in IoT environments, where contextual information is vital. In this paper, we develop a holistic framework to attack the QoS prediction in IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world datasets, and the experimental results verify the effectiveness of the proposed framework.

The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work. First , we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second , we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third , we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins. Finally , we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj.io .

The emergence of the Internet of Vehicles (IoV) introduces challenges regarding computation-intensive and time-sensitive related services for data processing and communication. Limited resource availability increases the processing latency and may cause application interruption due to the mobility of vehicles. To address the real-time requirements of users and tasks, mobile edge computing (MEC), in which data are processed at the network edge, has been proposed to collaborate with the cloud to provide better performance. However, the offloading strategies proposed previously have some shortcomings in addressing issues such as task dependency and resource competition. In this paper, we propose a novel offloading strategy for MEC, Com-DDPG, in which multiagent reinforcement learning is used to enhance the offloading performance. Within the IoV transmission radius, multiple agents work together to learn the changes in the environment, such as the number of mobile devices and the queue of tasks, and take appropriate action in the form of a strategy for offloading to an edge server. First, we discuss models of task dependency, task priority, and resource consumption from the perspective of server clusters and multiple dependencies among tasks. In the proposed method, the communication behavior among multiple agents is formulated; then, the policy determined through reinforcement learning is executed as an offloading strategy to obtain the corresponding results. Second, to enhance the communication of information among multiple agents, a long short-term memory (LSTM) network is employed as an internal state predictor to provide a more complete environmental state, and a bidirectional recurrent neural network (BRNN) is used to learn and enhance the features obtained from the agents' communication. Finally, experiments carried out based on the Alibaba Cluster Dataset are presented. The results show that our method is superior to baseline methods in terms of energy consumption, load status and latency.

In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques. In mobile edge computing, there are not many attempts that build neural network models in service recommendation or QoS (quality-of-service) prediction. The method proposed in this paper is an attempt to employ neural network technique for QoS prediction. Compared to the pure use of QoS records, the exploration for context information in QoS prediction also still needs a lot of efforts. But an increasing number of features are highly likely to result in overfitting problem, especially in the case that the data size is small. To solve those problems, in this paper, we propose several new techniques, including denoising auto-encoder with fuzzy clustering (DAFC) and recombination embedding network, focusing on how to use context information and how to alleviate overfitting problem. DAFC uses the denoising auto-encoder, which helps the fuzzy clustering algorithm overcome the defect that the performance is easy to be impacted by the number of clusters. Extensive experiments under different data densities show that these two network structures indeed improve the performance and reduce the overfitting problem.