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The rapid development of Internet-of-Things (IoT) computing leads to many problems, among which the management of massive mobile services has attracted much attention. When developers are looking for a service for IoT computing from mobile services, they typically try to discover the services according to annotations. If the mobile services are not assigned by proper annotations, it will be difficult for developers to find the suitable service. For providers, if the services that they provide cannot be used by developers, there will be no revenue. Therefore, it is a critical to assign proper semantic annotations to the mobile services. Existing approaches usually use the call records between services and developers to construct a score matrix, and compute the similarity between services and semantic annotations. However, these approaches do not leverage the natural association between services, providers and users. To make full use of the information inherent in services, we construct a heterogeneous information network (HIN) for service data, and propose a new model named GoT, which fully utilizes the structural and semantic information. GoT contains four components, which are the metapath construction, the intra-metapath fusion, the inter-metapath fusion, and the semantic annotation recommendation. We collected a real-world Web API dataset and performed adequate experiments. The experimental results show that our model produces superior recommendation accuracy and alleviates the cold-start problem. [ABSTRACT FROM AUTHOR]

Indoor multiple target tracking is a promising research field that attracts many efforts. Traditional approaches for tackling this problem are usually model-based methods. WiFi-based tracking approaches suffer from high cost in retrieving the CSI information. Most RF signal-based methods provide a mathematical framework correlating movement in space to a link's RSS value. Real RSS values are used to model the signal attenuation, and the distance correlation with signal attenuation is used to estimate locations. In this paper, we propose DCT, a noise-tolerant, unobtrusive and device-free tracking framework. DCT adopts density-based clustering to find the centers. We further use a linear function of mean RSS variances and target amount and FCM algorithm to adjust the number of targets and positions. The multiple particle filter (MPF) is adopted to refine the target tracking accuracy. DCT is tolerant for noise and multi-path effects, and can fast simultaneously tracking with a O(N) time complexity. The extensive experiments in trace-driven simulations and real implementations show that DCT is efficient and effective in tracking multiple target, and can achieve a high precision. [ABSTRACT FROM AUTHOR]

An accurate call traffic forecasting can help the call center to schedule and manage its employees more scientifically. Meanwhile, to meet the needs that some tasks in the call center require the prediction of call traffic in different time buckets for a future long term, it is necessary to forecast the call traffic in a long-term and multi-step way. However, existing forecasting methods cannot solve this problem as (1) Most existing methods merely focus on short-term forecasting for the next hour or the next day. (2) The temporal features of call traffic are ignored, which leads to a lower accuracy in long-term forecasting. Hence, in this paper, we propose a holistic solution for forecasting long-term multi-step ahead call traffic. In our method, we give a categorized way for temporal features by studying the call traffic data. After data preprocessing, we develop an extraction method for temporal features extraction for training the forecasting model. We propose two forecasting strategies based on taking different types of features as input. Experimental results on the real-world call traffic dataset show the effectiveness of our solution, including data preprocessing, temporal features mining, and the forecasting model. [ABSTRACT FROM AUTHOR]

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results. [ABSTRACT FROM AUTHOR]

According to the features of the AICE protocol, the authors select and extend the SVO logic to conduct the formal analysis of the protocol from the perspective of theoretical analysis of modal logic. The authors give the information flows of the AICE protocol by combining the privacy-preserving authentication (PPA) protocol and integrity checking protocol for MEC (ICE) together. In recent years, mobile networks and wireless networks have experienced explosive growth. [Extracted from the article]