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Matrix Factorization Techniques for Recommender Systems

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Current research on Internet of Things (IoT) security: A survey

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility are four major functions of cognitive radio systems. Spectrum sensing is utilized to observe the spectrum occupancy status and recognize the channel availability, while CR users dynamically access the available channels through the regulation processes of spectrum decision, spectrum sharing, and spectrum mobility. To alleviate the processing delays involved in these four functions and to improve the efficiency of spectrum utilization, spectrum prediction for cognitive radio networks has been extensively studied in the literature. This article surveys the state of the art of spectrum prediction in cognitive radio networks. We summarize the major spectrum prediction techniques, illustrate their applications, and present the relevant open research challenges.

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Spectrum prediction in cognitive radio networks

The problem of channel quality prediction in cognitive radio networks is investigated in this paper. First, the spectrum sensing process is modeled as a Non-Stationary Hidden Markov Model (NSHMM), which captures the fact that the channel state transition probability is a function of the time interval the primary user has stayed in the current state. Then the model parameters, which carry the information about the expected duration of the channel states and the spectrum sensing accuracy (detection accuracy and false alarm probability) of the SU, are estimated via Bayesian inference with Gibbs sampling. Finally, the estimated NSHMM parameters are employed to design a channel quality metric according to the predicted channel idle duration and spectrum sensing accuracy. Extensive simulation study has been performed to investigate the effectiveness of our design. The results indicate that channel ranking based on the proposed channel quality prediction mechanism captures the idle state duration of the channel and the spectrum sensing accuracy of the SUs, and provides more high quality transmission opportunities and higher successful transmission rates at shorter spectrum waiting times for dynamic spectrum access.

/pdf/channel-quality-prediction-based-on-bayesian-inference-in-8cysfx4b7v.pdf

Channel quality prediction based on Bayesian inference in cognitive radio networks

This article provides a comprehensive overview on jamming strategies in the physical layer for securing wireless communications. As a complement to traditional cryptography-based approaches, physical layer based security provisioning techniques offer a number of promising features that have not been previously available. Among the physical layer security techniques, jamming is an effective way to degrade the channel quality of the eavesdroppers for ensuring security. We start by giving a brief introduction to the fundamental principles of physical layer security and jamming. Then we classify jamming strategies from three different perspectives and explain the major related designs in various scenarios. Finally, we discuss the open issues of jamming that can be helpful to foster future research.

Jamming Strategies for Physical Layer Security

The benefits of cognitive radio networking (CRN) have been well recognized with the emerging wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interferences to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the PUs. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem is how to select a relay efficiently. Due to the potentially large number of SUs, it is infeasible for a PU to first scan all the SUs and then pick the best. Basically, the PU transmitter intends to observe the SUs sequentially. After observing an SU, the PU needs to make a decision regarding whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To evaluate the performance of our proposed scheme, we compare our optimal stopping policy with the random selection policy through simulation study, and the results demonstrate the superiority of our policy. Extensive simulation study is conducted to investigate the impact of different parameters on the system performance, and the results indicate that our algorithm can satisfy different system requirements by carefully tuning the corresponding system parameters.

/pdf/cooperative-relay-selection-in-cognitive-radio-networks-1vqx4wmjbl.pdf

Cooperative Relay Selection in Cognitive Radio Networks

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network when there exist multiple primary channels Deviated from the existing research our work focuses on a scenario in which each secondary user has the freedom to decide whether or not to participate in cooperative spectrum sensing; if not, the SU becomes a free rider who can eavesdrop the decision about the channel status made by others Such a mechanism can conserve the energy for spectrum sensing at a risk of scarifying the spectrum sensing performance To overcome this problem, we address the following two questions: “which action (contributing to spectrum sensing or not) to take?” and “which channel to sense?” To answer the first question, we model our framework as an evolutionary game in which each SU makes its decision based on its utility history, and takes an action more frequently if it brings a relatively higher utility We also develop an entropy based coalition formation algorithm to answer the second question, where each SU always chooses the coalition (channel) that brings the most information regarding the status of the corresponding channel All the SUs selecting the same channel to sense form a coalition Our simulation study indicates that the proposed scheme can guarantee the detection probability at a low false alarm rate

/pdf/utility-based-cooperative-spectrum-sensing-scheduling-in-4q1k6knn36.pdf

Tao Jing

Papers

Spectrum prediction in cognitive radio networks

Channel quality prediction based on Bayesian inference in cognitive radio networks

Jamming Strategies for Physical Layer Security

Cooperative Relay Selection in Cognitive Radio Networks

Utility-based cooperative spectrum sensing scheduling in cognitive radio networks