The Medium Access Control (MAC) protocol that uses non-overlapping multiple channels, called the multi-channel MAC protocol, was proposed in order to increase the capacity of ad hoc networks. Since the number of packet interfaces on each node is less than the number of channels in ad hoc networks in general, the node needs to select a suitable channel for data transmission. This means that the multi-channel MAC protocol must be provided with a good channel selection algorithm. In this paper, we design a channel selection algorithm called Conditionally Randomized Channel Selection (CRCS) based on Extended Receiver Directed Transmission (xRDT) protocol that only uses one packet interface. Briefly, CRCS uses the acitve channel for data transmission until the amount of data packets reaches a threshold, at which point it selects one of the available channels other than the active channel. Although CRCS is a very simple channel selection algorithm, by using network simulator we find that CRCS is effective to increase the capacity of ad hoc networks and to keep the load balance of all channels compared to the other channel selection algorithms.

With the rapid development of Internet of things (IoT), Radio Frequency Identification (RFID) has become one of the most significant information technologies in the 21st century. However, more and more privacy threats and security flaws have been emerging in various vital RFID systems. Traditional RFID systems only focus attention on foundational implementation, which lacks privacy protection and effective identity authentication. To solve the privacy protection problem this paper proposes a privacy protection method with a Privacy Enhancement Model for RFID (PEM4RFID). PEM4RFID utilizes a “2+2” identity authentication mechanism, which includes a Two-Factor Authentication Protocol (TFAP) based on “two-way authentication”. Our TFAP employs “hardware information + AES-ECC encryption”, while the ”“two-way authentication” is based on improved Combined Public Key (CPK). Case study shows that our proposed PEM4RFID has characteristics of untraceability and nonrepeatability of instructions, which realizes a good trade-off between privacy and security in RFID systems.

This paper presents a novel topic modeling (TM) approach for discovering meaningful topics for Web APIs, which is a potential dimensionality reduction way for efficient and effective classification, retrieval, organization, and management of numerous APIs. We exploit the possibility of conducting TM on multi-labeled APIs by combining a supervised TM (known as Labeled LDA) with ontology. Experiments conducting on real-world API data set show that the proposed method outperforms standard Labeled LDA with an average gain of 7.0% in measuring quality of the generated topics. In addition, we also evaluate the similarity matching between topics generated by our method and standard Labeled LDA, which demonstrates the significance of incorporating ontology.