LoRaWAN, as a wireless communication standard in Low Power Wide Area Network (LPWAN), provides the support for the development of IoT (Internet of Things). However, limited by the characteristics of incomplete orthogonality among Spreading Factor (SF) and the fact that LoRaWAN does not have a Listen-Before-Transmit (LBT) mechanism, the ALOHA-based transmission scheduling method will trigger serious channel conflicts, which reduces the scalability of LoRa (Long Range Radio) networks greatly. Therefore, in order to improve the scalability of LoRa network, Non-Persistent Carrier Sense Multiple Access (NP-CSMA) mechanism was proposed to replace the medium access control mechanism of ALOHA in LoRaWAN. The time of accessing the channel for each node with the same SF in LoRa network was coordinated by LBT, and multiple SF signals were transmitted in parallel for the transmission between different SFs, thus reducing the interference of same SF and avoiding inter-SF interference in the common channel. To analyze the impact of NP-CSMA on the scalability of LoRa networks, LoRa networks constructed by Lo RaWAN and NP-CSMA were compared by theoretical analysis and NS3 simulation. Experimental results show that NP-CSMA has 58.09% higher theoretical Packet Delivery Rate (PDR) performance than LoRaWAN under the same conditions, at a network communication load rate of 1. In terms of channel utilization, NP-CSMA increases the saturated channel utilization by 214.9% and accommodates 60.0% more nodes compared to LoRaWAN. In addition, the average latency of NP-CSMA is also shorter than that of the confirmed LoRaWAN at a network traffic load rate of less than 1.7, and the additional energy consumption to maintain the CAD (Channel Activity Detection) mode is 1.0 mJ to 1.3 mJ and 2.5 mJ to 5.1 mJ lower than the additional energy consumption required by LoRaWAN to receive confirmation messages from the gateway when spreading factor is 7 and 10. The above fully reflects that NP-CSMA can improve LoRa network scalability effectively.
In the era of big data, research in topic evolution is mostly based on the classical probability topic model, the premise of word bag hypothesis leads to the lack of semantic in topic and the retrospective process in analyzing evolution. An online incremental feature ontology based topic evolution algorithm was proposed to tackle these problems. First of all, feature ontology was built based on word co-occurrence and general WordNet ontology base, with which the topic in text stream was modeled. Secondly, a text stream topic matrix construction algorithm was put forward to realize online incremental topic evolution analysis. Finally, a text topic ontology evolution diagram construction algorithm was put forward based on the text steam topic matrix, and topic similarity was computed using sub-graph similarity calculation, thus the evolution of topics in text stream was obtained with time scale. Experiments on scientific literature showed that the proposed algorithm reduced time complexity to O(nK+N), which outperformed classical probability topic evolution model, and performed no worse than sliding-window based Latent Dirichlet Allocation (LDA). With ontology introduced, as well as the semantic relations, the proposed algorithm can demonstrate the semantic feature of topics in graphics, based on which the topic evolution diagram is built incrementally, thus has more advantages in semantic explanatory and topic visualization.
To solve the challenge of accurate user group discovering, a user topic discovering algorithm based on trust chain, which was composed by three steps, i.e., topic space discovering, group core user discovering and topic group discovering, was proposed. Firstly, the related definitions of the proposed algorithm were given formally. Secondly, the topic space was discovered through the topic-correlation calculation method and a user interest calculation method for topic space was addressed. Further, the trust chain model, which was composed by atomic, serial, and parallel trust chains, and its trust computation method of topic space were presented. Finally, the detail algorithms of topic group discovering, including topic space discovering algorithm, core user discovering algorithm and topic group discovering algorithm, were proposed. The experimental results show that the average accuracy of the proposed algorithm is 4.1% and 11.3% higher than that of the traditional interest-based and edge density-based group discovering methods. The presented algorithm can improve the accuracy of user group organizing effectively, and it will have good application value for user identifying and classifying in social network.
In order to solve the problem of being sensitive to external interference such as defects and occlusions in the existing discriminant analysis, a Sparsity reconstruction-based Discriminant Analysis (SDA) for dimensionality reduction was proposed in the term of local sparse representation. The algorithm firstly made use of sparse representation to complete local sparsity reconstruction in each class, and then completed between-class sparsity reconstruction with the average of each different class. Finally the algorithm preserved the ratio between the between-class sparsity reconstruction information and the within-class sparsity reconstruction information in the process of dimensionality reduction. The algorithm promotes the computational efficiency of sparse representation and the robust performance of discriminant analysis. The experimental results on AR and UMIST face datasets show, compared with Graph-based Fisher Analysis (GbFA) algorithm and Reconstructive-based Discriminant Analysis (RDA) algorithm, the proposed algorithm promotes 2-10 percent in the highest recognition accuracy based on nearest neighbor classification.
At present, the safety analysis of SNAKE algorithm is mainly about interpolation attack and impossible differential attack. The paper evaluated the security of SNAKE(2) block cipher against integral attack. Based on the idea of higher-order integral attack, an 8-round distinguisher was designed. Using the distinguisher, integral attacks were made on 9/10 round SNAKE(2) block cipher. The attack results show that the 10-round SNAKE(2) block cipher is not immune to integral attack.