• 期刊
• OpenAccess

Steiner Distance in Join, Corona, Cluster, and Threshold Graphs

For a connected graph G and a subset S of its vertices, the Steiner tree problem consists of finding a minimum-size connected subgraph containing S. The Steiner distance of S is the size of a Steiner tree for S, and the Steiner k-diameter of G is the maximum value of the Steiner distance over all vertex subsets S of cardinality k. Calculation of Steiner trees and Steiner distance is known to be NP-hard in general, so applications may benefit from using graphs where the Steiner distance and structure of Steiner trees are known. In this paper, we investigate the Steiner distance and Steiner k-diameter of the join, corona, and cluster of connected graphs, as well as threshold graphs.

• 期刊
• OpenAccess

A Cooperative Game Theoretic Approach for Congestion Management in 6LoWPAN

The internet of (IoT) is things a computing idea that describes the concept of everyday physical objects being connected to the web and having the ability to spot themselves to alternative devices. The Internet Engineering Task Force (IETF) has developed a set of IPv6-based protocols to overcome the challenges of connecting resource-limited wireless sensor nodes to the Internet. In that 6LoWPAN networks, due to the scalable number of devices, heavy network traffic causes congestion which significantly influences the network performance and affects the network QoS parameters. The proposed system of this paper includes the cooperative game theoretic approach for congestion prediction and buffer sharing algorithm (BSA) to reduce congestion in the 6LoWPAN network due to the buffer overflow. Simulation results of the proposed system imply that this outperforms by an average of 52%, 41.71%, 26.19% in terms of throughput, end-to-end latency, and energy consumption respectively as compared to existing GTCCF (game theory based congestion control framework) algorithm.

• 期刊
• OpenAccess

SRTM: A Sparse RNN-Topic Model for Discovering Bursty Topics in Big Data of Social Networks

Social networks such as Twitter, Facebook, and Sina microblog have become major sources for generating big data and bursty topics. As bursty topics discovery is helpful to guide public opinion and control network rumors, it is necessary to design an effective method to detect the quickly-updated bursty topics. However, bursty topics discovery is challenging. This main reason is that big data is both high dimensional and sparse in social networks. In this study, we propose a Sparse RNN-Topic Model (SRTM) named SRTM, to deal with the task. First, we leverage RNN to learn the inside relationship between words and IDF to measuring high-frequency words. Second, the model distinguishes modeling between the bursty topic and the common topic to detect the variety of word in time. Third, we introduce ＂Spike and Slab＂ prior to decouple the sparsity and smoothness of the topic distribution. The burstiness of word pair is leveraged to achieve automatic bursty topics discovery. Finally, to verify the effectiveness of the proposed SRTM method, we collect Sina microblog dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that our proposed SRTM method outperforms favorably against several state-of-the-art methods.

• 期刊
• OpenAccess

Priority-based Channel Scheduling and Route Discovery for IWSN Applications

Accomplishing a reliable transmission of data takes a vital role in Industrial Wireless Sensor Networks (IWSN) so as to overcome the practical messes in monitoring the industrial equipment deployed in a real-time scenario. The parameters sensed from each machine get transferred over to the controlling device through a multi-hop fashion in a networking environment assisted by a ZigBee standard. Taking concern over the information transmission, the PAN coordinator deployment scores a key impact in realizing a full-fledged proficient IWSN. PAN holds the responsibility of stipulating channels for communicating sensed information from sensing devices to the controlling authority. Allocating channel with proficiency certainly, mitigates the energy exhausted for transmission. In a case of acquiring a poor channel for transmitting the sensed information, then congestion occurs that consequently leads to a congested state of a channel. The traffic accustomed out of sensing some sorts of critical criterion should get transmitted with an utmost preference amidst of regular traffic. At this juncture, the latency for transmitting other data packets significantly surges and hence, the energy disbursed with respect to waiting also increases. Many techniques prevailing at presents such as IEEE 802.15.4 and data gathering approach that involves in relying upon sensed information through a multi-fold relaying scheme does not cater a sufficient channel slot without any congestion and utilizing those freed up channels for other highly prioritized sensing devices to transmit information. In order to get rid of these issues, this paper presents an effectual Priority based Scheduling with a Slot based Route Discovery (PCS-SRD) methodology. The procedure of route discovery gets accomplished on the basis of pre-defined slots and hence, the concept of energy conservation gets fulfilled. Likewise, effectiveness in transmitting information with an extreme urgency traverses the network deprived of subjecting towards any congestion through a high prioritized channel. Hence, the proficiency of the devised mechanism gets compared with those prevailing methodologies in terms of reliability, average delay, and throughput and power consumption.

• 期刊
• OpenAccess

RCUHP-SM: A Rule Generation and Clustering based Uncovering Hidden Patterns in Social Media

In recent days, uncovering the hidden patterns from social media is an important and essential task. For this purpose, some of pattern mining techniques are proposed in the traditional works. But, it has some drawbacks include vagueness of termination criteria, lack of interpretability, may extract the meaningless patterns and cannot adapt any constraints within the time interval. In order to overcome these issues, this paper proposed a Rule Generation and Clustering based Uncovering Hidden Patterns in Social Media (RCUHP-SM) technique to uncover the hidden patterns. The main aim of this technique is to analyze, observe and understand the human behavior. At first, the customer review dataset is given as the input and it will be preprocessed by eliminating the irrelevant and unwanted attributes. After that, the descriptive sentences are extracted from the preprocessed data and its score is calculated by counting the tagged words. It is based on the positive, negative and neutral reviews of the user of each product. Then, a set of rules from R1 to R27 is framed to predict the category of review. Consequently, the threshold value is calculated to create the cluster groups into least similar, moderately similar and most similar. Then, it will be labeled as C1 to C6 based on its category. In the analysis phase, the features are extracted from the product description and it's corresponding score is computed. Based on the score, the features are sorted and analyzed for the recommendation. In this work, the novelty is presented in rule generation, similarity computation, threshold based cluster formation and analysis stages. In experiments, the performance of the proposed uncovering hidden pattern system is evaluated and compared in terms of Mean Absolute Precision (MAP), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) measures.

• 期刊
• OpenAccess

A Secure Link Aware Fault Detection Algorithm for Enabling a Reliable Communication in MANET

Providing a secure communication in Mobile Ad-hoc Network (MANET) is one of the demanding and critical task in recent days, due to its dynamic nature. So, the traditional works focused to develop a secure routing protocols for detecting the harmful attacks in network. But, it failed to ensure the fault free link during communication, which affects the entire performance of the network. Also, it follows a single stage attack detection process, in which the attacks are detected at before routing or during data communication. So, the detection accuracy of the existing techniques is not highly efficient. Thus, this work motives to introduce a new routing mechanism, namely, Secure Link Aware Fault Detection (SLFD) for enabling a secure and fault free data communication in MANET. At first, the neighbor discovery and route discovery processes are performed by sending the HELLO packets and RREQ to the neighboring nodes that are in the range of ＜ 200m. After that, the attack detection process is performed by analyzing the behavior of the malicious nodes. In this environment, two harmful attacks such as black hole and gray hole are detected and blocked before communication. Then, the route between the trusted nodes are enabled by analyzing the link paths with the use Genetic Algorithm (GA). Furthermore, the data is transmitted by generating the bogus key and validating the authenticity of the nodes. During this process, the Jitter is also estimated for identifying the compromised nodes in the route. During simulation, the effectiveness of the proposed system is analyzed and validated by using different performance measures. Also, the superiority of the SLFD is proved by comparing it with the existing approaches.

• 期刊
• OpenAccess

A Privacy-Preserving Approach in Friendly-Correlations of Graph Based on Edge-Differential Privacy

It is a challenging problem to preserve friendly-correlations between individuals when publishing social network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the friendly-correlations of the graph are associated with probabilities. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper, we introduced a general model for constructing uncertain graphs. Then, we proposed an Uncertain Graph based on Differential Privacy algorithm (UGDP algorithm) under the general model which provides a rigorous privacy guarantee against powerful adversaries, and we define a new metric to measure privacy for different algorithms. Finally, we evaluate some uncertain algorithms in privacy and utility, the result shows that UGDP algorithm satisfies edge-differential privacy and the data utility is acceptable. The conclusions are that the UGDP algorithm has better privacy preserving than the (k,ε)-obfuscation algorithm, and better data utility than the RandWalk algorithm.

• 期刊
• OpenAccess

Multi-Scan Architecture with Scan Chain Disabling Technique for Capture Power Reduction

High test power dissipation can severely affect the chip yield and hence the final cost of the product. This makes it of utmost important to develop low power scan test methodologies. In this work we have proposed a capture power minimization method to disable those scan chains, needless for the target fault detection, during the capture cycle for multi-scan testing. This method combines the scan chain clustering algorithm with the scan chain disabling technique to disable partial scan chains during the capture cycles while keeping the fault coverage unchanged. This method does not induce the capture violation problem nor does it increase the routing overhead. Experimental results for the large ISCAS'89 benchmark circuits show that this method can reduce the capture power by 43.97% averagely.

• 期刊
• OpenAccess

Trajectory-Based 3D Convolutional Descriptors for Human Action Recognition

This article presents a new method for video representation, called trajectory based 3D convolutional descriptor (TCD), which incorporates the advantages of both deep learned features and hand-crafted features. We utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory constrained pooling to aggregate these convolutional features into effective descriptors. Firstly, valid trajectories are generated by tracking the interest points within co-motion super-pixels. Secondly, we utilize the 3D ConvNet (C3D) to capture both motion and appearance information in the form of convolutional feature maps. Finally, feature maps are transformed by using two normalization methods, namely channel normalization and spatiotemporal normalization. Trajectory constrained sampling and pooling are used to aggregate deep learned features into descriptors. The proposed (TCD) contains high discriminative capacity compared with hand-crafted features and is able to boost the recognition performance. Experimental results on benchmark datasets demonstrate that our pipeline obtains superior performance over conventional algorithms in terms of both efficiency and accuracy.

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• OpenAccess

A Homophone-based Chinese Text Steganography Scheme for Chatting Applications

Text messages can be used as the cover media for data hiding and a form of camouflage for securing secret messages. After data hiding, embedded secret messages can be correctly recovered by data extraction techniques. This paper presents a novel technique for hiding secret information into Chinese-based text messages used for public chat rooms via the selection of homophones. Using the application of chat rooms, users are allowed to generate and correct typing errors. Plausible variations of homophone selection (typing errors) can be adopted as a codebook for hiding secret data. Experimental results have shown that the proposed approach provides an effective way to embed secret data into chat text messages that is not readily detectable. The study concludes that public chat rooms can give a confidential and secure real-time communication channel using the proposed method.