Evaluation of node's trust value is certainly advantage in mobile Adhoc networks (MANETs) where the applications run efficiently by involving trustable nodes only. The proposed method "A Novel QoS Trust computation in MANETs using Fuzzy Petri Nets-QTFPN", evaluates node trust value based on its quality of service (QoS) parameters. Here the MANET is represented as Dynamic Adaptive Fuzzy Petri Nets (DAFPN) model with concurrent reasoning algorithm (CRA). In which delivery of each packet from node to node requires evaluation of certainty factor (μ) using fuzzy expert system. This fuzzy inference system uses QoS parameters as fuzzy input variables namely energy, bandwidth, node mobility and reliability. In the routing process the intermediate node's trust values are evaluated based on certainty factor. The concurrent reasoning algorithm can strengthen the proposed method in selection of quality path to destination and reestablishment of path in case of path breaks. The proposed method performance is analyzed theoretically in terms of time and space complexities. The simulation results are taken against node velocity and network size, where the proposed method outperforms the existing protocols.
The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning (HMM lstm RL). First, the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering. In the second stage, bi-directional long-short-term memory networks are used for key word extraction in the cross-language process. Finally, the proposed HMM lstm RL uses the voting concept in reinforcement learning for the identification and extraction of the keywords. The performance of the proposed HMM lstm RL is 2% better than that of the conventional bi-direction LSTM model.
Mobile ad-hoc network (MANET) is a decentralized and infrastructure less network where a nodes can communicate with other nodes within the access region. Due to mobility node can enter and leave a network at any moment. Due to unstable nature of MANETs, the provision of Quality of Service (QoS) to the applications is a difficult task. In this paper, fuzzy logic enabled QoS multicast routing is proposed. Here energy, bandwidth and link expiry time are considered as a QoS parameters. The existing methods lost their performance in handling multi constrained QoS protocols, since defining the dynamic priorities among the multiple QoS parameters is not a trivial task. In the proposed method "Fuzzy Logic Aware QoS Multicasting in MANETs with Load Balance-FQML", this issue is overcome by using fuzzy logic. The Competency Factor of each intermediate node along the route is calculated by aggregating it's QoS parameters using fuzzy inference system. In the classical multicast protocols, some of the nodes in the multicast tree are overloaded by having multiple branches towards the destination nodes. This leads to partition of the multicast tree and degrades the performance of protocols. In the proposed method, this problem is handled by limiting the number of branches at intermediate nodes. The results are taken in network simulator-ns2, where the proposed method could measure less number of path failures and improved results than existing methods.
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