International audienceEfficient reconfiguration of optical multicast trees in wavelength division multiplexing (WDM) networks is required. Multimedia applications which consume a huge bandwidth, require multicasting. So, multicast concept is extended to optical networks to improve performance. Today, networks are facing many phenomena such as changes in the traffic model, failures, additions or deletions of some network resources due to a maintenance operation. To cope with these phenomena, network operators compute new topology according to the applications requirements. Some real-time multicast applications are not indulgent with lightpath interruptions. So the configuration of the network must be done as quickly as possible to be spontaneously deal with the problem before other events appear and without connection interruption. To the best of our knowledge, there is no work in the literature that considers the reconfiguration of an optical multicast tree to another one without connection interruption. We prove that it is impossible to reconfigure any initial tree into any final tree using only one wavelength and without connection interruption. We propose in this paper BpBAR_2 method, using several wavelengths to reconfigure optical WDM network. This algorithm does tree reconfiguration without lightpath interruption, reduce the reconfiguration setup time and the cost of wavelengths used
Objectives: This study aims to present a framework for Automatic Modulation recognition using Deep learning without feature extraction. Methods: We study seven modulations using the In-Phase Quadrature constellation polluted by Additive White Gaussian Noise. We apply the K-means algorithm to normalize data transmitted and polluted by noise; the new diagram obtained is considered as an image and coded in pixel before entering in a Deep Neural Network where we apply 20% dropout on hidden layers to avoid overfitting. The simulation is carried out in Matlab. Findings: Experiment performed on selected modulations following the proposed framework gives a good percentage of recognition equal to 96.12%. Our algorithm Deep Neural Network imaGe gives the best performance results at epoch equal to 2,000,000. Applications: The outcome will be beneficial for researchers in Software-Defined Radio for civilian and military applications like electronic attacks and electronic protection.
Abstract-In this article, we present a chatbot model that can automatically respond to learners' concerns on an online training platform. The proposed chatbot model is based on an adaptation of the similarity of Dice to understand the concerns of learners. The first phase of this approach allows selecting the preestablished concerns that the teacher has in a knowledge base which are closest to those posed by the learner. The second phase consists of selecting among these k most appropriate concerns based on a measure of similarity built on the concept of domain keywords. The experimentation of the prototype of this chatbot makes it possible to find the adequate answers. In the case, where the question refers to a question from the teacher, the learner is asked if the question identified is the one he was referring to. If he answers in the affirmative, the instructions associated with his request are sent to him. If not, the learner's concern is sent to the human tutor. The hybridization of this chatbot with the human agent comes to enrich the initial knowledge base of the chatbot. The results obtained with the concept based on the keywords of the domain are encouraging. The learner's comprehension rate is above 50% when applying the concept of domain keywords while the measure of Dice is below 50%.
Automatic Modulation Classification (AMC) with intelligent system is an attracting area of research due to the development of SDR (Software Defined Radio). This paper proposes a new algorithm based on a combination of k-means clustering and Artificial Neural Network (ANN). We use constellation diagram I-Q (In phase, Quadrature) as basic information. K-means algorithm is used to normalize data transmitted and pollute by the Additive White Gaussian Noise (AWGN), then the new diagram obtained is considered as an image and coded in pixel before entering in MLP (Multi-Layer Perceptron) Neural Network. Simulation results show an improvement of recognition rate under low SNR (Signal Noise Rate) compare to some results obtained in the literature.
Abstract-Detecting redundant nodes and scheduling their activity is mandatory to prolong the lifetime of a densely-deployed wireless sensor network. Provided that the redundancy check and the scheduling phases both help to preserve the coverage ratio and guarantee energy efficiency. However, most of the solutions usually proposed in the literature, tend to allocate a large number of unnecessary neighbor (re)discovery time slots in the dutycycle of the active nodes. Such a shortcoming is detrimental to battery power conservation. In this paper, we propose a crossing points-based heuristic to fast detect redundant nodes even in heterogeneous networks; then, an integer linear program and a local exclusion based strategy to respectively, formulate and solve the sensing unit scheduling problem. Simulations show that the resulting localized asynchronous protocol outperforms some state-of-the-art solutions with respect to coverage preservation and network lifetime enhancement.
Despite progress made in recent years, cluster formation delay, load balancing, energy holes remain challenging for cluster-based topology control protocols. When, for energy efficiency purpose, one tries to address these problems simultaneously, one is confronted with latency, message overhead, and topological defects such as isolated Cluster Heads (CH), pairs of adjacent CHs etc. These unexpected outcomes are detrimental to both network capacity and lifetime. In this paper, we propose a fast cluster-based selforganization protocol that reduces time and energy wastes during cluster formation, minimizes the scope and frequency of cluster maintenance process, and mitigates energy holes in the sink's two-hop neighborhood. Simulation results show that our contribution is able to quickly construct a good quality communication topology while enhancing network lifetime.
Connectivity construction is the main phase of a communicationoriented topology control process. It consists of improving the current network physical topology while preserving important properties such as connectivity and symmetry. In this paper, we address the problem of combining two of the techniques commonly used for this purpose in networks composed of a large number of energy constrained wireless sensor-nodes namely, clustering and power control. We propose an ant colony-based asynchronous and localized protocol that helps to significantly reduce energy losses by simultaneously eliminating redundant and poor quality links, always keeping the Cluster Head-to-member distance up to khops (k≥1) and minimizing signalization. Simulation results show that our protocol outperforms some state-of-the-art solutions in terms of Quality of the Topology (QoT) and network lifetime prolongation.
Mobility and technology have become inseparable terms in the sense that users want to stay connected and continue to use their services while moving. The various technologies that manage mobility face fundamental challenges such as optimizing intra-domain and inter-domain routes.With the emergence of Software-Defined Networking (SDN), a promising technology that separates the control plane from the data plane in network devices, mobility management could benefit from its advantages and thus address the above challenges. In this paper, we propose a new method of communication during the movement of a mobile node between two different domains based on the operation of the PMIPv6 protocol in a software defined networking (SDN) architecture and the involvement of edge nodes to anticipate the new destination of the mobile node. The proposed method is inspired by proactive and reactive inter-domain handovers and allows the controller to anticipate inter-domain communication with its peers and simultaneously reduce the handover time in the visited domain and the data transfer delay. The experimental results confirmed the effectiveness of the proposed method.
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