Survivability is the major problem in Wavelength Division Multiplexed (WDM) Optical network. Also, the selection of grooming node is another important issue in the Optical Mesh Network. Most of the work in the field of the optical network is focused of sparse traffic grooming and survivability as a separate issue. We here presented a novel approach for providing survivability to sparse traffic grooming. This survivability on sparse traffic grooming is considered for the failure of a link on sparse grooming network. When we have sparse grooming network and a link failure occurs then we are providing a solution of shared link using combiner queues. We have proved that the combiner queues help to reduce the blocking probability and also reduce the number of call dropping. We have presented the queuing model which helps to calculate the total requests at a node or in the entire network. The presented combiner queuing model is useful for the calculation of probabilities, as m server queuing system.
Mostly survivability in optical Mesh network is major topic of discussion as failure in the high speed network will affect the services. Wavelength Division Multiplexing (WDM) technology has made it possible to use the large capacity of an optical fiber. In a WDM Network, survivability is a major area of concern as a WDM network carries a large amount of data. The failure of a network element could result in a large amount of data loss. The goal of survivability is to maintain connectivity and services after a failure. The proposed method is based on Survivable Traffic Grooming to address the gap between bandwidth capacity of wavelength and bandwidth requirement of a connection and also to provide fault tolerance. This method focuses on a sparse grooming network, where only a few network nodes have the traffic grooming capability. The objective is to provide a fault tolerant network while minimizing the network cost and maximize the network throughput as reducing the utilized number of wavelengths. We have compared the result of our system with Multicast traffic grooming with survivability in WDM mesh networks.
Wireless sensor networks (WMSNs) are becoming increasingly popular in many fields, from academia to transportation, environmental monitoring, wildlife preservation, and military espionage. Therefore, examining potential threats, power consumption, vulnerability recognition, and systemic vulnerability characteristics is essential to develop a reliable information security approach for WSNs. As a result, it is becoming increasingly crucial for the technical community to conduct intrusion recognition method evaluations. Since this is the case, using deep learning techniques in creating intrusion identification and mitigation systems for wireless multimedia sensor networks is essential. This article examines how well different machine learning and deep learning algorithms perform in attack identification systems. Testing the efficacy of different methods on the WMSN-DS database through experimentation is essential. In this work, we combine the power of a Convolutional Neural Network classifier with a Random forest. In order to accomplish this, a Convolutional Neural Network with a Random Forest Classifier is used. The intrusion detection system (IDS) is a crucial technique proposed in this study for WMSN. To address this issue, the current study proposal uses deep Learning with a Random Forest classifier to detect and prevent attacks and to promote efficient forwarding in WMSNs. Multiple WMSN assaults have been investigated, and the results of these investigations have been critically evaluated.
Recent years showed a wide range of applications in Wireless Sensor Networks (WSN). For a WSN, Topology Control is crucial to obtain an energy efficient network without affecting the connectivity and other properties. In this paper the sequence of strategies carried out to obtain a better scheme for a topology control in terms of energy is discussed. An quantitative study is also done on the impact of N-tiers in the performance of the various proposed algorithms. Comparison on one tier , two tier and three tier architecture using the proposed methodology is also made. The results showed the effectiveness of the different approaches proposed. The future works discussed also gives a wider vision on the probabilities of the various schemas for the forthcoming years.
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