Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application AI techniques for improving performance optical communication networks. The use AI-based first studied in applications related transmission, ranging from characterization operation network components monitoring, mitigation...

Utilizing the dormant path diversity through multipath routing in Internet to reach end users-thereby fulfilling their QoS requirements-is rather logical. While offering better resource utilization, reliability, and often even much quality of experience (QoE), provisioning was shown help network data center operators achieve traffic engineering form load balancing. In this survey, we first highlight benefits basic components. We take a top-down approach review various protocols, from...

Abstract A numerical study is conducted to investigate mixed convective cooling of a two-dimensional rectangular cavity with differentially heated side walls. The horizontal walls are assumed be adiabatic. Cold fluid blown into the from an inlet in wall and exited through outlet opposite wall. This configuration heat transfer has application building energy systems, electronic circuit boards, solar collectors, among others. objective research optimize relative locations order have most...

Traffic prediction and utilization of past information are essential requirements for intelligent efficient management resources, especially in optical data center networks (ODCNs), which serve diverse applications. In this paper, we consider the problem traffic aggregation ODCNs by leveraging predictable or exact knowledge application-specific requirements, such as holding time, bandwidth, history, latency. As flows (e.g., long/ elephant short/mice), utilize machine learning (ML)...

We propose Quantum Wrapper as a novel networking technology that enables simultaneous control, management, and operation of quantum networks coexist with classical networks. The wrapper enable the transparent interoperable transportation datagrams consisting payloads and, notably, headers, to facilitate datagram switching without measuring or disturbing qubits payload. Furthermore, can utilize common network control management for performance monitoring on header, infer channel quality.

Elastic optical networks are prone to spectrum fragmentation, resulting in poor resource utilization and often higher blocking probability. To overcome the a defragmentation (DF) of can be applied by reconfiguring some or all active connections. However, reconfiguration is generally not desirable, as it interrupt services existing In this paper, we propose two novel connection schemes efficiently address DF: (i) reactive–disruptive scheme (ii) proactive–non-disruptive scheme. Both utilize...

The automatic identification system (AIS) reports vessels' static and dynamic information, which are essential for maritime traffic situation awareness. However, AIS transponders can be switched off to hide suspicious activities, such as illegal fishing, or piracy. Therefore, this paper uses real world data analyze the possibility of successful detection various anomalies in domain. We propose a multi-class artificial neural network (ANN)-based anomaly framework classify intentional...

Understanding and representing traffic patterns are key to detecting anomalous trajectories in the transportation domain. However, some can exhibit heterogeneous maneuvering characteristics despite confining normal patterns. Thus, we propose a novel graph-based trajectory representation association scheme for extraction confederation of movement patterns, such that data uncertainty be learned by deep learning (DL) models. This paper proposes usage recurrent neural network (RNN)-based...

In a smart grid, estimating the power requirements of various regions and detecting malicious practices is very crucial. Advanced Metering Infrastructure (AMI) key component grid system that uses meters. Due to vulnerability meter against cyber attacks, strong defense algorithm needed. this paper, CNN-LSTM based deep learning methodology proposed for consumption forecasting anomaly detection using combination Convolutional layers Stacked Long Short Term Memory (LSTM) architecture. The...

Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of in optical networks. We evaluate various ML classifiers detecting out-of-band with varying intensities. Numerical results show that artificial neural network is the fastest (106 per second) inference most accurate (≈ 100%) as well identifying channels attacked. also discuss study novel mechanism when system under active...

Generally, an elastic optical network (EON) under a dynamic connection (non-uniform bandwidth) set up and termination scenario leads to spectrum fragmentation, results in higher blocking probability. To overcome this problem, defragmentation of the can be applied by reconfiguring some or all connections network. However, reconfiguration interrupt services existing if it is not performed non-disruptive manner, meaning that no data lost interrupted during reconfigurations. In paper, we propose...

The surge in the proliferation of private vehicles within urban centers has exacerbated challenges parking management, primarily due to a scarcity available spaces. To address this issue, implementation intelligent systems featuring autonomous monitoring and guidance become imperative. This paper proposes manual model ResNet-50 other classification networks using Global Perceptual Feature Extractor (GPFE) module. Evaluated on established datasets, namely PKLot CNREXT, GPFE module...

Data randomization or scrambling has been effectively used in various applications to improve the data security. In this paper, we use idea of proactively randomize spectrum (re)allocation connections' As it is well-known that random fragments and thus increases blocking elastic optical networks, analyze tradeoff between system performance To end, addition randomization, utilize an on-demand defragmentation scheme every time a request blocked due fragmentation. We model occupancy pattern...

In this paper, we investigate an animal-human cohabitation problem with the help of machine learning and fiber-wireless (FiWi) access networks integrating cloud edge (fog) computing. We propose early warning system which detects wild animals near road/rail wireless sensor alerts passing vehicles possible animal crossing. Additionally, show that animals’ detection at earliest related processing, if possible, sensors would reduce energy consumption devices end-to-end delay in notifying...

Scrambling or information randomization has been effectively used in various applications to prevent the falling into hands of rogue attackers. In this paper, we use idea spectrum scrambling proactively randomize across multiple fiber cores improve connections' security, while at same time defragmenting blocking performance. To end, propose a scheme called random defragmentation (RSD), and model occupancy pattern multi-core link using multi-class continuous-time Markov chain (CTMC) under two...

We experimentally demonstrate quantum channel monitoring by wavelength-time multiplexing of classical wrapper bits with payloads. Bit-error-rate measurements 5 Gb/s infer the coincidence-to-accidental ratio up to 13.3 dB.

The forecasting of the petroleum products consumption is very important as industry directly affects transport sector any country and so decision making regarding import/export these based on prediction extremely crucial. With increasing ease implementation deep learning techniques, various time series methods have been developed. However, performance most highly dependent availability large amount data. In this paper, we propose a methodology that use small datasets containing only past...

Flexible A.C. transmission systems (FACTS) are being used more in large power for their significance manipulating line flows. Traditional state estimation methods without integrating FACTS devices will not be suitable embedded with FACTS. In this paper the of presence is presented. Hopfield neural network simulated as an optimization tool to solve system problem

Driven by technological advances, there is a increase in electricity-based equipments and this leads to excessive energy consumption (EC) demand for power every day. To enhance management collaboration between electricity used building the smart grid, EC must be predicted. Forecasting techniques prediction of accurately are limited due challenges like dynamic behaviour residents climatic condition. So, conquer such we proposed deep learning based methodology. The methodology uses hybrid...