For multi-task mobile edge computing(MEC) systems in satellite Internet of Things(IoT), there are dependencies between different tasks, which need to be collected and jointly offloaded. It is crucial to allocate the computing and communication resources reasonably due to the scarcity of satellite communication and computing resources. To address this issue, we propose a joint multi-task offloading and resource allocation scheme in satellite IoT to improve the offloading efficiency. We first construct a novel resource allocation and task scheduling system in which tasks are collected and decided by multiple unmanned aerial vehicles(UAV) based aerial base stations, the edge computing services are provided by satellites. Furthermore, we investigate the multi-task joint computation offloading problem in the framework. Specifically, we model tasks with dependencies as directed acyclic graphs(DAG), then we propose an attention mechanism and proximal policy optimization (A-PPO) collaborative algorithm to learn the best offloading strategy. The simulation results show that the A-PPO algorithm can converge in 25 steps. Furthermore, the A-PPO algorithm reduces cost by at least 8.87 $\%$ by default compared to several baseline algorithms. In summary, this paper provides a new insight for cost optimization of multi-task MEC system in satellite IoT.

A singlemode-multimode-singlemode (SMS) fiber structure consists of a short section of multimode fiber fusion-spliced between two SMS fibers. The mechanism underpinning the operation of an SMS fiber structure is multimode interference and associated self-imaging. SMS structures can be used in a variety of optical fiber systems but are most commonly used as sensors for a variety of parameters, ranging from macro-world measurands such as temperature, strain, vibration, flow rate, RI and humidity to the micro-world with measurands such as proteins, pathogens, DNA, and specific molecules. While traditional SMS structures employ a short section of standard multimode fiber, a large number of structures have been investigated and demonstrated over the last decade involving the replacement of the multimode fiber section with alternatives such as a hollow core fiber or a tapered fiber. The objective of replacing the multimode fiber has most often been to allow sensing of different measurands or to improve sensitivity. In this paper, several different categories of SMS fiber structures, including traditional SMS, modified SMS and tapered SMS fiber structures are discussed with some theoretical underpinning and reviews of a wide variety of sensing examples and recent advances. The paper then summarizes and compares the performances of a variety of sensors which have been published under a number of headings. The paper concludes by considering the challenges faced by SMS based sensing schemes in terms of their deployment in real world applications and discusses possible future developments of SMS fiber sensors.

Optical nonlinearity impairments have been a major obstacle for high-speed, long-haul and large-capacity optical transmission. In this paper, we propose a novel convolutional neural network (CNN)-based perturbative nonlinearity compensation approach in which we reconstruct a feature map with two channels that rely on first-order perturbation theory and build a classifier and a regressor as a nonlinear equalizer. We experimentally demonstrate the CNN equalizer in 375 km 120-Gbit/s dual-polarization 64-quadrature-amplitude modulation (64-QAM) coherent optical communication systems. We studied the influence of the dropout value and nonlinear activation function on the convergence of the CNN equalizer. We measured the bit-error-ratio (BER) performance with different launched optical powers. When the channel size is 11, the optimum BER for the CNN classifier is 0.0012 with 1 dBm, and for the CNN regressor, it is 0.0020 with 0 dBm; the BER can be lower than the 7 $\%$ hard decision-forward threshold of 0.0038 from −3 dBm to 3 dBm. When the channel size is 15, the BERs at −4 dBm, 4 dBm and 5 dBm can be lower than 0.0020. The network complexity is also analyzed in this paper. Compared with perturbative nonlinearity compensation using a fully connected neural network (2392-64-64), we can verify that the time complexity is reduced by about 25 $\%$ , while the space complexity is reduced by about 50 $\%$ .

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.

A dual-channel single-mode-multi-mode-single-mode (SMS) fiber optic sensor encapsulated by polydimethylsiloxane (PDMS) was proposed for the first time, for the simultaneous monitoring of the brachial and radial arteries for accurate blood pressure prediction. With the help of the machine learning algorithm Support Vector Regression (SVR), the SMS fiber sensor can continuously and accurately monitor the systolic and diastolic blood pressure. Commercial sphygmomanometers are used to calibrate the accuracy of blood pressure measurement. Compared with the single-channel system, this system can extract more pulse wave features for blood pressure prediction, such as radial artery transit time (RPTT), brachial artery transit time (BPTT), and the transit time difference between the radial artery and the brachial artery (DBRPTT). The results show that the performance of dual-channel blood pressure monitoring is more accurate than that of single-channel blood pressure monitoring in terms of the absolute value of the correlation coefficient (R) and the average value of the difference between SBP and DBP. In addition, both the single-channel and dual-channel blood pressure monitoring are in line with the Association for the Advancement of Medical Devices (AAMI), but the average deviation (DM, 0.06 mmHg) and standard deviation (SD, 1.54 mmHg) of dual-channel blood pressure monitoring are more accurate. The blood pressure monitoring system has the characteristics of low cost, high sensitivity, non-invasive and capability for remote real time monitoring, which can provide effective solution for intelligent health monitoring in the era of artificial intelligence in the future.

In the era of the fifth-generation fixed network (F5G), optical networks must be developed to support large bandwidth, low latency, high reliability, and intelligent management. Studies have shown that software-defined optical networks (SDON) and artificial intelligence can help improve the performance and management capabilities of optical networks. Inside a large-scale optical network, many types of alarms are reported that indicate network anomalies. Relationships between the alarms are complicated, making it difficult to accurately locate the source of the fault(s). In this work, we propose a knowledge-guided fault localization method, using network alarm knowledge to analyze network abnormalities. Our method introduces knowledge graphs (KGs) into the alarm analysis process. We also propose a reasoning model based on graph neural network (GNN), to perform relational reasoning on alarm KGs and locate the network faults. We develop an ONOS-based SDON platform for experimental verification, which includes a set of processes for the construction and application of alarm KGs. The experimental results show the proposed method has high accuracy and provide motivation for the industry-scale use of KGs for alarm analysis and fault localization.