Optimizing C-RAN Backhaul Topologies: A Resilience-Oriented Approach Using Graph Invariants

ABSTRACT: At the verge of the launch of the first commercial fifth generation (5G) system, trends in wireless and optical networks are proceeding toward increasingly dense deployments, supporting resilient interconnection for applications that carry higher and higher capacity and tighter latency requirements. These developments put increasing pressure on network backhaul and drive the need for a re-examination of traditional backhaul topologies. Challenges of impending networks cannot be tackled by star and ring approaches due to their lack of intrinsic survivability and resilience properties, respectively. In support of this re-examination, we propose a backhaul topology design method that formulates the topology optimization as a graph optimization problem by capturing both the objective and constraints of optimization in graph invariants. Our graph theoretic approach leverages well studied mathematical techniques to provide a more systematic alternative to traditional approaches to backhaul design. Specifically, herein, we optimize over some known graph invariants, such as maximum node degree, topology diameter, average distance, and edge betweenness, as well as over a new invariant called node Wiener impact, to achieve baseline backhaul topologies that match the needs for resilient future wireless and optical networks

A feasibility study of powerline communication technology for digital inclusion

ABSTRACT In the current national scene, many actions point at projects of digital inclusion and citizenship. In this context, providing access technologies as a requisite for the implementation of these actions is primordial. In this way, many innovative experiences have been presented in the past few years. This paper presents a study on the Powerline Communication -PLC technology; as a proposal for a feasible access network for Brazilian Amazon. First, the characteristics of the PLC technology are studied from an implanted indoor prototype at Federal University of Pará. The measures used in this prototype serve as input for a created model, from which it is intended to study the system more widely, considering factors such as: scalability, reliability and the physical characteristics

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors

Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts

Analytical Analysis and Experimental Validation of a Multi-parameter Mach-Zehnder Fiber Optic Interferometric Sensor

Abstract Here we report a simple analytical technique to model a Mach-Zehnder fiber optic interferometric sensors that allow us to predict and calculate via computer simulations parameters that are not easily obtained experimentally. This model was calibrated and compared with experimental data using a 120 mm sensor for measurements of temperature, refractive index and water level. For instance, we were able to calculate the effects on the cladding effective index caused by the variation of those physical parameters. Moreover, this analysis could further our understanding of such sensors and allow us to make predictions about their use in different applications and even their behavior with different sensing lengths

Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses

This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips’ contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types

A Multilayer Approach for Optical Network Planning

With the increase in demand to Backbone networks, one became fundamental the application of new telecommunication technologies for efficient use of devices and optical links, such as xPSK modulations with dual polarization, dispersion compensating fibers, coherent detection and digital processing signals. Thus, network planning using analytical models have been proposed in the last years for this purpose. In this paper we propose the use of numerical simulations in wavelength-division multiplexing networks planning via a novel iterative method with high-performance processing, which does an analysis of the quality of transmission in transparent networks

The Smart Grid Concept in Oil & Gas Industries by a Field Trial of Data Communication in MV Power Lines

Abstract Nowadays, communication and networking technologies are essential for Oil & Gas industries in order to guarantee reliability, safety and low costs in the producing process. Information access and commands and set-points transmission from/to automation systems are challenging tasks mainly due to distributed production areas and hostile environments. This paper presents a field trial of broadband power line communication over an overhead medium voltage grid as a data transport employing 200 Mb/s BPL modems. Throughput, frequency response, physical speed and latency are some of the investigated network parameters. Experimental results obtained after propagation through 1.63 km of a multipath MV power grid reveal the viability of the BPL technology as an access network for video and automation data transport in onshore Oil & Gas industries

Optimizing C-RAN Backhaul Topologies: A Resilience-Oriented Approach Using Graph Invariants

At the verge of the launch of the first commercial fifth generation (5G) system, trends in wireless and optical networks are proceeding toward increasingly dense deployments, supporting resilient interconnection for applications that carry higher and higher capacity and tighter latency requirements. These developments put increasing pressure on network backhaul and drive the need for a re-examination of traditional backhaul topologies. Challenges of impending networks cannot be tackled by star and ring approaches due to their lack of intrinsic survivability and resilience properties, respectively. In support of this re-examination, we propose a backhaul topology design method that formulates the topology optimization as a graph optimization problem by capturing both the objective and constraints of optimization in graph invariants. Our graph theoretic approach leverages well studied mathematical techniques to provide a more systematic alternative to traditional approaches to backhaul design. Specifically, herein, we optimize over some known graph invariants, such as maximum node degree, topology diameter, average distance, and edge betweenness, as well as over a new invariant called node Wiener impact, to achieve baseline backhaul topologies that match the needs for resilient future wireless and optical networks