Search

Smart cities are based on complex systems that use legacy infrastructure and modern and intelligent solutions to achieve a highly efficient functioning of society in several verticals. In the context of road intersections, the main objective is to offer safe, efficient, and reliable mobility services for all agents involved in the physical space (infrastructure, vehicles, and pedestrians). The digital twin paradigm has recently been considered by both academia and industry as a means for reaching intelligent transportation solutions. The article makes a case for digital twins in intelligent intersection modeling as it is expected to consolidate real-time monitoring, control, and management of intelligent intersections. The design of a framework and the development of a testbed are presented in this paper. New functionalities for the safe operation of the road intersection are the expected results of this proposal.

An Open Source routing platform that uses a disaggregated model fully populated with carrier-class implementation of network protocols is discussed here. FreeRouter's architecture allows portability from emulated to testbed experiments (and on different hardware targets) is briefly explained. In order to demonstrate its ease of use, installation and entry-level examples are provided.

Computing performance and hardware integrity are tightly linked to adequate thermal management in datacenters. In addition, a large share of its electricity bills comes from cooling systems, so there is a pressing need for enhanced thermal management systems. This paper proposes a fiber Bragg grating (FBG) quasi-distributed sensing device as an alternative to electronic sensors. The main focus is to demonstrate that a FBG-based solution allows thermal measurement at critical non-monitored regions (blind spots) within a server in an acquisition rate of 1 Hz, with thermal sensitivity of 8.757 pm/°C, a thermal resolution smaller than 1 °C and spatial resolution and multiplexing needed to monitoring diverse blind spots where there is virtually no room for adapting conventional sensors. In addition, we also monitor spots where there are already internal sensors next to them (referential spots), such as central processing unit (CPU) and graphics processing unit (GPU). As a result, a simple FBG sensor calibration technique is proposed using those referential spots. The technique proposed can be scaled out to monitor a large number of elements across large areas in a datacenter environment, since quasi-distributed FBG sensing signal can be transmitted over longer optical fibers than those employed in optical distributed temperature systems (DTS).

Motivated by its high toxicity and presence on many industrial processes, in this work, a hydrogen sulphide (H2S) gas detector was developed. The construction of the device was accomplished by taking advantage of both silica optical fiber and the potentialities of gold nanoparticles, where the physics for the plasmon resonance on localized surface are exploited. The localized surface plasmon resonance occurs in gold nanoparticles and has strong influence when the free electrons of gold suffer external perturbation. In this case, strong bonding energy between the gold element and sulfur was the main basis for the operation principle of the developed colorimetric detector. In the process of nanoparticles adhesion, polyvinyl alcohol was used on the interface between the fiber and the nanoparticles. The developed detector has the ability of detecting H2S levels in the range of 0.4 to 2.0 ppm, at room temperature. Different experiments were carried out to verify the behavior of the nanoparticle concentration at the detection limit of the gas. Thus, the proposed detector has many applications in different industrial processes and environments considering the numerous accidents caused by H2S inhalation with fatal victims already registered around the world.

In this paper, we take the position of developers that need to deploy emerging programmable protocols (or services) with specific network requirements and want to know how to benefit from an open router platform and testbed infrastructure. Our focus is to exploit the PolKA lifecycle experience as a use case to figure out a balance between integrating and reusing legacy protocols with new protocols.

Physical topologies are evolving from elementary survivable rings into complex mesh networks. Nevertheless, no topology model is known to provide an economic, systematic, and flexible interconnection paradigm for ensuring that those meshes bear resilience features. This paper argues that intrinsic resilience can be brought by twin graph topologies, as they satisfy equal length disjoint path property with minimal number of physical links. Also, they benefit from property preserving recursive methods to graciously scale up/down and merge/split topologies. An exhaustive investigation is performed across twin graph families composing networks from 4 to 17 nodes, whereas diverse real-world topologies and ring networks are used as benchmarks. First, we illustrate the growing and the merging processes, and discuss the topology diversity of twin graphs. We analyze the impact of single cable cuts between neighbouring nodes, then we stress topologies with 2, 3, and 4 simultaneous cable cuts. Improved resiliency is seen for neighbor nodes and also reduction of cut sets able to disconnect the twin topologies in comparison with real-world networks. At transport layer, we derive and validate an upper bound for additional capacity required to implement $$1+1$$ path dedicated protection. As networks grow larger, this protection cost is consistently reduced compared to benchmark topologies. We also test the suitability of our approach at optical layer regarding transponders consumption. Finally, we present as a use case the redesign of CESNET into a resilient network.

The interest in applying gold nanoparticles is principally based on their monodispersity. Therefore, the synthesis of monodisperse gold nanocrystals has a key role in designing nanomaterials for their specific application. However, it can represent a cutting edge for many laboratories, mainly because it requires a variety of complex techniques for properly characterizing metal nanoparticles with expensive and rugged equipment. Herein, we propose an approach to synthesize nanoparticles with controlled monodispersity that can be adjusted through the factorial experimental plan 32 using response surface modeling (RSM). The particles were characterized on the basis of visual observation, UV–visible spectroscopy, transmission electron microscopy, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM/EDS) and statistics. Our findings illustrate how factorial design is important to providing accurate projections of nanoparticle production using sodium citrate and borohydride as reducers. The proposed method leverages facile and inexpensive routes to synthesize monodisperse gold nanocrystals without the assistance of time-consuming image-based characterization tools. Keywords: Gold nanoparticles, Growth steps, Experimental design, Response surface methodology

One great challenge of modern networks is how to select paths and react to highly variable and demanding traffic patterns. In recent years, emerging networking languages and architectures, such as P4/PISA, have created unprecedented opportunities for rapidly prototyping disruptive solutions in programmable data planes. In this direction, source routing (SR) is a prominent alternative to conventional table-based routing, since it reduces the convergence time of building distributed routing tables. In particular, PolKA explores the polynomial residue number system (RNS) for a fully stateless SR, i.e., no state update on packet headers needs to be conveyed along the route. This paper reports the deployment of PolKA in a continental testbed composed of P4 programmable switches. Results for end-to-end throughput and forwarding latency show that PolKA matches the data plane performance of traditional approaches, while paving the way for further innovative applications by exploring RNS properties in dynamic scenarios.

Fiber optic-based liquid-level sensors (FOLLSs) can work in harsh environments with inherent advantageous features that only optical fiber offers, such as intrinsic safety, resistance to chemical corrosion, immunity to electromagnetic interference, electric isolation, small size, lightweight sensing heads, high accuracy and resolution, easy multiplexing, and capability for extremely remote monitoring without the need of electrical power at the measuring point. In this context, this paper presents three specific optical fiber sensor technologies that show considerable potential for liquid-level monitoring: the Mach–Zehnder and Fabry–Perot interferometers and the fiber Bragg gratings (FBGs). Mathematical models and operation principle are presented for each sensor technology, and parameters, such as level range, level sensitivity, and temperature cross sensitivity, are analyzed. Finally, a critical and comparative analysis among the sensors is performed, where it is reported a tradeoff between sensitivity and level range.

This paper presents the development of a single polymer diaphragm-based fiber Bragg grating (FBG) sensor for simultaneous measurement of pressure and temperature. The FBG response is analyzed through a transient model for heat conduction and the pressure is estimated with the diaphragm and FBG strain, where the frequency difference between temperature and pressure variations were used to decouple both variables. Results show root mean squared error (RMSE) of 1.86 °C for the temperature analysis with constant pressure and 0.92 kPa for the pressure analysis with constant temperature. Experiments with variation of both temperature and pressure were conducted and the errors were higher than the ones with only temperature or pressure variations due to a residual cross-sensitivity. Therefore, the variation of both pressure and sensitivity leads to a RMSE of 3.88 °C and 5.13 kPa for temperature and pressure, respectively, which represent low errors of about 5% for both pressure and temperature.

SummaryDespite recent advancements on cloud-enabled and human-in-the-loop cyber-physical systems, there is still a lack of understanding of how infrastructure-related quality of service (QoS) issues affect user-perceived quality of experience (QoE). This work presents a pilot experiment over a cloud-enabled mobility assistive device providing a guidance service and investigates the relationship between QoS and QoE in such a system. In our pilot experiment, we employed the CloudWalker, a system linking smart walkers and cloud platforms, to physically interact with users. Different QoS conditions were emulated to represent an architecture in which control algorithms are performed remotely. Results point out that users report satisfactory interaction with the system even under unfavorable QoS conditions. We also found statistically significant data linking QoE degradation to poor QoS conditions. We finalize discussing the interplay between QoS requirements, the human-in-the-loop effect, and the perceived QoE in healthcare applications.

Measurement of torque or twist angle and mechanical bending is important in several fields such as structural health monitoring, movement analysis, robotics instrumentation, and mechanical systems in general. Several optical fiber sensors have been already proposed to measure these parameters separately. However, their high-cost interrogation systems are the main drawback for their large commercial application. Distributed Bragg reflector (DBR) allows to overcome this issue since its inherent heterodyne behavior leads to interrogation schemes in the frequency domain instead optical domain reducing significantly the interrogation systems cost. In this context, this paper presents a DBR to measure twist angle up to 180°. The enhancement of twist sensitivity by bending-induced birefringence is analytically and experimentally demonstrated achieving a value of $-$ 2.95 MHz/rad. With a constant bending angle of 20°, a twist sensitivity of $-$ 32.73 MHz/rad is achieved, which represents a enhancing sensitivity by a factor of $\sim$ 7, when compared to some DBR sensor configurations reported in the literature.

The detection and identification of Distributed Denial-of-Service (DDoS) attacks remains a challenge in cloud/edge/fog computing environments. It usually requires network middleboxes, such as deep packet inspectors (DPI), for detection task mostly. But clouds and fogs have native powerful telemetry systems that are not yet fully exploited for DDoS detection; and provide so much information that could aid attack identification tasks as well. Machine Learning (ML) algorithms can help one diving into the richness of cloud’s native data collection services, which have a multitude of metrics from both physical and virtual hosts. This paper evaluates the use of ML algorithms over datasets collected from a experimental testbed based on OpenStack. Controlled attack scenarios were used to investigate the ability of ML for tasks such as detecting and identifying SYN_Flood and GET_Flood DDoS attacks mixed, in different proportions, with legitimate clients. kNN and Random Forest ML algorithms were trained and tested, and for evaluation the metrics accuracy, recall, precision, and F1-score were used. Our experiments presented about 87% of accuracy in the detection of SYN_Flood and GET_Flood DDoS attacks, whereas Snort IDS mostly fails to detect the latter attack by processing the corresponding packet traces. Also, the detection of PING_Flood DDoS attack was tested without training as an initial evaluation towards the generalization of the proposal.

Provedores de rede precisam oferecer uma grande variedade de serviços a seus clientes. A virtualização de funções de rede e redes definidas por software facilitam a implementação desses serviços por meio da virtualização e do encadeamento de funções de rede. Entretanto, há casos em que essas funções encontram-se em \textit{data centers} distantes geograficamente ou em nuvens em domínios diferentes. Por outro lado há poucos trabalhos na literatura que focam nos mecanismos para implementação do encadeamento de funções de rede em múltiplas nuvens. Neste contexto, este trabalho apresenta uma solução para o encadeamento de funções de rede em múltiplas nuvens. O artigo destaca o roteamento na origem, baseado no KeySFC, como facilitador da solução proposta e apresentando diversas vantagens quanto ao gerenciamento dos fluxos e à manutenção dos estados nos dispositivos de rede. Um protótipo é implementado e avaliado, com o objetivo de se realizar uma avaliação funcional da proposta em cenários de encadeamento em múltiplas nuvens.

Edge computing transfers processing power from large remote data centers (DCs) to distributed DCs at the edge of the network. This shift requires the ability to provide network functions virtualization (NFV) solutions that can efficiently manage and combine a large number of dynamic services in a resource-constrained DC. However, the routing mechanisms of traditional data center networks are not adequate for the dynamic composition of these services, because they are complex, rigid, subject to large delays in the propagation of control information, and limited by the size of switches' routing tables. In addition, traditional service function chaining (SFC) solutions in the service overlay are often decoupled from routing decisions in the network underlay, and restrict path selection options by traffic engineering. In this way, the NFV orchestrator cannot explore the full capacity of the network to provide composite services. To tackle these issues, this thesis investigated a programmable, expressive, scalable, and agile SFC proposal that allows dynamic and efficient orchestration of the network infrastructure of edge DCs with commodity network equipment. The proposal exploits virtualization and programmability technologies of DC networks, server-centric DCs, fabric networks, and a source routing mechanism based on the residue number system (RNS). As proof-of-concept, we developed prototypes with production DC technologies, such as OpenFlow, OpenStack, Open vSwitch and P4. The results of functional and performance tests showed that the proposed SFC scheme provides mechanisms to the NFV orchestrator that allow traffic engineering to make optimized decisions in the selection of network paths. This thesis also paves the way for exploring RNS-based source routing properties in SFC schemes, which can provide features such as fast failure reaction and forwarding without packet rewrite. In a broader analysis, the student published 22 papers in journals and conferences, contributed to funding initiatives, worked on international and national research projects, supervised undergraduate students, and leaded initiatives with innovation impacts.

Network Function Virtualization (NFV) ambitiously aims at shifting functionalities from proprietary hardware to commodity servers as well as providing a common platform for the consolidation of network management. In practice, however, network architects have not massively subscribed to NFV so far. One possible reason for the slow adoption of NFV is the trade-off between commoditization and end-to-end performance in core networks. On the other hand, the ever-increasing disparity between bandwidth requirements and computing power is already pushing network architects toward NFV. By using innetworking processors to co-execute parts of tenant's applications in the cloud, it is possible to offload packet processing tasks. We can also save end-host server CPU cores, while achieving lower latency for service requests. However, applications with an in-networking processor bring two main challenges: flexible programmability and manageable offloading constraints. This work proposes PIaFFE: a framework that uses P4 language for decomposing and deploying Virtual Network Functions (VNFs) into small embedded Network Functions (eNFs) on in-network processors. Our key contribution is allowing the correct balance between hardware capabilities from a programmable NIC, and the flexibility of traditional VNFs on vCPUs. It is only made possible by a novel multi-level chaining scheme in PIaFFE. An experimental use case focuses on the chaining of three eNFs and multi-tenant ready hypervisor, where the percentage of packet processing sharing with a VNF is varied for one of them. As a result, the appropriate offload factor can be found for different traffic conditions for either maximum throughput or minimum latency.