Flight: A Flexible Light Communications network architecture for indoor environments

Recent experimental works have demonstrated the feasibility of the visible light based vehicular communications (VVLC) in intelligent transportation systems (ITS). However, in many respects, this technology is in its infancy and requires further research efforts in several areas. This work presents a flexible network architecture named flexible light (Flight), which is designed for VLC to tackle existing mobility challenges in the network environment. Flight proposes a low-latency handover system that decreases the handover delays to a few tens and hundreds of milliseconds. By means of experiments, we emulate and evaluate indoor mobile network scenarios using only VLC technology

A Flexible Transport Layer Protocol Architecture for Handover in a Vehicular VLC Network

Recent research works have focused on the feasibility of using the multipath-transmission control protocol (MPTCP) in order to optimize network throughput and latency. In this work, we propose a novel architecture using MPTCP for a vehicular visible light communications (VLC) network to improve the performance in terms of network outage duration and throughout. Two relevant MPTCP schedulers and an MPTCP tool are selected to analyze VLC performance during the handover. The results show that the proposed system offers low-outage duration handover of 24 ms and high data throughput of 125 Mbps using "Redundant"and "Default"schedulers, respectively

FDLA: A Novel Frequency Diversity and Link Aggregation Solution for Handover in an Indoor Vehicular VLC Network

VLC (VLC) has been introduced as a complementary wireless technology that can be widely used in industrial indoor environments where automated guided vehicles aim to ease and accelerate logistics. Despite its advantages, there is one significant drawback of using an indoor () network that is there is a high handover outage duration. In line-of-sight VLC links, such handovers are frequently due to mobility, shadowing, and obstacles. In this paper, we propose a frequency diversity and link aggregation solution, which is a novel technique in Data link layer to tackle handover challenge in indoor networks. We have developed a small-scale prototype and experimentally evaluated its performance for a variety of scenarios and compared the results with other handover techniques. We also assessed the configuration options in more detail, in particular focusing on different network traffic types and various address resolution protocol intervals. The measurement results demonstrate the advantages of our approach for low-outage duration handovers in. The proposed idea is able to decrease the handover outage duration in a two-dimensional network to about 0.2 s, which is considerably lower compared to previous solutions

Analyzing Interface Bonding Schemes for VLC with Mobility and Shadowing

Node mobility and shadowing are the most common reasons requiring a handover in vehicular visible light communications (VVLC). In order to provide seamless mobility during the handover, it is required to decrease the network outage duration. This paper aims to improve the outage duration in handover caused by mobility and shadow for VLC networks. We analyze interface bonding schemes using two different primary interface reselection methods. The results show that using "failure"interface selection method instead of "always"method reduces the VLC handover outage duration by 62% and 44% in bonding schemes for transmission control protocol (TCP) and user datagram protocol (UDP) network traffic, respectively

Efficient network traffic classifier: composition approach

Mención Internacional en el título de doctorInternet Service Providers (ISP) are eagerly looking for obtaining metadata from the traffic that they carry. The obtained metadata is a valuable asset for ISPs to enhance their functionality and reduce their operational cost. Classifying a network traffic based on the application (app) that generates the traffic is vital for today’s ISPs and network providers. They use Network Traffic Classification (NTC) to improve many aspects of their network like security and resource allocation. In addition, NTC enables the ISPs to offer new services to their customers and end users. However, NTC faces a big challenge due to the high dynamic Internet ecosystem. Thousands apps are published daily[1] and NTC needs to be updated with their footprint. Moreover some of the existing apps do not follow IANA[2] port number assignment list to use port number which provides more complexity to the ecosystem. Besides, encryption is a trend to secure end-to-end communication and it makes performing NTC hard for those classifiers who relay on information in users payload. Last but not least the volume of traffic that NTC has to investigate is drastically increasing. Therefore, NTC should be fast enough to do the classification on-line which is an essential requirement for many NTC applications. in this thesis, I propose Chain as a novel algorithm to do NTC. Chain sequentially investigates different aspects of a network traffic and brings a significant improvement in tradeoff between classification performance and speed. Besides, it shows a great flexibility to adopt to the new network traffic due to its modularity design. I have implemented Chain in Traffic Identification Engine (TIE) [3] platform and have evaluated its performance with data set [4] which is published by CBA research group at Technical University of Catalunya. Following I have developed a platform named GTEngin to collect ground truth driven from mobile apps and then I have reevaluated the performance of my proposal with the new ground truth. In addition, I participated in an investigation carrying out on mobile Internet to study the possibility of improving my proposal performance in mobile ecosystem.Consequently, I leverage the result of the investigation and measure the enhancement of my proposal performance which achieved accordingly.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Carlos García Rubio.- Secretario: Francisco Javier Simó Reigadas.- Vocal: Roberto Bifulc

Li-Tect: 3-D Monitoring and Shape Detection Using Visible Light Sensors

In this paper, we propose Li-Tect, an algorithm to detect the shape of an object located in an indoor environment using low cost optical elements through sensing the environment's light. The algorithm analyzes, relying on the predictability of optical propagation paths, how much light is expected to propagate in the absence of obstructions caused by the presence of an object. Then, based on the received light when the object is in the room, the algorithm infers the shape of the object. In addition, the algorithm considers the reflected paths from surfaces in order to determine the object's estimated shape. We study five different scenarios characterized by different levels of complexity, room sizes, and a range of reflection nodes. The algorithm is also tested in a real prototype where several experiments are carried out in two scenarios to demonstrate the capabilities of Li-Tect in 2-D and 3-D monitoring and shape detection cases. Finally, the results show that the shape and the detection of objects in the scenarios can be easily acquired with high accuracy, even if the number of transceivers is reduced.This work was supported in part by the Spanish Government under the National Projects “ELISA” and “TERESA-ADA” with ID TEC2014-59255-C3-3-R and TEC2017-90093-C3-2-R, respectively and in part by the European project called VisIoN. This work has been finalized under the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement 764461.Publicad

Immunogenicity of mannan derived from Mycobacterium bovis as a promising adjuvant in vaccine BCG

Background and Objectives: Lipoarabinomannan is one of the components of the significant structural cell surfaces of mycobacteria and serves as an immunostimulatory factor. TNF-α and IL-12 are two examples of the anti-bacterial inflammatory cytokines that are activated and induced during infection. Materials and Methods: In this study, mannan was extracted and processed, and then Bulb/c female mice were used in three groups, one group was given BCG vaccine, the other group was given BCG vaccine with mannan adjuvant, and a non-injected group was used as a control group. Inflammatory factors interleukin-12, TNF-α, IgG and IgM were measured in mouse serum. Results: The levels of the inflammatory factors interleukin-12 and TNF-α in the serum isolated from mice receiving the BCG vaccine with mannan adjuvant showed a significant difference compared to the group that received only the BCG vaccine and the control group [IL-12] and , with P≤0.05.The examination of the level of IgG immune factors in these three groups revealed a significant difference. The group that received the BCG vaccine with mannan adjuvant showed a marked contrast compared to the group that received only the BCG vaccine and the control group, with P≤0.05. The level of IgM was higher in the group that received the BCG vaccine alone compared to the adjuvant vaccine group and the control group, with P≤0.05. Conclusion: Our results indicated that mice receiving the BCG vaccine with mannan adjuvant had significantly higher serum levels of IL-12, TNF-α, and IgG than the group receiving BCG alone

Speeding-up DPI traffic classification with Chaining

The importance of network traffic classification has grown over the last two decades in line with the increasing diversity of networked applications. Nowadays traditional approaches to traffic classification, relying on port numbers and on Deep Packet Inspection (DPI), are not very effective in real scenarios respectively due to the usage of random or non-standard port numbers and to the wide usage of end-to-end encryption. Despite their limitations, port- based and DPI approaches are still widely used in operational networks for a number of network monitoring and management tasks. This paper proposes a practical approach for improving the efficiency of traditional traffic classification techniques by chain- ing fast classification stages (port-based and machine-learning- based), combined to lower their false-positive rate, and a more precise - but time- and resource-demanding - stage based on DPI. Experimental results demonstrate that Chain obtains results in line with DPI approaches in term of Precision, Recall, Accuracy and Area Under the Curve (AUC), while it is 45% faster when compared to nDPIng, a well- known DPI implementation. The appealing of the proposed approach in Network Function Virtualization (NFV) contexts is also discussed