Network Interface Research Articles

Power control system for lightweight electric aircraft

This article describes the development and key features of the lightweight electric aircraft power system. The overview includes internals of main power control units and propulsion system rigging, hardware parts selection guide and fail-safe features in hardware and software of aircraft. The propulsion system includes lightweight solution utilizing brushless DC (BLDC) motor with a propeller as the main thrust generator, and a control system, which consists of insulated gate bipolar transistors (IGBT) 3-phase bridge and control system, as well as a special optical-insulated throttle controller. Main power control system automatically starts up aircraft modules and provides all the necessary information coming from the sensors. All of the key components in aircraft are connected through Controller Area Network (CAN) interface. To create a strong protection against surges and electric interference special cabling and interfacing aspects between several systems of lightweight aircraft are described. Part of the article provides a future extendibility, versatility and economical concepts.

Towards Optimizing WLANs Power Saving: Context-Aware Listen Interval

Despite the rapid growth of Wireless Local Area Networks (WLANs), the energy consumption caused by wireless communication remains a significant factor in reducing the battery life of power-constrained wireless devices. To reduce the energy consumption, static and adaptive power saving mechanisms have been deployed in WLANs. However, some inherent drawbacks and limitations remain. We have developed the concept of Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. In this paper we develop the power saving modes of CALI. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to Pyles’ et al. SAPSM power saving approach, which also employs an ML classifier.

A prominent approach to design low noise amplifiers for 802.11 wireless receiver frontends

With the efforts of innovative consortium, wireless local area network (WLAN) became alternative standard in wireless local area networking applications. From the beginning the data rate keeps pace from mega bits per second (Mbps) up to giga bits per second. The ease of adaptability of WLAN protocol and the need for high data rate communication system made a reason to develop a solution and to incorporate real time wireless transceivers on network interface cards and FPGAs. Over the past decade, the RFIC technology became the hive of communication industry, with the proliferation of 802.11 a, g, n, ac, ad wireless protocols. The applications like ultra wide band (UWB), digital TV broadcasting and global positioning system found their way to main-stream gadgets. With the design preferences such as interconnect parasitics, interconnects and coupling between devices the passive elements must be precisely designed. In this article, novel requirements of low noise amplifier (LNA) design for 2.4 GHz application with noise modelling is presented.

DNIC Architectural Developments for 0-Knowledge Detection of OPC Malware

We present an anti-malware solution that is able to reliably detect Object Linking and Embedding for Process Control (OPC) malware on machines in production. Detection is attained on the very first encounter with OPC malware, and hence without any prior knowledge of their code and data. We architected the integration of a decoy network interface controller (DNIC) with a layer of kernel code that emulates a target OPC machine. A DNIC displays a (nonexistent) network, which the compromised machine appears to be connected to. OPC emulation displays a valid (but nonexistent) target OPC machine, which appears to be reachable from the compromised machine over the (nonexistent) network. Our code intercepts OPC malware during their search for target machines over the network. Its overall architecture is crafted to validate the infection by leveraging OPC protocol mechanics. The same principles of operation are used to recognize goodware that access a DNIC by accident. Safe co-existence with production functions and real I/O devices is ensured by a monitor filter driver, which removes all decoy data bound for the monitor. We tested our DNIC architectural developments against numerous OPC malware samples involved in the Dragonfly cyber espionage campaign, and discuss the findings in the paper.

Design and Development of Real-time landslide early warning system through low cost soil and rainfall sensors

Development of cost effective early warning systems is of paramount importance for real time monitoring of natural hazards such as Landslides, Floods, Earthquake etc. The present research work discusses about the design and development of cost effective early warning system for monitoring shallow debri landslides. The warning system consist of field unit and remote network interface. The field system includes the rainfall, temperature, humidity, soil temperature, soil moisture and Tiltmeter as primary sensors for monitoring the landslide movements with an independent power supply of 50 W solar panel. The datasets that are received are stored in the local data logger with the help of microsd card. Real time monitoring of the datasets and analysis was made possible by storing the datasets in remote Database Management System. These datasets are transmitted via the Global System for Mobile communication module installed at the field unit. In order to convey the alerts during emergency time a simple visual message has been adopted for the current model. Level I, Level II and Level III, Level I being the threshold values are within the desired limit and actions are not necessary and Level III denotes threshold values have exceeded the set limit and warning are issued to local stake holders. A pilot study of the Early Warning System has been installed on site for quality checks of the sensor & data and the results were monitored. The model developed not only can address the early warning signs of mass movement but also can be calibrated to predict flood in major streams based on rainfall intensity.

A Review of Fuel Cell to Distribution Network Interface Using D-FACTS: Technical Challenges and Interconnection Trends

Today, the worldwide public interest in reducing power quality issues and greenhouse gas emissions is a key driver to study fuel cells (FCs) connected to distribution network systems (DNs) based on distributed flexible alternating-current transmission systems (D-FACTS). DNs will need to develop a better performance on Power Quality (PQ) while providing a more efficient energy technology. This study reviewed in-depth the interface of DN to FC systems. By focusing on the FC interface and the associated technical challenges, this review may help reduce the risk of DNs, minimizing the consumption of fossil fuels for power generation, lowering the emission of hazardous gases while dramatically increasing electrical power loads, improving PQ and stability. Besides, the study deliberated aspects of FC power technology with DNs interfacing based on D-FACTS. Specifically, the discussion encompassed the configuration structures of FC power technology and DNs connection based on D-FACTS, technical challenges of DNs, and its trends to determine the diagnosis, integration, and optimization for FC power technology.

Застосування навчання з підкріпленням для побудови рекомендаційної системи

Nowadays the recommendation is an important part of human life, it is used for industrial, business, and even academic purposes. Currently, various recommendation techniques are provided such as content-based ltering [11], matrix factorization [6], Naive Bayes classi- er [1,12], logistic regression [1], and multi-armed bandit approaches [7,18]. Unfortunately, all of them are considering the recommendation as a static process, assuming that user preferences are not changing during the period of time and they are not taking into consideration that user interactions with the system are sequential. These methods and algorithms are widely used by giant companies like Amazon [9], Netix [4, 15] and Google [2]. The article demonstrates using machine learning and especially reinforcement learning for items recommendation based on positive interactions of users with the system. This approach, unlike others, is dynamic, otherwise, it is adapted to user preferences changes and allows to focus on maximizing the benets of user interaction with recommendation system. Besides that reinforcement learning also is focused on long-term reward and understands that user interactions with the system are sequential. The article describes an example of using this approach to recommend movies based on user positive ratings provided to that movies, but speaking in general this technique can also be used for any type of information the only necessary thing is a representation of these elements to work with the neural network interface. The environment of the proposed recommendation system is represented as Markov Decision Process [19] in which the user interacts with the recommendation agent and that agent creates proposed items for a specic user. Neural network architecture is built using the ¾Actor-Critic¿ learning conceptual model [19] which combines two methods of learning: Policy-learning as of ¾Actor¿ network and Q-learning as of ¾Critic¿ network. ¾Actor¿ network is taking users' positive interaction with the system as an input and generates approximate recommendation. On the other hand ¾Critic¿ is taking that approximate Ðîìàíþê Á., Ïåëþøêåâè÷ Î., Ùåðáèíà Þ. 13 recommendation, concatenate it with positive interaction, and calculate Q-value to determine how good will be that approximate recommendation. The combination of these two learning techniques considers both dynamic adaptations to user preferences changes and long-term interactions which lead to fast performance and better recommendations are proposed. The main purpose of this process is to maximize reward, which means that the proposed recommendation will be positive for both user and system. Inside the system, movies are represented as an array of numeric elements to work properly with the neural network interface. Movies representation are created according to specic characteristics of each one, such as year of production, director, genres, country of production, companies, rating, popularity, budget, etc. To combine all those properties principal component analysis is used. Besides that to represent user ratings there was developed ¾State representation module¿, which combines rated movies and numeric rates itself and represents them in the same way as an array of digits. 10 thousand movies and 1 million rates from a dataset called MovieLens were used to train the model, from which 95% of ratings were used to train the model and the other 5% for accurate calculation. The proposed approach demonstrates great convergence up to 98% on big data sets and does not take a big amount of time to train the neural network model. The proposed methodology combines the dynamic approach of generating recommendations understanding user actions as a sequential process and concentrating on getting a long-term reward. The recommendation in such a way is very perspective in the future and can be used by big companies to maximize their prots from sealing dierent types of things or information.

Network Function Decomposition and Offloading on Heterogeneous Networks With Programmable Data Planes

Programmable network hardware is emerging as a viable option for offloading and thus accelerating network functions. However, the heterogeneous resources available in the network calls for a disaggregated deployment approach. Programmable switches, programmable Network Interface Cards (NICs), and in-network compute nodes exposes different peculiar resources and capabilities that can be maximally exploited only if the network functions are decomposed into multiple smaller network functions. This work presents a framework for the automatic deployment of disaggregated and decomposed network functions. The framework comprises an orchestrator capable of deploying the decomposed network functions on programmable network hardware and software switches running in containers. The orchestrator exploits an optimization model for choosing the best decomposition according to the traffic demands, the network topology, and other constraints. An improved seamless deployment model and heuristic also accounts for the necessity of rerouting traffic when hardware nodes need to be re-programmed. Furthermore, the framework provides a tool to combine multiple functions into a single P4 program via a template pipeline that can be deployed to a programmable switch. Numerical results highlight the advantages of offloading decomposed network functions to programmable network hardware. Furthermore, we show how the seamless deployment model and heuristic have negligible effects on the allocation time and accepted traffic requests while guaranteeing the rerouting of traffic when switches are put in maintenance mode.

RETRACTED: Remote classroom system for Chinese linguistics teaching based on FPGA and embedded system

Online intelligent remote classrooms subject to Chinese Linguistics and FPGA (Field-programmable gate array) procedures are used to handle complex blends of teaching and work estimations. FPGA (Field-Programmable Gate Array) is regularly realized as ASICs (Application-Specific Integrated Circuit) because of the high model thickness and execution essentials. Embedded designs regularly don't achieve high efficiency and low power application. The FPGA (Field-Programmable Gate Array) online course replication, appropriately giving the Fault Distributed Algorithm and hardware stage to organizing and evaluating new features before silicon is set into usage. Chinese Linguistics causes the network interface to grow faster and decline the ASIC design cycle. The Fault Distributed Algorithm is the improved forefront FPGA (Field-Programmable Gate Array) introduced another maximize card that reinforces the latest ASIC and FPGA plans that are a bit of checking trigger. The delivered output is taken care of in a model group and set aside in the data line that makes up an amount model pack. Considering the produced test set, the proposed system discovers each level's progression closeness, ultimately the overall likeness.

Flow length and size distributions in campus Internet traffic

The efficiency of flow-based networking mechanisms strongly depends on traffic characteristics and should thus be assessed using accurate flow models. For example, in the case of algorithms based on the distinction between elephant and mice flows, it is extremely important to ensure realistic flows’ length and size distributions. Credible models or data are not available in literature. Numerous works contain only plots roughly presenting empirical distribution of selected flow parameters, without providing distribution mixture models or any reusable numerical data. This paper aims to fill that gap and provide reusable models of flow length and size derived from real traffic traces. Traces were collected at the Internet-facing interface of the university campus network and comprise four billion layer-4 flow (275 TB). These models can be used to assess a variety of flow-oriented solutions under the assumption of realistic conditions. Additionally, this paper provides a tutorial on constructing network flow models from traffic traces. The proposed methodology is universal and can be applied to traffic traces gathered in any network. We also provide an open source software framework to analyze flow traces and fit general mixture models to them.

Assessing Container Network Interface Plugins: Functionality, Performance, and Scalability

Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability, and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different Pods (a group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source ‘CNI plugins’. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this article, we compare the various open-source CNI plugins available from the community, qualitatively, and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, especially because of the interaction with the datapath/iptables as well as the host network stack. Overlay tunnel offload support in the network interface card plays a significant role in achieving the good performance of CNIs that use overlay tunnels for inter-host Pod-to-Pod communication. We also study scalability with an increasing number of Pods, as well as with HTTP workloads, and briefly evaluate Pod startup latency. Our measurement results inform the outline of an ideal CNI environment for Kubernetes.

Toward In-Network Event Detection and Filtering for Publish/Subscribe Communication Using Programmable Data Planes

Industrial Internet of Things (I-IoT) applications require a large number of sensor data to be processed under tight delay and jitter constraints. In such applications, flexible event detection and fast reaction to critical events is an important building block. Traditional approaches use either proprietary networks and dedicated hardware or transmit sensor data towards processing elements in the Cloud or at the Network Edge, using distributed stream processing frameworks. For scalability, a large number of servers are needed and processing on commodity CPUs typically involves high and unpredictable latency. In this article, we explore how programmable data planes can be used to detect events flexibly and trigger customized and programmable actions directly from the switch program or the programmable network interface card (SmartNIC). We present FastReact-PS, an event-based publish/subscribe I-IoT processing framework in P4 language, which can be flexibly customized from the control plane. Together with stateful processing, FastReact-PS supports windowed time series analysis as well as complex event detection and processing based on Boolean logic directly in the data plane of newly emerging programmable networking devices. The logic can be adjusted dynamically from the control plane without the need for recompilation. We implement FastReact-PS in P4 and evaluate it on both a SmartNIC and a DPDK-based software switch running in user space. Our evaluation shows that the latency is reduced by one order of magnitude compared to end-host based approaches at significantly lower jitter while being scalable to processing up to 11 million events per second.

The organizational adaptation of universities to smart specialization: the emergence of strategic network interface units

ABSTRACT Universities are increasingly expected to engage in regional innovation policy. This has reinforced the need for organizational adaptation of university structures to respond to these new challenges. Recently, a variation in the typical knowledge transfer structures has emerged: strategic network interface units. These units are multidisciplinary and cluster-like formal networks led by universities in collaboration with businesses, government authorities and other organizations. This paper compares the organizational adaptation of two universities – the University of Aveiro and the Autonomous University of Barcelona – as they assume increased responsibilities in regional innovation dynamics. Through interview-based analysis, findings suggest these interface units were created to support the alignment of the universities with smart specialization strategies and EU priority areas for accessing funding. However, while the original aims of these units are very similar, their institutional and operational configuration has led to different cooperative arrangements. Transversal communication based on trust and capacity-building was an important supporting factor in the innovation impact of these units.

Adaptable Switch: A Heterogeneous Switch Architecture for Network-Centric Computing

Network-centric computing offloads and disaggregates computing and data processing from CPU to network in order to support growing throughput, big data volume, and information complexity in data centers. An emerging paradigm is employing SmartNIC for network-centric computing, which introduces user-specific processing at a host's network interface. In this article, we take this initiative further to tackle current proprietary processing and computing issues in the network core: the switches. We propose a new hardware architecture called adaptable switch. Based on testing of its prototype empowering three use cases, we demonstrate that both high throughput and processing flexibility can be achieved simultaneously on an adaptable switch.

Localisation algorithm for security access control in railway communications

With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal strength (RSS) and channel state information (CSI) in railway communications are measured by AR9344 network interface card. Then, based on the measurement data, the authors propose trajectory-based, neural network-based (NN-based) and ray tracing-based (RT-based) localisation algorithms, serving for location-based security access control. Specifically, the trajectory-based algorithm combined with trajectory simulation, movement detection and dynamic time warping algorithms, realises passengers enter/exit pattern detection. The NN-based algorithm leverages back-propagation network (BPN) and constructs training sets with measurement-based RSS and CSI, finishing accurate localisation. Besides, they evaluate the algorithm performance under different layers of BPN. RT-based localisation algorithm combines measurement data and simulation analysis, leveraging simulated-based multiple-input multiple-output received power and delay spread to realise lightweight localisation. After evaluation, the RT-based algorithm can achieve the highest accuracy of localisation, up to 99.9% and is designed to be straightforward for integration with commercial access points and deployment to railway communications.

A Study on Network Interface Scheme of Heterogeneous Systems for SEM's Smart Factory Preliminary Preparation

A Study on Network Interface Scheme of Heterogeneous Systems for SEM's Smart Factory Preliminary Preparation

Cinnamon Cassia Oil Emulsions Stabilized by Chitin Nanofibrils: Physicochemical Properties and Antibacterial Activities.

Nowadays consumers are increasingly demanding food with fewer synthetic preservatives, which makes antimicrobial essential oils (EOs) from plants promising alternatives. In this work, surfactant-free emulsions were successfully fabricated from Cinnamon cassia oil (C. cassia oil) with partially deacetylated chitin nanofiber (ChNF) adopted as a Pickering stabilizer. The storage stability and microstructures of the emulsions with different concentrations of ChNF were studied in detail. As ChNF concentration increased, the emulsion droplet size decreased while the emulsion stability increased with stable periods as long as 90 days. This could be attributed to the Pickering stabilization realized by irreversible adsorption of the ChNF at the oil-water interface (revealed by fluorescent microscopy) and subsequent formation of an interdroplet ChNF network in the continuous phase, which was further strengthened in the presence of the aldehyde moiety in the C. cassia oil (verified by FTIR spectra). The rheological data and SEM images provided further evidence for network formation in the emulsions with increased ChNF concentration. Furthermore, the antimicrobial activity of the emulsion against Escherichia coli and the release patterns of EOs from emulsions were also investigated. The emulsions showed prolonged antibacterial activities but enhanced diffusion efficiency with the introduction of ChNF, which turned out to be a good encapsulation system for the controlled release of EOs. This work evidences the promising advantages of ChNF-stabilized Pickering emulsions as a facile EOs delivery system for application in food preservation and related fields.

(Invited) Silica Based Ionogels: Raman Study of Protic Vs. non Protic Ionic Liquids, with Lithium Salt Vs. Sodium Salt

Ionogels represent a route to biphasic materials, for the use of ionic liquids (ILs) for all solid devices. Confining ILs within host networks enhances their averaged dynamics, resulting in improved charge transport. Fragility, short relaxation times, low viscosity, and good ionic conductivity, all them appear to be related to the IL / host network interface. The presence of ILs at interface neighborhood leads to the lowering of cation-anion interaction, i.e. to the breakdown of aggregated and structured regions that are systematically found in bulk ILs.[1] This “destructuration”, as well as segregative interactions at interface,[2] coupled with percolation of the bicontinuous solid/liquid interface,[3] make these materials very competitive among the existing solid electrolytes.Herein we will present an in-depth study of coordination of TFSI anion with lithium or with sodium cations, in the presence of protic or non protic pyrrolidinium cations, confined within silica with well-controlled porosities. The coordination number of TFSI-cation is shown to decrease upon confinement, making the lithium of sodium cation more diffusive. Moreover, the cation-anion layering at the interface is studied by DFT and shows striking modifications when a lithium or sodium salt is added, thus highlighting the effect of the chemistry of the host network, similarly with previous study.[4] References 1) A. Guyomard-Lack, P.-E. Delannoy, N. Dupré, C. V. Cerclier, B. Humbert, J. Le Bideau, Phys. Chem. Chem. Phys., 16, (2014) 23639-236452) A. Guyomard-Lack, B. Said, N. Dupré, A. Galarneau, J. Le Bideau, New J. Chem., 40, (2016) 4269-42763) C. V. Cerclier, J.-M. Zanotti, J. Le Bideau, Phys. Chem. Chem. Phys., 17, (2015) 29707—297134) D. Aidoud, D. Guy-Buissou, D. Guyomard, B. Lestriez, J. Le Bideau, J. Electrochem. Soc., 165, (2018) A3179-A3185

Modified spider monkey optimization—An enhanced optimization of spectrum sharing in cognitive radio networks

SummaryAt present, the demand for wireless communications is growing tremendously. Cognitive radio network plays an important role in making the spectrum to be used effectively. Uncertainty in channels and interference were generally occurs that reduced the system efficiency. To overcome the load usage problem, the proposed system of spectrum sensing and scheduling algorithms is introduced, where the available spectrums are sensed or detected and scheduled to load the free spectrum. Spectrum sensing is a cognitive radio basic function to thwart the harmful interference with licensed users and detect the available spectrum for enhancing the spectrum's utilization. In the proposed technique, initially, the spectrum sharing and sensing method is put forward to raise the throughput and quality of service necessity. At this time, spectrum sharing in common with scheduling process is presented, where the available spectrum, the load is scheduled and sensed to free the spectrum. Here, the modified spider monkey optimization (MSMO) technique is used for spectrum sensing and detecting free spectrums, thereby enhancing the energy efficiency of the available spectrum. This technique will found the optimal solution and increases the expectation of some decisions. Modified round robin algorithm is used for scheduling load. In this algorithm, every packet flow has its packet queue presented in the network interface controller. The performance analysis is finally measured using metrics such as throughput, handoff, success probability, and false alarm probability.

Permutation-Based Transmissions in Ultra-Reliable and Low-Latency Communications

This letter proposes a novel permutation-based transmission strategy to improve the goodput in wireless networks for achieving ultra-reliable and low-latency communications. The proposed scheme divides the application-layer data into two portions: the first one is conveyed by the permutation with repetition of various lengths in a group of packets rather than encapsulated into the packets, whilst the second portion is encapsulated into these packets to be physically delivered through network interface in the conventional way. The lengths of the packets used to deliver the second portion are determined by the first one. Thanks to the goodput gain attained by the permutation-conveyed application-layer data, the network congestion is alleviated, which leads to lower latency and/or less dropped packets. The validity of this transmission strategy is substantiated by the analysis in the metrics of goodput, latency, physical-layer throughput and secrecy rate within a design paradigm of short-packet communications.