Configuration Protocol Research Articles

Software Defined Network: Load Balancing Algorithm Design and Analysis

Software Defined Network (SDN) cut down the monopolies of producing network devices and their applications. It allows the use of an omniscient controller that manages the overall network and promises for simplifying the configuration and management burden of the traditional Internet Protocol (IP) network. The use of hardware load balancer is a critical issue in conventional IP networks that creates many negative impacts such as the cost affordability, features customization, and availability. Also, the existing load balancing algorithm does not consider the flow size generated by the client nodes. Further, flows are not classified based on the threshold value of the dynamic flow size. The paper proposes to compare the performance of two load balancing algorithms such as flow-based load balancing algorithm and traffic pattern-based load balancing algorithm with distributed controllers' architecture. The result shows that the flow-based load balancing algorithm minimizes response time by 94%, enhances transaction rate by 14% and Traffic pattern-based load balancing algorithm has improved availability by 2.69%.

Schooling in Xenopus laevis Tadpoles as a Way to Assess Their Neural Development.

Escape behaviors, orienting reflexes, and social behaviors in Xenopus laevis tadpoles have been well-documented in the literature. Schooling behavior experiments allow for the observation of tadpole social interactions and in the past have been used to characterize behavioral deficits in models of neurodevelopmental disorders. Unlike other species of frogs, Xenopus tadpoles show polarized schooling. Not only do tadpoles aggregate, they also swim in the same direction. Quantifying both aggregation and relative swim angle can give us an important measure of social behavior and sensory integration. Past iterations of these experiments have required the continued presence of an experimenter throughout the duration of each trial and relied on expensive software for subsequent data analysis. The instrument configuration and analysis protocol outlined here provide an automated method to assess schooling by delivering a series of timed vibratory stimuli to a group of tadpoles to induce swimming behavior and then controlling a camera to document their positions via still images. Both stimulus delivery and image acquisition are automated using the Python programming language. Analysis is done using ImageJ and custom Python scripts, which are provided in this protocol. The specific equipment configuration and scripts shown here provide one solution, but other equipment and custom scripts can be substituted.

Countering radiometric signature exploitation using adversarial machine learning based protocol switching

A Radiometric signature refers to transceiver specific features that are caused by variations in the manufacturing process even for the same circuit design. While such a radiometric signature constitutes a fingerprint that can be exploited for device authentication, it is a threat to privacy. Particularly, in the realm of wireless networks, an adversary may exploit radio frequency (RF) fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show how an adversary could employ RF fingerprinting to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. To counter such a threat, we propose a novel methodology that requires no hardware changes to the radio transceiver and the associated host device. Our methodology is based on coordinated switching among preset link-layer and physical-layer communication protocols. For the latter, we particularly exploit distributed beamforming. We employ adversarial machine learning to select the protocol configuration for each transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.

Analysis and Simulation of IPv4 and IPv6 Protocol

In this modern technology where the growth of the network is increasing rapidly. This paper covers the analysis of different routing protocols, it specifies and identifies that how routers communicate with each other by sharing data also provides the infrastructural view of a network, where routing protocols are used to transmit data packets over wired and wireless networks. There are multiple ways to design routing protocols depend on IPv4 and IPv6 version being widely used as follow RIP (Routing information protocol), IGRP (Interior Gateway Routing Protocol), OSPF (Open Shortest Path First), EIGRP (Enhanced Interior Gateway Routing Protocol) and IS-IS (Intermediate System to Intermediate System) [1]. In this paper, we are analyzed and simulate wired/wireless topological area network simulation using routing protocols. Analysis, simulation, and configuration of this different routing protocol are done by using the CISCO packet tracer.

Satellite quantum communication protocol regardless of the weather

This paper presents a protocol for a relay configuration of one quantum CubeSat and two quantum drones positioned at distant places over the Earth, where: (a) an entangled pair is generated and distributed by the CubeSat between both drones located over the clouds, (b) each drone descends through the clouds with its respective entangled photon, and (c) each drone generates a new entangled photon pair, conserves one and distributes the other one to Mobile Ground Stations (MGS). These latter photons allow to teleport both CubeSat’s entangled photons to the MGSs. Once on Earth, the CubeSat’s entangled photons constitute a bridge for the teleportation of an arbitrary qubit among the MGSs. In this way, we solve the main problem of all quantum communication between a satellite and the Earth: the weather as well as unfavorable environmental conditions. Finally, this paper evaluates the performance of the protocol, which first teleports the CubeSat’s entangled photons and thanks to these the definitive desired qubit, with implementations on the 16-qubit Melbourne processor of IBM Q Experience, where this evaluation constitutes the first stage of a project that tries to communicate two distant points of the earth at any time regardless of the weather.

RETRACTED ARTICLE: An improved source location privacy protection using adaptive trust sector-based authentication with honey encryption algorithm in WSN

A wireless sensor network is a set of sensor nodes used to identify the environment and the corresponding object. It collects all the data of the situation and transmits this detected data to the central node through a wireless network. However, some challenges are encountered in wireless sensor networks i.e., the efficient deployment of sensor nodes in an environment. Some of the issues are limited sources of energy, limited transmission bandwidth, short coverage, data distribution, data persistence, faulty node, and data security redundancy. The Discovery Configuration Protocol (DCP) has been signed for a period of time to monitor the applications. It is required for alternate use, which is very important to recreate the correct data source. Recently, Source Location Privacy (SLP) security at the WSN has attracted a way to prevent opponents from pursuing a backtracking strategy to capture the data source. Delivery of messages from different source nodes to sink nodes is done through an end-to-end location privacy process. With location-based services, data must be retrieved by users who do not have information about the data’s location. So, introduce a proposed Adaptive Trust Sector-Based Authenticated System (ATSAS), which is meant to evaluate the trust confidence value of each node with the help of agent sinks node. When the joining nodes discover their neighbors, they exchange information about the number of single-hop neighbors. The updated confidence value of the node is formed in the network algorithm. The first stage is done using the Honey Encryption (HE) for message authentication and packet encryption to provide security in WSN. These encryptions are making Distribution-Transforming Encoder (DTE) for space of the un-encrypted plain text message into the seed space of n-bit string. The output encryption point is the input to feedback for key generation. To the next level, both direct and indirect observations, such as its neighbor’s trust value, are calculated based on two sources of information. This protocol thereby increases secure communication in a hierarchical WSN. The trust rate control of the network at the source of the packet, which helps drive the time control mechanism. Such Network Control Point (NCP) system development methods provide security and reduce the occurrence of overload on distributed sensor nodes. The theoretical analysis and test results demonstrate that the proposed method improves security, reduces directional attacks, and increases the network’s longevity.

LoRa Device Fingerprinting in the Wild: Disclosing RF Data-Driven Fingerprint Sensitivity to Deployment Variability

Deep learning-based fingerprinting techniques have recently emerged as potential enablers of various wireless applications. However, their resiliency to time, location, and/or configuration changes in the operating environment undoubtedly remains one major challenge that lies ahead in their deployment pathway. In this paper, we present an experimental framework that aims to disclose, understand and overcome the sensitivity of LoRa device fingerprinting to variations in deployment settings. We first began by presenting our RF fingerprinting datasets, collected from 25 different LoRa devices. The datasets cover a comprehensive set of experimental scenarios, considering both indoor and outdoor environments with varying network deployment settings, such as varying the distance between the transmitters and the receiver, the configuration of the LoRa protocol, the physical location of the conducted experiment, and the receiver hardware used for capturing the fingerprints. We then proposed a new technique that leverages out-of-band spectrum distortions, that are caused by device-specific hardware impairments, to provide unique device signatures that we exploit to improve fingerprinting accuracy. Finally, we conducted an experimental study that discloses the sensitivity of deep learning-based RF fingerprinting to changes in various deployment settings while considering three data representations of the learning model input: time-domain IQ, frequency-domain FFT, and Amplitude/Phase polar-coordinate. We found that the learning models perform relatively well when trained and tested under the same deployment settings, with FFT representation yielding the best performance followed by IQ representation. However, when trained and tested under different settings, the models (i) fail to maintain their high accuracy when the channel conditions change, and (ii) completely lose their ability to classify devices when the LoRa configuration and/or the USRP receiver hardware change. In addition, we interestingly observed that FFT representation performs exceptionally poorly when training and testing are done under different deployment settings, regardless of the type of the setting change.

Retracted] Social Governance Structure Construction and Resource Allocation Methods under the Management Mode of the Internet of Things

Aiming at the problems in the social governance structure level under the management mode of the Internet of Things, this paper designs a network agent based on the idea of service grid construction that has the functions of service resource configuration discovery, load balancing, and protocol conversion. Using it as a service governance infrastructure reduces the coupling between business services and service governance, and the network agent will intercept the service call traffic within the platform to achieve cross‐protocol and cross‐frame service communication. Through the proposed global query decomposition strategy and algorithm, a global query mapping is realized, which solves the problem of global query decomposition based on the global data model. We designed the unified management mechanism of two commonly used IoT databases based on XML. This process involves the realization of heterogeneous Internet of Things database resource configuration, data mode conversion, and global query mapping. Among them, in the process of data schema conversion, the conflict of the data in the schema mapping is resolved, the XML Schema is generated, the schema mapping metadata is established, and the XML document of the data is generated. We build a framework for optimal allocation of cloud computing resources based on load forecasting and propose an adaptive elastic control system for resource allocation based on resource status monitoring and demand forecasting. In view of the low resource utilization problem caused by the resource management mode of single virtual machine serving single user adopted by Internet of Things service providers, this paper constructs a new public cloud architecture with a five‐layer structure. On the basis of this architecture, an adaptive configuration mode of virtualized resources based on a single virtual machine serving multiple users is proposed. This mode can automatically search for optimal virtualization resources for application resource requests made by different users and run different applications on the same virtual machine without affecting the quality of service, so that IoT providers can guarantee services. At the same time, it improves the utilization efficiency of IoT resources and reduces energy consumption.

Retracted] Optimization Strategy of IoT Sensor Configuration Based on Genetic Algorithm‐Neural Network

This article carries out the overall design framework of the IoT sensor data processing platform and analyzes the advantages of using the integrated construction platform. The platform is divided into two parts, a web management platform and a data communication system, and interacts with the database by integrating the business layers of the two into one. The web management platform provides configurable communication protocol customization services, equipment information, personal information, announcement information management services, and data collection information monitoring and analysis services. The collected data is analyzed by the sensor data communication service system and then provided to the web management platform for query and call. This paper discusses the theoretical basis of the combination of genetic algorithm and neural network and proposes the necessity of improving genetic algorithm. The improved level involves chromosome coding methods, fitness function selection, and genetic manipulation. We propose an improved genetic algorithm and use an improved genetic algorithm (IGA) to optimize the neural network structure. The finite element method is adopted, the finite element model is established, and the shock piezoelectric response is numerically simulated. The genetic neural network method is used to simulate the collision damage location detection problem. The piezoelectric sensor is optimized, and the optimal sensor configuration corresponding to its initial layout is obtained, which provides guidance for the optimal configuration of the actual piezoelectric sensor.

Multilevel Privacy Controlling Scheme to Protect Behavior Pattern in Smart IoT Environment

Traditional approaches generally focus on the privacy of user’s identity in a smart IoT environment. Privacy of user’s behavior pattern is an important research issue to address smart technology towards improving user’s life. User’s behavior pattern consists of daily living activities in smart IoT environment. Sensor nodes directly interact with activities of user and forward sensing data to service provider server (SPS). While availing the services provided by a server, users may lose privacy since the untrusted devices have information about user’s behavior pattern and it may share data with adversary. In order to resolve this problem, we propose a multilevel privacy controlling scheme (MPCS) which is different from traditional approaches. MPCS is divided into two parts: (i) behavior pattern privacy degree (BehaviorPrivacyDeg), which works as follows: firstly, frequent pattern mining‐based time‐duration algorithm (FPMTA) finds the normal pattern of activity by adopting unsupervised learning. Secondly, patterns compact algorithm (PCA) is proposed to store and compact the mined pattern in each sensor device. Then, abnormal activity detection time‐duration algorithm (AADTA) is used by current triggered sensors, in order to compare the current activity with normal activity by computing similarity among them; (ii) multilevel privacy design model: we have divided privacy of users into four levels in smart IoT environment, and by using these levels, the server can configure privacy level for users according to their concern. Multilevel privacy design model consists of privacy‐level configuration protocol (PLCP) and activity design model. PLCP provides fine privacy controls to users while enabling users to set privacy level. In PLCP, we introduce level concern privacy algorithm (LCPA) and location privacy algorithm (LPA), so that adversary could not damage the data of user’s behavior pattern. Experiments are performed to evaluate the accuracy and feasibility of MPCS in both simulation and real‐case studies. Results show that our proposed scheme can significantly protect the user’s behavior pattern by detecting abnormality in real time.

A Ferroelectric-based Volatile/Non-volatile Dual-mode Buffer Memory for Deep Neural Network Accelerators

In this paper, a dual-mode buffer memory based on the CMOS compatible HfZrO2 ferroelectric material is proposed for DNN accelerators. It can operate in both volatile eDRAM mode and non-volatile ferroelectric RAM (FeRAM) mode. The functionality of the proposed dual-mode memory bit-cell design is verified using SPICE simulation with the multi-domain Preisach physical model. A data-lifetime-aware memory mode configuration protocol is proposed to optimize the buffer access energy for both DNN inference and training. Detailed circuitry and architectural support for the dual-mode memory are discussed. For DNN training with ferroelectric-field-effect-transistor (FeFET) and SRAM-based compute-in-memory (CIM) accelerator, the proposed dual-mode buffer design improves the overall energy efficiency by 92.2%~98.7%, 44.1%~47.6%, 12.6%~13.0% compared to baseline designs using SRAM buffer with the same buffer area, eDRAM and FeRAM with the same buffer capacity, respectively. For DNN inference with tensor-processing-unit (TPU)-like systolic array, the energy efficiency during computing is improved by 40.7%~45.6%, 18.4%~29.6% compared to the designs with eDRAM and FeRAM buffer, respectively. By storing the persistent data using the non-volatile mode, the energy efficiency of systolic array is improved by 2.3~5.5 over SRAM-based design when standby is frequent.

"Leveraging Raspberry Pi as a server for the integration of the NETCONF protocol within IoT systems based on YANG"

Herein the idea of leveraging Raspberry Pi as a server for the integration of an incipient network management protocol, the Network Configuration Protocol (NETCONF), within IoT systems based on YANG is presented. The practical realization of this idea requires the implementation of the NETCONF protocol together with REpresentational State Transfer web services (RESTful). Such an interesting and innovative practical realization like this opens new additional possibilities in domotics systems and these possibilities will be discussed in this paper.

Decentralized Configuration Protocols for Low-Cost Offloading From Multiple Edges to Multiple Vehicular Fogs

A vehicular-fog (VF) system as an emerging platform consists of electric vehicles with computing resources that are mostly under-utilized. This paper considers a two-tier federated Edge and Vehicular-Fog (EVF) system, where edge systems may partially offload user traffic to nearby VFs for potential cost reduction. Offloading configuration is to determine the ratios and targets of offloading traffic for maximal cost reduction, which is formulated as a mixed integer programming problem in this paper. We first present a decentralized offloading configuration protocol (DOCP) for an individual edge system to set up its own offloading configuration. We then propose a matching protocol among multiple edge systems to resolve resource contention when they simultaneously request resources from the same VF. Simulation results show that the proposed approach can leverage the heterogeneity of cost and capacity between edge systems and VFs. The proposed protocol outperforms greedy approaches by at most 40% and is comparable to a centralized off-line approach that is based on Particle Swarm Optimization.

Configurable Network Protocol Accelerator (COPA)

Today's field-programmable gate arrays (FPGAs) offer not only programmable acceleration capabilities but also include advanced features that are on-par with a mainstream processor platform. However, rather than being deployed as autonomous acceleration nodes, they are generally deployed as second-class citizens under the control of a standard processor platform. The configurable network protocol accelerator (COPA) framework enables FPGAs to support custom inline/lookaside accelerators and attach directly to a 100-Gb/s Ethernet network. The hardware component provides the necessary networking/accelerator infrastructure. The software component supports an open standard API that enables the integrated accelerators to be used with network communication by applications/middleware. COPA enables a truly distributed FPGA accelerator model and has been implemented on variants of Stratix10 FPGAs. The COPA framework along with example accelerator IPs has been validated with microbenchmarks as well as proxy client/server benchmarks and achieves full network bandwidth.

Deep Neural Network Feature Designs for RF Data-Driven Wireless Device Classification

Most prior works on deep learning-based wireless device classification using radio frequency (RF) data apply off-the-shelf deep neural network (DNN) models, which were matured mainly for domains like vision and language. However, wireless RF data possesses unique characteristics that differentiate it from these other domains. For instance, RF data encompasses intermingled time and frequency features that are dictated by the underlying hardware and protocol configurations. In addition, wireless RF communication signals exhibit cyclostationarity due to repeated patterns (PHY pilots, frame prefixes, etc.) that these signals inherently contain. In this paper, we begin by explaining and showing the unsuitability as well as limitations of existing DNN feature design approaches currently proposed to be used for wireless device classification. We then present novel feature design approaches that exploit the distinct structures of the RF communication signals and the spectrum emissions caused by transmitter hardware impairments to custom-make DNN models suitable for classifying wireless devices using RF signal data. Our proposed DNN feature designs substantially improve classification robustness in terms of scalability, accuracy, signature anti-cloning, and insensitivity to environment perturbations. We end the paper by presenting other feature design strategies that have great potentials for providing further performance improvements of the DNN-based wireless device classification, and discuss the open research challenges related to these proposed strategies.

Архітектура контролера вузла адаптивної мобільної мережі з аморфною топологією

The purpose of the article is to develop an adaptive mobile network node architecture with an amorphous topology. The article describes the host controller architecture. The concept of a mobile network is described. The mobile network is adaptive, self-organizing, and able to operate autonomously. The presented mobile network node controller architecture is the object swarm control system basis. A stack of mobile network protocols is presented, consisting of a control protocol, a data exchange protocol and a configuration protocol. The nodes of the network controllers interaction is carried out using several transceivers. The use of multiple transceivers made it possible to distribute data traffic, configuration traffic, and management traffic over different channels, which made it possible to carry out information exchange at the same time. The protocol stack is minimized. The functionality of the host and the network object (swarm) is completely separated. The topology of the mobile network is not deterministic, amorphous and changes when the network objects move in space. In this case, some connections are lost and others arise. The routing tables are constantly updated. The network in accordance with the laid down algorithm, is able to build the necessary topology and organize the necessary connections in order to complete the task with a swarm of objects. The network is capable of building packet retransmission chains for remote network objects. Thus, the implementation of a mobile network at its low cost allows solving a certain range of problems. The performer can be either a separate object associated with the operator through a swarm of repeaters, or a swarm of objects. In order to increase the efficiency of the formation of routing tables and minimize the cluster structures in a mobile network, it is advisable to use optimization algorithms based on set theory. The mobile network is designed to control a swarm of objects, which can be UAVs, robotic objects and control systems for various technological processes.

Research on spatiotemporal fluctuation characteristics of underwater acoustic communication signals

In underwater acoustic communication systems, channel characteristics are mainly affected by changes in space and time. The changes embody the random fluctuations of the seabed and sea surface, and the effects of refraction and scattering caused by seawater layered media on the sound field. As the channel is time-varying and space-varying, the rapid fluctuation of communication signals, which is shown in the frequency selective fading in frequency-domain and the signal waveform distortion in the time domain, leads to a bad effect on the performance of the underwater acoustic communication system. The techniques to compensate or offset the effects of deep fading such as coding error correction and spatial diversity are widely used in underwater acoustic communication systems, which may cause a significant waste of limited communication efficiency. This paper analyzes the spatiotemporal fluctuation characteristics of both signal field and noise field, and summarizes the temporal and spatial variation rules. The effects of signal spatiotemporal fluctuation on communication systems are significantly reduced by reasonably selecting the communication signal parameters, equipment deployment depth and horizontal distance, which will guide the parameter configuration and network protocol optimization of communication systems.

Network evaluation and protocol deployment for complex deep-space networks based on DTN

Previous research on deep-space networks based on delay-tolerant networking (DTN) has mainly focused on the performance of DTN protocols in simple networks; hence, research on complex networks is lacking. In this paper, we focus on network evaluation and protocol deployment for complex DTN-based deep-space networks and apply the results to a novel complex deep-space network based on the Universal Interplanetary Communication Network (UNICON-CDSN) proposed by the National Space Science Center (NSSC) for simulation and verification. A network evaluation method based on network capacity and memory analysis is proposed. Based on a performance comparison between the Licklider Transmission Protocol (LTP) and the Transmission Control Protocol (TCP) with the Bundle Protocol (BP) in various communication scenarios, a transport protocol configuration proposal is developed and used to construct an LTP deployment scheme for UNICON-CDSN. For the LTP deployment scheme, a theoretical model of file delivery time over complex deep-space networks is built. A network evaluation with the method proposed in this paper proves that UNICON-CDSN satisfies the requirements for the 2020 Mars exploration mission Curiosity. Moreover, simulation results from a universal space communication network testbed (USCNT) designed by us show that the LTP deployment scheme is suitable for UNICON-CDSN.

Lighting control network based on KNX protocol, for the reduction of energy consumption

<span>This article presents the development of a lighting control network to reduce the energy consumption of a commercial building, using the KNX protocol; because of the high rates of electricity consumption, the same that are reflected in the payment of the electricity supply. For this, the design of the network architecture is carried out, the tree type quality and it has KNX, DALI components and LED luminaires, which are interconnected by means of an Ethernet type BUS; The KNX protocol configuration is then performed using the ETS version 5 software; carries out the implementation of KNX technology, determines the reduction of energy consumption by 82.33%. Likewise, emissions of carbon dioxide (CO2), one of the main gases involved in climate change, were reduced by 85%. With these results we obtain economic and environmental benefits; Reason why it is proposed to perform the same procedure for the control of air conditioning systems, since their operation represents 32.8% of the energy consumption of an establishment.</span>

Developing energy-based loading protocols for seismic evaluation of steel moment frames' components

One of the main obstacles at the very first step of every experimental or numerical investigation is to select proper loading scenarios for evaluating the seismic performance of structural elements. For both qualifying the structural members and developing behavioral models as the primary input for nonlinear analysis of structures, there is a need for loading regimes that are able to simulate loading conditions similar to real earthquakes. This paper discusses the methodology used to develop a series of energy-based loading protocols for testing beam to column connections and columns of steel special moment frames. Several relationships controlling the configuration of loading protocols are proposed based on results obtained from multiple nonlinear time history analyses that have been conducted on a variety of structures. The histories of cycles obtained from these loading regimes are case-specific in order to dissipate a certain amount of energy from each structural component within a definite time based on its properties. In the case of column axial loading protocol, in addition to a force-control regime, a displacement-control one is also introduced to eliminate the instability issues arising when force-control loading scenarios are employed. After presenting the required steps to implement the proposed loading protocols, their accuracy is investigated by comparing them to the results of nonlinear time history analyses as well as some conventional and well-known loading protocols.