Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

PREPARATION OF STAR NETWORK PEG-BASED GEL POLYMER ELECTROLYTES FOR ELECTROCHROMIC DEVICES

An amorphous,colorless,and highly transparent star network polymer with a pentaerythritol core linking four PEG-block polymeric arms was synthesized from the poly(ethylene glycol)(PEG),pentaerythritol,and dichloromethane by Williamson reaction.FTIR and ~1H-NMR measurement demonstrated that the polymer repeating units were C[CH_2-OCH_2O-(CH_2CH_2O)_m-CH_2O-(CH_2CH_2O)_n-CH_2O]_4.The polymer host held well mechanical properties for pentaerythritol cross-linking.The gel polymer electrolytes based on Lithium pe...

AN ALGORITHM FOR CANONICAL PIECEWISE-LINEAR DYNAMIC NETWORKS CONTAINING DEVICES WITH CHARACTERISTIC FAMILY

This paper improves the modeling method for the device with characteristic family presented by L. O. Chua (1977) and results in the one-dimensional fluctuating canonical piecewise-linear model. It is an efficient model. The algorithm for canonical piecewise-linear dynamic networks with one dimensional fluctuating model is discussed in detail.

The Development of Network based Composite Power quality Regulation Device

First, the paper analyzes the advantages and disadvantages of all kinds of reactive power compensation technology, and then proposes a principle and integrated control strategy of the composite operation of TSC and SVG, also the paper designs and develops the main controller of Network based composite power quality regulation device, based on RTDS, the real-time digital simulation model of The Device is established, and finally the prototype of the device is developed with the function of filter and split-phase compensation. The main controller determines the cooperative operation of both TSC and SVG, and the switching strategy of TSC. The simulation result in RTDS can verify the precision of the measure system and the validity of the control logic, the prototype has finished the type test according to the national standard.

A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization

5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.

Retraining Deep Neural Network with Unlabeled Data Collected in Embedded Devices

Because of computational complexity,the deep neural network(DNN)in embedded devices is usually trained on high-performance computers or graphic processing units(GPUs),and only the inference phase is implemented in embedded devices.Data processed by embedded devices,such as smartphones and wearables,are usually personalized,so the DNN model trained on public data sets may have poor accuracy when inferring the personalized data.As a result,retraining DNN with personalized data collected locally in embedded devices is necessary.Nevertheless,retraining needs labeled data sets,while the data collected locally are unlabeled,then how to retrain DNN with unlabeled data is a problem to be solved.This paper proves the necessity of retraining DNN model with personalized data collected in embedded devices after trained with public data sets.It also proposes a label generation method by which a fake label is generated for each unlabeled training case according to users’feedback,thus retraining can be performed with unlabeled data collected in embedded devices.The experimental results show that our fake label generation method has both good training effects and wide applicability.The advanced neural networks can be trained with unlabeled data from embedded devices and the individualized accuracy of the DNN model can be gradually improved along with personal using.

A Framework for Multi-Hop Ad-Hoc Networking over Wi-Fi Direct with Android Smart Devices

The wide diffusion of mobile devices that natively support ad hoc communication technologies has led to several protocols for enabling and optimizing Mobile Ad Hoc Networks (MANETs). Nevertheless, the actual utilization of MANETs in real life seems limited due to the lack of protocols for the automatic creation and evolution of ad hoc networks. Recently, a novel P2P protocol named Wi-Fi Direct has been proposed and standardized by the Wi-Fi Alliance to facilitate nearby devices’ interconnection. Wi-Fi Direct provides high-performance direct communication among devices, includes different energy management mechanisms, and is now available in most Android mobile devices. However, the current implementation of Wi-Fi Direct on Android has several limitations, making the Wi-Fi Direct network only be a one-hop ad-hoc network. This paper aims to develop a new framework for multi-hop ad hoc networking using Wi-Fi Direct in Android smart devices. The framework includes a connection establishment protocol and a group management protocol. Simulations validate the proposed framework on the OMNeT++ simulator. We analyzed the framework by varying transmission range, number of hops, and buffer size. The results indicate that the framework provides an eventual 100% packet delivery for different transmission ranges and hop count values. The buffer size has enough space for all packets. However, as buffer size decreases, the packet delivery decreases proportionally.

FirmVulSeeker—BERT and Siamese Network-Based Vulnerability Search for Embedded Device Firmware Images

In recent years,with the development of the natural language processing(NLP)technologies,security analyst began to use NLP directly on assembly codes which were disassembled from binary executables in order to examine binary similarity,achieved great progress.However,we found that the existing frameworks often ignored the complex internal structure of instructions and didn’t fully consider the long-term dependencies of instructions.In this paper,we propose firmVulSeeker—a vulnerability search tool for embedded firmware images,based on BERT and Siamese network.It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus.Then,a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage.Finally,it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it’s a real vulnerability manually.We evaluate the accuracy,robustness,scalability and vulnerability search capability of firmVulSeeker.Results show that it can greatly improve the accuracy of matching semantically similar functions,and can successfully find more real vulnerabilities in real-world firmware than other tools.

The Design of Management Software for Network Device Faults

Effective network management software ensures networks to run credibly. In this paper we discuss the design and implementation of network device fault management based on Pure Java. It includes designs of general functions, server functions, client functions and a database table. The software can make it convenient to monitoring a network device, and improve network efficiency.

一种Device-to-Device移动社交网络中的热点文件预测方法

随着信息技术的蓬勃发展,移动社交网络越来越受到人们的关注,尤其是对移动社交网络中的热点分析.以基于Device-to-Device技术构建的移动社交网络为研究对象,从网络规模、文件分享特征以及用户影响力等方面入手,提出一种预测该类社交网络中热点文件的方法.该方法从信息和用户的角度出发,通过量化文件重要性、文件敏感性、用户参与力和用户影响力四个指标,利用多元线性回归方法建立预测模型,并在真实数据上验证了方法的有效性.

基于SIR的Device-to-Device移动社交网络信息传播模型

智能移动设备的迅速普及在加快移动社交网络发展的同时,也给底层通信网络带来了沉重的负担。为了缓解底层通信网络的负担,越来越多的社交网络应用开始采用Device-to-Device技术传播信息。本文以Device-to-Device移动社交网络为研究对象,首先分析该网络中用户参与信息传播的特点,然后在传染病模型的基础上构建适合此网络特点的信息传播模型,并利用该模型分析真实Device-to-Device环境中的信息传播过程。结果表明,Device-to-Device移动社交网络中的信息传播与基于Internet的社交网络中的信息传播有相似之处;但是,由于Device-to-Device移动社交网络存在较大的传播时延,信息在该网络中需要较长的时间才能达到传播高峰。

Domain adaptive methods for device diversity in indoor localization

To solve the problem of variations in radio frequency characteristics among different devices,transfer learning is applied to transform device diversity to domain adaptation in the indoor localization algorithm.A robust indoor localization algorithm based on the aligned fingerprints and ensemble learning called correlation alignment for localization(CALoc)is proposed with low computational complexity.The second-order statistical properties of fingerprints in the offline and online phase are needed to be aligned.The real-time online calibration method mitigates the impact of device heterogeneity largely.Without any time-consuming deep learning retraining process,CALoc online only needs 0.11 s.The effectiveness and efficiency of CALoc are verified by realistic experiments.The results show that compared to the traditional algorithms,a significant performance gain is achieved and that it achieves better positioning accuracy with a 19%improvement.

Accurate Distance Estimation for VANET Using Nanointegrated Devices

Vehicular Ad-Hoc Networks (VANET) is a research venue that promises for many useful applications. Most of these applications require a precise real-time positioning system for each vehicle. However, practically the existing tecniques are still not accurate and hence not suitable for some critical applications. In this paper, we will focus on the most critical ones which are the collision avoidance, and collision warning, or lane-tracking. Collision occurs when the distance between nearby vehicles decreases rapidly. Hence, an accurate and precise knowledge of the distance among each vehicle and all the surrounding vehicles has to be obtained to enable a realistic collision avoidance service. We propose to use the carbon nanotube network (CNT) integrated with other nano-devices that can provide accuracy in the order of millimeters. In this paper, theoretical investigations and mathematical formulations are presented. The obtained results show the effectiveness and accuracy of the proposed methodology.

ANN-Based Identification of Steady-State Behavior Parameters of Composite Power Semiconductor Device Model

The paper describes the application of an ANN based approach to the identification of the parameters relevant to the steady state behavior of composite power electronic device models of circuit simulation software. The identification of model parameters of IGBT in PSPICE using BP neural network is illustrated.

A Lightweight Anonymous Device Authentication Scheme for Information-Centric Distribution Feeder Microgrid

Distribution feeder microgrid(DFM)built based on existing distributed feeder(DF),is a promising solution for modern microgrid.DFM contains a large number of heterogeneous devices that generate heavy network traffice and require a low data delivery latency.The information-centric networking(ICN)paradigm has shown a great potential to address the communication requirements of smart grid.However,the integration of advanced information and communication technologies with DFM make it vulnerable to cyber attacks.Adequate authentication of grid devices is essential for preventing unauthorized accesses to the grid network and defending against cyber attacks.In this paper,we propose a new lightweight anonymous device authentication scheme for DFM supported by named data networking(NDN),a representative implementation of ICN.We perform a security analysis to show that the proposed scheme can provide security features such as mutual authentication,session key agreement,defending against various cyber attacks,anonymity,and resilience against device capture attack.The security of the proposed scheme is also formally verified using the popular AVISPA(Automated Validation of Internet Security Protocols and Applications)tool.The computational and communication costs of the proposed scheme are evaluated.Our results demonstrate that the proposed scheme achieves significantly lower computational,communication and energy costs than other state-of-the-art schemes.

On One Model of Complex Technical Queuing System with Unreliable Devices and with Time Redundancy

The given article deals with the development of analytical model of request service process by multichannel technical system with unreliable, repaired and reconfigured service facilities. It is assumed that the system is functioning in service mode of random length random request flows. The system considers the existence of time redundancy for afterservice of calls, the service of which is interrupted with refusal, non-depreciating the performed part of the task. Special probability functions are introduced which on the basis of probability reasoning allow to make the systems of integral equations describing the dynamics of request service process.

无线Device Net控制系统在移载车中的应用

根据以往运输线设计理念和使用效果,设计人员综合客户和创新设计要求,将铅阳极板运输线的运输小车采用无线Device Net网络与PLC之间的通讯方式,一改传统的有线网络模式,避免了有线网络的诸多弊端,此方案设计新颖,通讯快速灵敏,维护简洁方便。