Network Server Research Articles

Blockchain-based batch authentication protocol for Internet of Vehicles

The vehicles in Internet of Vehicles (IoV) can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities in an IoV-based smart city deployment. To address this issue, we aim to design a novel blockchain-enabled batch authentication scheme in Artificial Intelligence (AI)-envisioned IoV-based smart city deployment. The latest trends and revolutions in technologies incorporate AI/Machine Learning (ML) in blockchaining to produce a secure, efficient and intelligent blockchain based system. The data stored in the blocks in the blockchain are authentic and genuine, which makes the AI/ML algorithms to work at their exceptions in order produce correct predictions on the blockchain data. Through the signing phase of the proposed scheme, each vehicle in a dynamically formed cluster broadcasts a message to its own member and respective road-side unit (RSU). In the proposed scheme, two types of authentication take place: vehicle to vehicle (V2V) authentication allows a vehicle to authenticate its neighbor vehicles in its cluster, while batch authentication permits a group of cluster vehicles to be authenticated by their R S U . At the end, a group key is established among the vehicles and R S U in their cluster. R S U then gathers securely data from its vehicles and form several transactions including the information of vehicles and its own given information to the cluster member vehicles. The transactions are formed later by the nearby fog server associated with R S U and then by the cloud server to form a complete block. The created blocks are mined by the cloud servers in a Peer-to-Peer (P2P) cloud server network through the voting-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm. The authentic and genuine data of the blockchain are utilized for Big data analytics through AI/ML algorithms. It is shown that the proposed scheme is highly robust against various attacks through formal and informal security analysis, and also through formal security verification tool. A detailed comparative analysis reveals that the proposed scheme achieves superior security and functionality features, and offers comparable storage, communication and computational costs as compared to other existing competing schemes. Finally, the blockchain implementation has been carried out on the proposed scheme to show its effectiveness.

The Conditional Reliability Evaluation of Data Center Network BCDC

Abstract As the number of servers in a data center network (DCN) increases, the probability of server failures is significantly increased. Traditional connectivity is an important metric to measure the reliability of DCN. However, the traditional connectivity of a DCN based on the condition of arbitrary faulty servers is generally lower. Therefore, it is important to increase the connectivity of a DCN by adding some limited conditions for the faulty server set. As a result, $g$-restricted connectivity and $h$-extra connectivity, which are two crucial subjects for a DCN’s ability to tolerate faulty servers, were proposed in the literature. In this paper, we study the $g$-restricted connectivity and $h$-extra connectivity of a new server-centric DCN, called BCDC, based on crossed cube with excellent performance. We prove that the $g$-restricted connectivity of BCDC is 4 for $n=3$ and $2n+g(n-2)-2$ for $n\geq 4$, where $0\leq g\leq n-3$, and the $h$-extra connectivity of BCDC is 4 for $n=3$ and $2n+h(n-2)-2$ for $n\geq 4$, where $0\leq h\leq n-3$.

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges.

Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions.

Mechanisms of public management in the personal data protection in smart cities

More than ten years ago in the world has emerged the concept “smart city” and till today smart cities have developed in many countries of the world.A smart city is a place where traditional networks and services are made more efficient with the use of digital and telecommunication technologies for the benefit of its inhabitants and business [1].Any smart city, in turn, consists of a huge amount of cyber physical systems (CPSs).Cyber physical systems are smart systems that include engineered interacting networks of physical and computational components [2].The essential requirements to functioning of these systems are safety, security, reliability, resilience, confidentiality. Automated decision-making, including profiling in the personal data processing constitutes a significant part of cyber physical systems as components of smart cities.As the personal data is processed automatically, computations are performed in the “cloud” in the network of distant servers, there is a risk of various cyber threats and real cyber attacks on certain cyber physical system (as part of a smart city) which may cause a damage or losses to the personal data subject as a result of unlawful destruction, use, alteration or disclosure of the personal data.The Ukrainian legislation does not contain the concept “smart city”. The unsolved problem in the Ukrainian legislation is the recognition of the personal data subject as a central element of any cyber physical system (as part of a smart city) and the necessity of ensuring balance between the fundamental rights and freedoms of the personal data subject and the rights of the data possessors, data processors, data protection officials, the authority of the Commissioner of the Verkhovna Rada of Ukraine on human rights who gain access to the personal data by means of personal data subject consent.The comparative analysis of the Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (Regulation) [5] with the Ukrainian Law [4] which refers to various elements of the processing of personal data in different processing systems, to the rights and obligations of the data controller, the data processor, the supervisory authority with regard to ensuring lawful, secure, confidential processing of personal data is made.

A distributed sensor-fault detection and diagnosis framework using machine learning

The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resources of the sensors. More importantly, the detection of faults is time-sensitive, whereas the diagnosis does not necessarily need to be fast. We propose a distributed sensor-fault detection and diagnosis system based on machine learning algorithms where the fault detection block is implemented in the sensor in order to achieve output immediately after data collection. This block consists of an auto-encoder to transform the input signal into a lower-dimensional feature vector, which is then provided to a Support Vector Machine (SVM) for classification as normal or faulty. Once detected, fault diagnosis is performed at a central node, such as a network server, to reduce the computational load on the sensor. In this work, a Fuzzy Deep Neural Network (FDNN) is used for diagnosis to provide further information, such as the type of fault. Here, the input propagates through a deep neural network and a fuzzy representation process. The output of these two components is then fused through densely connected layers. This multi-modal technique learns high-level representations in the data that are missed by conventional methods. To assess the performance of the proposed model, we utilize data obtained from a healthy temperature-to-voltage converter that are then injected with five types of fault: drift, bias, precision degradation, spike, and stuck faults. The performance from fault detection is analyzed in terms of detection accuracy, area under the ROC curve (AUC-ROC), false positive rate, and F1 score. Furthermore, the efficiency of fault diagnosis is shown by the classification accuracy parameter. The experimental results show the efficiency of the proposed fuzzy learning-based model over classic neuro-fuzzy and non-fuzzy learning approaches.

A decentralized prediction market platform based on blockchain and masternode technologies

Prediction market can help to trade the outcome of events, which is very useful for risk hedging, especially when people are making business ventures. However, the traditional prediction market is centralized, which means the platform is completely controlled by just one person or team. This centralization has bad influence on the platform to obtaining trust from massive users. So it is important for the prediction market industry to decentralize the traditional platform into a decentralized one, and get its control from few person and give it back to a lot of people in the prediction market community. Blockchain is a very promising technology to make it true as it is a popular decentralizing technology. However, existing blockchain technologies like Bitcoin or Ethernum bring about new problems like high energy consumption, expensive fee, and very low system capacity, which is not suitable for the current prediction market. To solve these problems, we propose to combine masternode technology together with blockchain to serve as a decentralized node, and each masternode is deployed and running on an edge server in mobile edge network. The network of master-nodes serves together as the decentralized prediction market platform. As we know, it is not easy to run a masternode. So the number of masternodes is much less than the number of complete nodes of the blockchain. As a result, it is easier to reach the consensus among these masternodes. Theoretical analysis and experimental results show that our proposed method is useful.

The Age of Information in Networks: Moments, Distributions, and Sampling

A source provides status updates to monitors through a network with state defined by a continuous-time finite Markov chain. An age of information (AoI) metric is used to characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, first order linear differential equations are derived for the temporal evolution of both the moments and the moment generating function (MGF) of the age vector components. It is shown that the existence of a non-negative fixed point for the first moment is sufficient to guarantee convergence of all higher order moments as well as a region of convergence for the stationary MGF vector of the age. The stationary MGF vector is then found for the age on a line network of preemptive memoryless servers. From this MGF, it is found that the age at a node is identical in distribution to the sum of independent exponential service times. This observation is then generalized to linear status sampling networks in which each node receives samples of the update process at each preceding node according to a renewal point process. For each node in the line, the age is shown to be identical in distribution to a sum of independent renewal process age random variables.

Sabic to boost resin for circuit boards

Sabic says it intends to nearly double capacity in Asia for its Noryl SA9000 resin, used in printed circuit boards for 5G cellular network base stations and servers. The project, at a facility in I...

A Novel Collision-Aware Adaptive Data Rate Algorithm for LoRaWAN Networks

Low-power wide-area network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long-range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an adaptive data rate (ADR) algorithm to allocate proper data rates to end devices (EDs). However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to EDs one-by-one in an independent way. In this article, we propose a novel and more efficient ADR algorithm, denoted as collision-aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of EDs in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation–experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement with respect to the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated, and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.

A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON

Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.

Trading off Between User Coverage and Network Robustness for Edge Server Placement

Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on edge servers attached to base stations or access points in close proximity to mobile users. From the edge infrastructure provider’s perspective, a cost-effective <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge server placement ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP) aims to place <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge servers within a particular geographic area to maximize the number of covered mobile users, i.e., to maximize the <i>user coverage</i> . However, in the distributed and volatile ECC environment, edge servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Mobile users connected to a failed edge server have to access services in the remote cloud if they are not covered by any other edge servers. This significantly impacts mobile users quality of experience. Thus, the robustness of the edge server network (referred to as network robustness hereafter) in a specific area must be considered in edge server placement. In this article, we formally model this joint user coverage and network robustness oriented <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> edge server placement ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR) problem, and prove that finding the optimal solution to this problem is <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hard. To tackle this ESP-CR, we first propose an integer programming based optimal approach (namely ESP-O) for finding optimal solutions to small-scale <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR problems. Then, we propose an approximation approach, namely ESP-A, for solving large-scale <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> ESP-CR problems efficiently and theoretically prove its approximation ratio. Finally, the performance of these two approaches are experimentally evaluated against three representative approaches on a widely-used real-world dataset.

Information System Strategic Planning in PT XYZ Using Wetherbe's Methodology

Along with the development of technology in business, PT XYZ, which is engaged in the food industry, which is located in Sidomukti, Salatiga City is required to be adaptive and innovative by utilizing the availability of information technology. In order to win the competition, and information system strategic plan is needed to support the implementation of information technology and information systems in terms of operations as well as for strategic ones. Therefore, this research conducted by making strategic planning of information systems for use at PT XYZ using the Wetherbe methodology. This methodology is considered as an appropriate approach tool for the company. The solution for information system requirements is based on the strategic planning analysis that has been carried out, namely, producing nine information systems consisting of one information system to be developed from existing applications and eight information systems for the proposed application. And IT personnel are needed for the database and network server work or to carry out proper training for each employee so that if there is damage to the system and infrastructure it can be directly handled. This was done due to the condition of the number of human resources and limited funds for the strategic planning of information systems.

An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

A Client Bootstrapping Protocol for DoS Attack Mitigation on Entry Point Services in the Cloud

This paper presents a client bootstrapping protocol for proxy-based moving target defense system for the cloud. The protocol establishes the identity of prospective clients who intend to connect to web services behind obscure proxy servers in a cloud-based network. In client bootstrapping, a set of initial line of defense services receive new client requests, execute an algorithm to assign them to a proxy server, and reply back with the address of the chosen proxy server. The bootstrapping protocol only reveals one proxy address to each client, maintaining the obscurity of the addresses for other proxy servers. Hiding the addresses of proxy servers aims to lower the likelihood that a proxy server becomes the victim of a denial-of-service (DoS) attack. Existing works address this problem by requiring the solution of computationally intensive puzzles from prospective clients. This solution slows the progression of attacks as well as new clients. This paper presents an alternative idea by observing that limited capacity of handling initial network requests is the primary cause of denial-of-service attacks. Thus, the suggested alternative is to utilize cost-effective high-capacity networks to handle client bootstrapping, thus thwarting attacks on the initial line of defense. The prototype implementation of the protocol using Google’s firebase demonstrates the proof of concept for web services that receive network requests from clients on mobile devices.

Improved QoS at the Edge Using Serverless Computing to Deploy Virtual Network Functions

Multiaccess edge computing (MEC) will strengthen forthcoming 5G networks by improving the Quality of Service (QoS), in particular, reducing latency, increasing data processing rates, and providing real-time information to develop high-value Internet-of-Things (IoT) services. To enable data-intensive network services and support advanced analytics, many network operators have proposed to integrate MEC systems with network function virtualization (NFV) consolidating virtual network functions (VNFs) and edge capabilities on a shared infrastructure. As of yet, this integration is not fully established, with various architectural issues currently open, even at standardization level. For instance, any update to VNFs deployed in a MEC system requires a time-consuming manual effort, which affects the overall infrastructure operations. To address these pitfalls, VNFs can be decomposed into microservices, which maintain their own states and exhibit different resource consumption requirements. This article presents an approach to integration that leverages serverless computing to merge MEC and NFV at the system level and to deploy VNFs on demand, by combining MEC functional blocks with an NFV orchestrator using a Kubernetes cluster. We further investigate whether the resource utilization of a MEC system can be improved by leveraging networked FPGA-enabled MEC servers, through an extension of the edge layer that takes advantage of available programmable hardware. We quantitatively evaluate and demonstrate the improvement of 75% end-to-end latency, 99.96% VNF execution time, 26.9% resource utilization, and 15.8% energy consumption in comparison with traditional baselines of cloud, edge, and serverless-edge test cases for a high-definition real-time video streaming application.

SELECTING NETWORK MONITORING SYSTEM SOFTWARE WITH ANALYTICAL HIERARCHY PROCESS METHOD

Nowadays there are lots of Network Monitoring System (NMS) software that are reliable and easy to use, such as CiscoWork, HP-OpenView, and IBM-Tivoli. It is just that the software is quite expensive, because it has classified as a commercial product. Fortunately, availability of NMS products is not limited to commercial, but also many other alternatives, namely products that classified as Free and Open Source Software (FOSS). Unfortunately, most of these FOSS products are not only difficult to implementation both in term of installation and configuration, but also limitations in the number of nodes and types of monitored, including network devices, servers, and applications. The method of selecting FOSS-based NMS software based on the Oyku Alanbay and ISO 7498-4 research methods through the Analytical Hierarchy Process (AHP) approach using the Expert Choice tool. Among GroundWork, Hyperic, Nagios, OpenNMS, and Zenoss, based on the results of this study put Nagios as a fair reliable software for monitoring networks, servers, and applications.

User-level Threading

An important class of computer software, such as network servers, exhibits concurrency through many loosely coupled and potentially long-running communication sessions. For these applications, a long-standing open question is whether thread-per-session programming can deliver comparable performance to event-driven programming. This paper clearly demonstrates, for the first time, that it is possible to employ user-level threading for building threadper- session applications without compromising functionality, efficiency, performance, or scalability. We present the design and implementation of a general-purpose, yet nimble, user-level M:N threading runtime that is built from scratch to accomplish these objectives. Its key components are efficient and effective load balancing and user-level I/O blocking. While no other runtime exists with comparable characteristics, an important fundamental finding of this work is that building this runtime does not require particularly intricate data structures or algorithms. The runtime is thus a straightforward existence proof for user-level threading without performance compromises and can serve as a reference platform for future research. It is evaluated in comparison to event-driven software, system-level threading, and several other user-level threading runtimes. An experimental evaluation is conducted using benchmark programs, as well as the popular Memcached application. We demonstrate that our user-level runtime outperforms other threading runtimes and enables thread-per-session programming at high levels of concurrency and hardware parallelism without sacrificing performance.

Multi-Armed Bandit Learning for Computation-Intensive Services in MEC-Empowered Vehicular Networks

Mobile edge computing (MEC) is an emerging paradigm to offload computations from the cloud to the MEC servers in vehicular networks, aiming at better supporting computation-intensive services with requirements of low latency and real-time processing. In this work, we investigate a new service scenario of computation offloading and workload balancing in MEC-empowered vehicular networks, where the computational resources of MEC/cloud servers are cooperatively utilized. Then, we formulate a distributed task assignment (DTA) problem by considering heterogeneous computation resources, high mobility of vehicles and uneven distribution of workloads, targeting at optimizing task assignment among MEC/cloud servers and minimizing task completion time. We prove that the DTA is NP-hard. Further, we propose a multi-armed bandit learning algorithm called Utility-table based Learning. For workload balancing among MEC servers, a utility table is established to determine the optimal solution by online learning of real-time workload distribution, which is updated based on the feedback signal of task assignment. For optimal computation offloading, a theoretical bound is derived to determine the ratio of workload assigned to the cloud. Lastly, we build the simulation model and conduct an extensive experiment, which demonstrates the superiority of the proposed algorithm.

Machine Learning Approach to Task Scheduling in Cloud Computing

Cloud computing provides on demand service on internet using network of remote servers. The pivotal role for any cloud environment would be to schedule tasks and the virtual machine scheduling have key role in maintaining Quality of Service (QOS) and Service Level Agreement (SLA). Task scheduling is the process of scheduling task (user requests) to certain resources and it is an NP-complete problem. The primary objectives of scheduling algorithms are to minimize makespan and improve resource utilization. In this research work an attempt is made to implement Artificial Neural Network (ANN), which is a methodology in machine learning technique and it is applied to implement task scheduling. It is observed that neural network trained with genetic algorithm will outperforms default genetic algorithm by an average efficiency of 25.56%.