Commercial Networks Research Articles

Notes on the birds collected by Giovanni Emilio Cerruti during his journey to New Guinea (1869–1870)

In the later decades of the nineteenth century, some Italian explorers carried out expeditions to the Indonesian archipelago and New Guinea, with the aims of concluding diplomatic agreements, strengthening commercial networks and obtaining territorial concessions. During these expeditions, collecting animal specimens and artefacts, later deposited in several museums, was fairly common, increasing the scientific renown of post-unitarian Italy. Giovanni Emilio Cerruti ( fl. 1860–1875) was a traveller, little known today, who visited those then remote lands. He was a merchant and a strong supporter of colonial policies. Through the mediation of government officials, he managed to obtain a commission from the Italian government to identify one or more places close to New Guinea where a penal colony could be established. Although the trip was commissioned exclusively for diplomatic purposes, Cerruti collected some interesting ornithological specimens, which were later donated to Italian natural history museums. We reconstruct Emilio Cerruti’s journey, using his letters to illuminate the most interesting details of its natural history. Fifty of Cerruti’s specimens, extant in the collections of the Museo di Scienze Naturali di Torino and Museo Calderini di Varallo Sesia, are recorded in an annotated catalogue.

Watching your call: Breaking VoLTE Privacy in LTE/5G Networks

Voice over LTE (VoLTE) and Voice over NR (VoNR), are two similar technologies that have been widely deployed by operators to provide a better calling experience in LTE and 5G networks, respectively. The VoLTE/NR protocols rely on the security features of the underlying LTE/5G network to protect users' privacy such that nobody can monitor calls and learn details about call times, duration, and direction. In this paper, we introduce a new privacy attack which enables adversaries to analyse encrypted LTE/5G traffic and recover any VoLTE/NR call details. We achieve this by implementing a novel mobile-relay adversary which is able to remain undetected by using an improved physical layer parameter guessing procedure. This adversary facilitates the recovery of encrypted configuration messages exchanged between victim devices and the mobile network. We further propose an identity mapping method which enables our mobile-relay adversary to link a victim's network identifiers to the phone number efficiently, requiring a single VoLTE protocol message. We evaluate the real-world performance of our attacks using four modern commercial off-the-shelf phones and two representative, commercial network carriers. We collect over 60 hours of traffic between the phones and the mobile networks and execute 160 VoLTE calls, which we use to successfully identify patterns in the physical layer parameter allocation and in VoLTE traffic, respectively. Our real-world experiments show that our mobile-relay works as expected in all test cases, and the VoLTE activity logs recovered describe the actual communication with 100% accuracy. Finally, we show that we can link network identifiers such as International Mobile Subscriber Identities (IMSI), Subscriber Concealed Identifiers (SUCI) and/or Globally Unique Temporary Identifiers (GUTI) to phone numbers while remaining undetected by the victim.

Mean–variance scaling and stability in commercial sex work networks

Mean–variance scaling and stability in commercial sex work networks

Reliable Wireless Networking via Soft-Source Information Combining

This article puts forth a multistream networking paradigm, referred to as soft-source-information-combining (SSIC), to support wireless Internet of Things (IoT) applications with ultrareliability requirements. For SSIC networking, an SSIC dispatcher at the source dispatches duplicates of packets over multiple streams, which may be established over different physical wireless networks. If a packet on a stream cannot be decoded due to wireless interference or noise, the decoder makes available the packet’s soft information. An aggregator then combines the soft information of the duplicates to boost reliability. Of importance are two challenges: 1) how to descramble the scrambled soft information from different streams to enable correct SSIC and 2) the construct of an SSIC dispatching and aggregation framework compatible with commercial network interface cards (NICs) and TCP/IP networks. To address the challenges, we put forth: 1) a soft descrambling (SD) method to minimize the bit-error rate (BER) and packet-error rate (PER) at the SSIC’s output and 2) an SSIC networking architecture readily deployable over today’s TCP/IP networks without specialized NICs. For concept proving and experimentation, we realized an SSIC system over two Wi-Fi’s physical paths in such a way that all legacy TCP/IP applications can enjoy the reliability brought forth by SSIC without modification. Experiments over our testbed corroborate the effectiveness of SSIC in lowering the packet delivery failure rate and the possibility of SSIC in providing 99.99% reliable packet delivery for short-range communication.

Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.

IPaaS: Intelligent Paging as a Service

Paging is a critical operation in 5G mobile networks for localization of User Equipment (UE) in idle mode. Current multistep paging method increases the signaling exponentially and delay linearly, and analysis of a commercial Access and Mobility management Function (AMF) reports that paging accounts for 27.9% of AMF total traffic. To reduce signaling overhead and service delay by increasing the paging success rate, this paper presents a Deep Learning (DL) driven intelligent Paging as a Service (iPaaS) over a distributed NWDAF architecture based on serverless computing. iPaaS clusters UEs based on their movement patterns and feed cluster ID, movement history, and elapsed time in idle mode of a UE into DL model for UE localization prediction. Prediction results are used in two proposed schemes: iPaaS for Reduced Signaling (iPRS) and iPaaS for Reduced Delay (iPRD), which focus on cutting down signaling overhead and delay, respectively. Performance of iPaaS with two proposed schemes is evaluated on 110 million handover logs that are collected over 15 days from a commercial 5G network. Results show that iPRS reduces the signaling overhead up to 94% and iPRD reduces the delay up to 43% comparing to current method.

Big Data Application in Urban Commercial Center System Evaluation

Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.

A Data Processing and Distribution System Based on Apache NiFi

The monitoring of physical and logical networks is essential for the high availability of 5G/6G networks. This could become a challenge in 5G/6G deployments due to the heterogeneity of the optical layer. It uses equipment from multiple vendors, and, as a result, the protocols and methods for gathering monitoring data usually differ. Simultaneously, to effectively support 5G/6G networks, the optical infrastructure should also be dense and ensure high throughput. Thus, vast numbers of photonic transceivers operating at up to 400 Gbps are needed to interconnect network components. In demanding optical solutions for 5G and beyond, enterprise-class equipment will be used—for example, high-capacity and high-density optical switches based on the SONiC distribution. These emerging devices produce vast amounts of data on the operational parameters of each optical transceiver, which should be effectively collected, processed, and analyzed. The aforementioned circumstances may lead to the necessity of using multiple independent monitoring systems dedicated to specific optical hardware. Apache NiFi can be used to address these potential issues. Its high configurability enables the aggregation of unstandardized log files collected from heterogenous devices. Furthermore, it is possible to configure Apache NiFi to absorb huge data streams about each of the thousands of transceivers comprising high-density optical switches. In this way, data can be preprocessed by using Apache NiFi and later uploaded to a dedicated system. In this paper, we focus on presenting the tool, its capabilities, and how it scales horizontally. The proven scalability is essential for making it usable in optical networks that support 5G/6G networks. Finally, we propose a unique optimization process that can greatly improve the performance and make Apache NiFi suitable for high-throughput and high-density photonic devices and optical networks. We also present some insider information on real-life implementations of Apache NiFi in commercial 5G networks that fully rely on optical networks.

Analysis of balanced, unbalanced, sinusoidal and nonsinusoidal three-phase systems through a Python developed tool

This paper proposes a Python tool for the analysis of balanced, unbalanced, sinusoidal and nonsinusoidal three-phase systems based on the IEEE Std. 1459. The proposed tool was developed to be used by electrical engineers and professionals of related careers providing a free access for electrical power calculations that allows validation and power analysis in power systems. The proposed tool is available for use on a GitHub link and only requires voltage and current signals as inputs, making it possible to dispense expensive commercial network analyzers. The tool is made of two interfaces, one for making power calculations of sinusoidal three-phase systems and the other one for making calculations of nonsinusoidal three-phase systems; both interfaces can be accessed through a main menu. The proposed tool facilitates decision-making in electrical system issues, which helps improving the efficiency and power quality of electrical systems.

Administering Disseminations: Film Exhibition and Censorship During the Great War in India

ABSTRACT This article explores the debates on film exhibition and censorship during the war years in India. The First World War (1914–18) was a watershed moment for the rapidly burgeoning cinema exhibition business in India. During the war, the government of India was caught by surprise at the pace with which the business had spread in the country. When the need for cinema propaganda arose in India, the government began to assess the nature of commercial networks the cinema exhibition companies had been able to build. Throughout the war years, the Government of India debated ways of intervening in the cinema business either by competing with the monopolistic cinema companies or by introducing censorship laws to control exhibition spaces and content. At the behest of the war propaganda department in London, the government of India found itself playing a reluctant arbitrator in a bitter competition between two influential cinema companies – Madan and Bandman – for the exhibition of Britain Prepared. This article further draws attention to the continuities between pre-war censorship debates and the war-year debates that led to the enactment of the first Cinematograph Act (1918) in India. By exploring the pre-war censorship debate about the exhibition of Johnson-Jeffries film, this article reviews a commonly held assumption in Indian film history that film censorship began in the country during the war years.

Design, Implementation, and Empirical Validation of a Framework for Remote Car Driving Using a Commercial Mobile Network.

Despite the fact that autonomous driving systems are progressing in terms of their automation levels, the achievement of fully self-driving cars is still far from realization. Currently, most new cars accord with the Society of Automotive Engineers (SAE) Level 2 of automation, which requires the driver to be able to take control of the car when needed: for this reason, it is believed that between now and the achievement of fully automated self-driving car systems, there will be a transition, in which remote driving cars will be a reality. In addition, there are tele-operation-use cases that require remote driving for health or safety reasons. However, there is a lack of detailed design and implementation available in the public domain for remote driving cars: therefore, in this work we propose a functional framework for remote driving vehicles. We implemented a prototype, using a commercial car. The prototype was connected to a commercial 4G/5G mobile network, and empirical experiments were conducted, to validate the prototype's functions, and to evaluate its performance in real-world driving conditions. The design, implementation, and empirical evaluation provided detailed technical insights into this important research and innovation area.

Hui Corporate Strategies and Islamic Cultural Capital and in Qing Guangzhou

Abstract This article adapts methods from the study of late imperial Chinese society to understand Hui families’ pursuit of Islamic cultural capital in Qing Guangzhou. It outlines three processes that crystallized by the middle of the nineteenth century: the integration of Guangzhou’s mosques into regional and long-distance commercial networks; the institutionalization of Islamic education at the city’s mosques; and the rationalization of mosque management. Through an analysis of mosque inscriptions and three Hui genealogies, it shows that the local development of Islamic learning and the organization of mosque authority were linked to the wider social field in which Hui families competed for status.

Identifying the critical factors of transmission efficiency loss in China’s natural gas network

China’s market-oriented reform supports the sustainable development of energy mix and the low-carbon target, and natural gas has bridged the transition from traditional fossil energy to clean and renewable energies. The third-party access policy, launched recently by China’s natural gas market, drives the decouple between gas trade and transport. The decouple might lead to the transmission resources of physical network not optimally used, which is caused by the contractual arrangement between entry and exit capacities in commercial network. Aiming at this issue, we established a mathematical programming with equilibrium constraints (MPEC) to integrate the allocations of commercial capacity and physical flows, based on a minimum cost maximum flow problem (MCMF) abstracted from China’s existing gas network. The MPEC model was then used to strategically evaluate the transmission efficiency, and identify the critical factors of its loss. Our results show that there is transmission efficiency loss of China’s gas network from the shortage of geospatial gas supply and the invisible segmentation of gas network due to interdicted cost of pipeline, bottleneck of pipeline capacity and economic radius of gas supply chains to transport gas. Therefore, the critical factor of the loss to be identified will be helpful for strategically reducing the cost of decoupling gas trade and transport.

Practical Performance Analysis of Interference in DSS System

The 5G network is developing rapidly. However, due to spectrum resource limitation, it is expected to use the 5G network to ensure high resource utilization and network efficiency, while keeping part of 4G in the same band for existing 4G users. Dynamic spectrum-sharing (DSS) technology enables 4G/5G wireless networks to coexist in scarce spectrum resources and dynamically allocates spectrum resources in the same band. 4G/5G DSS has been successfully commercialized in some countries such as Germany and Brazil. However, complex 4G/5G DSS networks will introduce intra-frequency interference in the inter-system, which will affect network performance. Therefore, we innovatively proposed two interference mitigation schemes: buffer setting and rate matching. Furthermore, we have verified the practical performance of both schemes in a commercial network for the first time to determine the feasibility of the schemes. From theory, simulation, and practical analysis, both schemes can effectively mitigate the interference of the inter-system introduced by DSS: increasing the network rate by 60% in the interference environment and improving the user experience in the DSS architecture.

Explainable machine learning for performance anomaly detection and classification in mobile networks

Mobile communication providers continuously collect many parameters, statistics, and key performance indicators (KPIs) with the goal of identifying operation scenarios that can affect the quality of Internet-based services. In this regard, anomaly detection and classification in mobile networks have become challenging tasks due to both the huge number of involved variables and the unknown distributions exhibited by input features. This paper introduces an unsupervised methodology based on both a data-cleaning strategy and explainable machine learning models to detect and classify performance anomalies in mobile networks. Specifically, this methodology dubbed explainable machine learning for anomaly detection and classification (XMLAD) aims at identifying features and operation scenarios characterizing performance anomalies without resorting to parameter tuning. To this end, this approach includes a data cleaning stage that extracts and removes outliers from experiments and features to train the anomaly detection engine with the cleanest possible dataset. Moreover, the methodology considers the differences between discretized values of the target KPI and labels predicted by the anomaly detection engine to build the anomaly classification engine which identifies features and thresholds that could cause performance anomalies. The proposed methodology incorporates two decision tree classifiers to build explainable models of anomaly detection and classification engines whose decision structures recognize features and thresholds describing both normal behaviors and performance anomalies. We evaluate the XMLAD methodology on real datasets captured by operational tests in commercial networks. In addition, we present a testbed that generates synthetic data using a known TCP throughput model to assess the accuracy of the proposed approach.

Hunting for Insider Threats Using LSTM-Based Anomaly Detection

Insider threats are one of the most difficult problems to solve, given the privileges and information available to insiders to launch different types of attacks. Current security systems can record and analyze sequences from a deluge of log data, potentially becoming a tool to detect insider threats. The issue is that insiders mix the sequence of attack steps with valid actions, reducing the capacity of security systems to programmatically detect the attacks. To address this shortcoming, we introduce LADOHD, an anomaly detection framework based on Long-Short Term Memory (LSTM) models, which learns the expected event patterns in a computer system to identify attack sequences even when attacks span for a long time. The applicability of the framework is demonstrated on a dataset of 38.9 million events collected from a commercial network of 30 computers over twenty days and where a 4-day long insider threat attack occurs. Results show that LADOHD outperforms the anomaly detection system used to protect the commercial network with a True Positive Rate of 97.29% and a False Positive Rate of 0.38%. Experiments also show that LSTMs have higher prediction precision in variable-length sequences than methods like Hidden Markov Models, a crucial requirement in sequence-analysis-based anomaly detection techniques.

A Novel Compact Broadband SIW Six-Port Junction for Angle-of-Arrival Applications

A compact broadband SIW six-port junction based on a novel topology and its application in AoA (angle-of-arrival) finding system are proposed in this paper. The miniaturization design philosophy of the six-port junction is based on the change of the conventional topology rather than the special structure of the discrete component or the advantage of the fabrication technology. The proposed six-port junction is constructed by two multi-step H-plane Hybrid couplers, one complementary CSRR (split-ring resonator)-loaded wideband 90 ° phase shifter, two power dividers, and two extra phase shifters of arbitrary phase. The additional phase differences introduced by the extra phase shifters don’t deteriorate the application performance based on the S-parameters requirement of the six-port junction design. The power divider and the adjacent extra phase shifter are realized by one E-plane 3dB coupler with one port short by metalized holes. The combination of E-plane and H-plane coupler significantly reduces the volume of the entire six-port junction. The fabrication of the prototype is measured by a commercial vector network analyzer and shows a good agreement with the simulation results. The fractional working bandwidth is 32.2% at 7.15 GHz and more than 20% volume is reduced compared with other designs with comparable bandwidth. Besides, to directly verify the availability of the novel topology, a simplified six-port AoA finding system utilized the proposed six-port junction is realized. The maximum average error of the measured incident angle in the entire bandwidth is no larger than 1.65°, which is comparable to the traditional system. However, it’s worth to be noted that the proposed structure can make a considerable reduction of volume and can be utilized widely for the miniaturization design of the six-port measurement systems.

HACM: High Availability Control Method in Container-Based Microservice Applications Over Multiple Clusters

Emerging cloud-native technologies, such as container runtime and container orchestrator, offer unprecedented agility in developing and running applications, especially when combined with microservice-style architecture. Several commercial Samsung Network products such as Samsung Element Management System (S-EMS), 5G Radio Access Network (RAN) & Core network elements are being redesigned to fit the microservice paradigm. The cloud environment allows enterprises to scale their applications on-demand with minimum cost; however, it is often difficult to use containers without sacrificing the many benefits offered by container technology. S-EMS manages 5G RAN & Core network elements (NEs) deployed nationwide, and systematically stores a huge volume of stateful data per second. Containers are characterized to have an ephemeral state, hence ‘stateful-ness’ aspect of S-EMS makes management more complex. The existing system in a container-based application does not support geo-redundancy where services/data are stateful/state dependent. In this paper, different challenges around geo-redundancy between different independent Kubernetes set up with active and standby modes between the sites where state-dependent data is stored in each site are described. To overcome these challenges, we propose the High Availability Control Method (HACM) - which enables the Kubernetes cluster to be active and standby, where state-dependent data and context-based operations are intrinsically supported by the underlying S-EMS container application. Our approach has been designed to maintain the geo-redundancy philosophy of cloud-native by associating the status of each site using high availability (HA), switching over services based on the health of applications, deciding state when there are conflicts in site state, and the option to auto fallback based on user preference and services are transferred between sites without user intervention with optimized storage that ensures consistency, persistence, reliability, and availability. Through evaluation, we show that HACM with S-EMS can facilitate geo-redundancy HA, while not posing a significant burden on the Cloud.

Burstiness of Interference Pikes in Wireless Networks

The temporal dynamics of interference in wireless networks affects their performance but has only been studied for some cases. This article addresses this gap by analyzing high-interference events, called pikes, concluding that they arrive in bursts in many cases. Specifically, we show that in Poisson networks with random access and multipath fading, the pike interarrival time increases with the interference correlation, irrespective of the source of correlation and burstiness of pikes. To demonstrate the applicability of this theory, we conduct a measurement campaign with an automotive user in different commercial 4G cellular networks. The experimental results indicate that interference pikes are bursty in the real world as well.

OPTIMIZING PROACTIVE COLLABORATIVE AND COOPERATIVE RELATIONSHIPS BETWEEN FOOD SUPPLIERS AND RESTAURANTS

Currently, agri-gastronomy and the ”tourist-catering” subsector have an increasingly well-defined role in the realization and commercialization of goods and services offered in terms of food quality and systemic efficiency. This study aims to identify that tourist destinations and many local communities offer a variety of gastronomic products and services and that the supply sector in semi-structured and still needs improvements in quality, safety and a wider commercial network. The paper highlights the need for suppliers to reinvent themselves and create new market opportunities to benefit from rapid growth in key sectors of the integrated food system. A number of relational solutions are analyzed with the potential to generate an authentic cuisine based on tradition and sustainability, with strong elements of originality. Consequently, it is highlighted that the consolidated and optimized relationships in the producer-processor network may be able to ensure diversified and quality local products, which gives restaurants a competitive advantage in the post-pandemic recovery.