Customer Network Research Articles

An embedded intrusion detection and prevention system for home area networks in advanced metering infrastructure

AbstractWith the widespread adoption of smart metres in the power sector, anomaly detection has become a critical tool for analysing customers' unusual consumption patterns and network traffic. Detecting anomalies in power consumption and communication is primarily a real‐time big data analytics issue regarding data mining along with a vast number of parallel streaming data from smart metres. In this study, an embedded Intrusion Detection and Prevention System (IDPS) is proposed as a Wifi‐based smart metre for Home Area Networks (HANs) in the Advanced Metering Infrastructure (AMI) network. So, the proposed system employs one machine learning model based on IDPS to guard the HAN network from various attacks that utilise the Message Queueing Telemetry Transport protocol between the smart metre and IoT sensors. Also, it uses two machine learning models to detect the abnormality in periodic and daily data metering respectively. So, multiple algorithms have been used to find the suitable algorithm for each of the three anomaly detection models. These models have been evaluated and tested using real data sets regarding resources usage and detection performance to demonstrate the efficiency and effectiveness of using machine learning algorithms in the built anomaly detection models. The experiments show that the anomaly detection models performed well for various abnormalities.

JRCS: Joint Routing and Charging Strategy for Logistics Drones

Unmanned aerial vehicles (UAVs), commonly known as drones, are currently being used to combat the COVID-19 pandemic through applications, including the delivery of medical supplies, aerial spraying, and public space monitoring. In a pandemic, drone-based delivery is a promising and highly efficient method to reduce transportation time, cost, and exposure to infection. However, owing to both the limited battery lifetime and the limited functions of UAV in-flight missions, it is difficult to implement multiple deliveries over long distances in a single transportation mission. In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization methods are required to design the delivery area networks of customers, charging stations, and delivery routes. We propose a joint routing and charging strategy (JRCS) comprising three phases to perform multiple deliveries in a single mission. We first split the customers of a delivery area using a clustering algorithm according to their distance from the nearest charging station and the maximum flight range. The second phase provides flight segmentation and intersegment routes between the depot, customer locations, and charging stations based on the maximum flight range and safe flight distance. The joint consideration of drone routes with charging stations minimizes the number of charging stations and ensures safe delivery. Finally, we formulate mixed-integer linear programming to solve the drone delivery route problem. According to simulation results, the proposed JRCS outperforms existing delivery approaches in terms of various performance metrics.

TMS 38 MADDİ OLMAYAN DURAN VARLIKLARIN MUHASEBELEŞTİRİLMESİ

İşletmenin karlılığının artmasında etkin rol oynayan, işletmeye rekabet avantajı kazandıran, ancak fiziki olarak mevcut olmayan haklara maddi olmayan duran varlık denir. Günümüz şartlarında, dijitalleşme artıkça, işletmelerin varlıkları içerisinde maddi olmayan duran varlıklar gittikçe önem kazanmaktadır. İşletmeler tarafından, artan ticari rekabet şartlarında, varlıklarının devamı ve faaliyetlerinin kesintisiz olarak sürdürülebilmesi için fiziki varlıkların yanı sıra, maddi olmayan varlıklardan da faydalanılmaktadır. Patent, telif hakkı, ithalat kotası, yazılım programı, film, müşteri ve tedarikçi ağı ve bağlantısı, dağıtım sistemi, isim kullanma hakkı, faaliyet hakkı, piyasadaki hâkimiyet durumu gibi varlıklar, Maddi olmayan duran varlıklara örnek olarak verilebilir. Piyasa şartları doğrultusunda bu varlıklara değerleme uygulanarak, varlıkların mali tablolarda doğru bir şekilde yer alması önem kazanmaktadır. İşletmeler tarafından kullanılan bu kaynaklardan, maddi olmayan duran varlıklar sınıfına dâhil olması gereken varlıklar ile bunların muhasebe kayıtlarına alınması, değerlemeye tabi tutulması, yıpranma payı ayrılması, yenileme gideri, değer düşüklüğü uygulaması gibi konular TMS 38 Maddi Olmayan Duran Varlıklar Standardında ayrıntılı olarak düzenlenmiştir.Bu çalışmada, işletmeler tarafından edinilen maddi olmayan duran varlıklar gurubundaki varlıkların, TMS38 Maddi olmayan duran varlıklar standardı hükümlerine göre muhasebeleştirilme usul ve esasları incelenmiştir. Öncelikle; TMS38 standardında sayılan kavramlar hakkında temel bilgiler verilmiş, ardından; maddi olmayan duran varlıkların değerlemesi, muhasebeleştirilmesi, amortismana tabi tutulması ve yapılması gereken diğer işlemler ile ilgili uygulamalar açıklanmış ve uygulama örnekleri verilmiştir.Çalışmada literatür taraması ile sonuca ulaşılarak monografi yöntemi ile uygulama yapılmıştır. Aynı zamanda, maddi olmayan duran varlık kavramı içinde yer alan kalemler incelendiğinde, çalışmanın, ekonomiye yüksek katma değer kazandıracak unsurları içerdiği ve bunların üzerinde önemle durulması gerektiği görülmüştür.

Low-frequency, high-impact: Discovering important rare events from UGC

Rare events are those with a small occurrence probability, but might have a substantial impact. Despite their potential importance, previous research hardly considers rare events because of the difficulty of handling long-tail information. We propose a solution that incorporates a large-scale topic model to extract them from customer opinions and network inference to select high-impact variables. We test our model by analyzing hotel reviews. The result indicates that some of the rare events (e.g., “misleading description”, “home feeling”) can be highly influential to customer ratings or recommendations, which helps marketers sharpen their understanding of consumers and redesign their services accordingly.

Finding Social Networks Among Online Reviewers for Customer Segmentation

Abstract Recently, online user-generated data have emerged as a valuable source for industrial applications. In the consumer product area, many studies analyze online data and draw implications on product design. However, most of them treat online customers as one group with the same preferences, while customer segmentation is a key strategy in conventional market analysis. This paper proposes a new methodology based on text mining and network analysis for online customer segmentation. First, the method extracts customer attributes from online review data. Then, a customer network is constructed based on these attributes and predefined networking rules. For networking, a new concept of “topic similarity” is proposed to reflect social meaning in the customer network. Finally, the network is partitioned by modularity clustering, and the resultant clusters are analyzed to understand segment properties. We validate our methodology using real-world data sets of smartphone reviews. The result shows that the proposed methodology properly reflects the heterogeneity of the online customers in the segmentation result. The practical application of customer segmentation is presented, illustrating how it can help companies design target-customer-oriented products.

State-Dependent Multi-Constraint Topology Configuration for Software-Defined Service Overlay Networks

Service Overlay Network (SON) is an efficient solution for ensuring the end-to-end Quality of Service (QoS) in different real-world applications, including Video-on-Demand, Voice over IP, and other value-added Internet-based services. Although SON offers many advantages, such as ease of deployment and resilience to the node failures, it has to face the challenge of overlay network configuration that needs to dynamically adjust to the change in communication requirements. In this paper, we propose a novel method for adaptive overlay topology configuration, called AOTC based on Software-Defined Networks, deep learning, and reinforcement learning. The intuitive motivation is to address the above challenge, maximize the QoS from two aspects of customer preference and network cost. The obtained experimental results demonstrate the superiority of AOTC. Such a method can significantly reduce network cost while providing an improvement of 50% and 60% in terms of average delay and packet loss rate as compared to other traditional approaches.

Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics

Visual inspection is a main stage of quality assurance process in many applications. In this paper, we propose a new network architecture for detecting the fabric defects based on convolutional neural network. Four different pre-trained and customized model network architectures have compared in terms of performance. Results has been evaluated on a fabric defect dataset of 13.800 images. Among the existing Inception V3, MobileNetV2, Xception and ResNet50 methods, the InceptionV3 model has achieved 78% classification success. Our designed deep network model could achieve 97% success. The experimental works show that the designed deep model is effective in detecting the fabric defects.

BORDER: A Benchmarking Framework for Distributed MQTT Brokers

Message queuing telemetry transport (MQTT), one of the most popular application layer protocols for the Internet of Things, works according to a publish/subscribe paradigm where clients connect to a centralized broker. Sometimes (e.g., in high scalability and low-latency applications), it is required to depart from such a centralized approach and move to a distributed one, where multiple MQTT brokers cooperate together. Many MQTT brokers (both open source or commercially available) allow to create such a distributed environment: however, it is challenging to select the right solution due to the many available choices. This article proposes, therefore benchmarking framework for distributed MQTT brokers (BORDER), a framework for creating and evaluating distributed architectures of MQTT brokers with realistic and customizable network topologies. Based on isolated Docker containers and emulated network components, the framework provides quantitative metrics about the overall system performance, such as End-to-End latency as well as network and physical resources consumed. We use BORDER to compare five of the most popular MQTT brokers that allow the creation of distributed architectures and we release it as an open-source project to allow for reproducible researches.

CSR investment for a two-sided platform: Network externality and risk aversion

The business of two-sided platforms, such as those used for online ride-sharing, has expanded rapidly thanks to opportunities to efficiently match supply- and demand-side users. However, the development of these platforms faces the challenge of complying with corporate social responsibility (CSR) regulations, particularly on the supply side of two-sided platforms. How to improve CSR investment, in order to reduce CSR violations while ensuring profitability, is now an important question. In this paper, we develop a game-theoretic model for a two-sided platform and analyze the impacts of network externalities (including customer network externality and provider network externality) and risk aversion on CSR investment decision-making. The analytical results show that, contrary to our intuition, greater network externalities do not necessarily stimulate the platform to invest in CSR. When the strength of customer network externality is high, investment in CSR may have a “dilution effect”, i.e., CSR investment may reduce the platform's number of users and profit. Conversely, risk aversion always has an “incentive effect” on CSR investment, thereby increasing the platform's willingness to invest in CSR. Second, both provider and customer network externalities will increase the platform's prices. Interestingly, when the strength of customer network externality is high, the platform attracts more customers, even at high prices. Finally, our analysis indicates that risk aversion may reduce customer surplus when the strength of customer network externality is high. This result contradicts the initial expectation that risk aversion inspires platforms to invest in CSR, thereby improving customer utility and customer surplus.

Optimal Power Flow Based DR in Active Distribution Network With Reactive Power Control

Demand response works as a bridge to enable activities between end customer and distribution network operators. Previously, most of the demand response work is concerned about maintaining supply–demand balance and reducing peak load demand without acknowledging distribution network behavior. This article presents a demand response scheme for an active distribution network via the operation of an unbalanced feeder line and consumer loads. The day-ahead dynamic price is coupled with active distribution network constraints to prevent the formation of new peaks. To tackle up the complexities of unbalanced network and distributed generation (DG) integration, the merits of symmetrical domain components are utilized. Power electronically interfaced DG reactive power component is also considered for constant power and voltage mode of operation. For optimizing the distribution network, optimal power flow is modified to achieve social welfare with distribution network constraints. The performance and computational efficiency of the proposed algorithm are verified through extensive case studies.

MTREE: A Customized Multicast-Enabled Tree-Based Network on Chip for AI Chips

The network on chip (NoC) used in the AI chip is required to be able to deliver data to multiple destination endpoints simultaneously. The existing solutions, such as hierarchial mesh (HMESH) and FATTREE all modify the connection between routers to achieve multicasting. Although this method can achieve delivering data to multiple destination endpoints at the same time, it significantly increases the number of routers used and, therefore, brings a huge additional area and power consumption overhead. In order to solve this problem, this work designed a customized multicast-enabled tree-based NoC, named mTREE, by modifying the microarchitecture of the router, including the routing calculation unit, credit counter, and switch allocator. When implemented on Arria10 FPGA, mTREE only brings a 1% increase in resource utilization compared with the traditional tree-topology NoC. Compared with two other multicast-enabled NoCs: 1) FATTREE and 2) HMESH, mTREE can reach a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.19\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.50\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.67\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.02\times $ </tex-math></inline-formula> reduction in resource utilization, respectively, and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.81\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.01\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.85\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.03\times $ </tex-math></inline-formula> reduction in energy consumption, respectively.

A Comparative Study of Software Defined Networking Controllers Using Mininet

Software Defined Networking (SDN) is a relatively new networking architecture that has become the most widely discussed networking technology in recent years and the latest development in the field of developing digital networks, which aims to break down the traditional connection in the middle of the control surface and the infrastructure surface. The goal of this separation is to make resources more manageable, secure, and controllable. As a result, many controllers such as Beacon, Floodlight, Ryu, OpenDayLight (ODL), Open Network Operating System (ONOS), NOX, as well as Pox, have been developed. The selection of the finest-fit controller has evolved into an application-specific tool operation due to the large range of SDN applications and controllers. This paper discusses SDN, a new paradigm of networking in which the architecture transitions from a completely distributed form to a more centralized form and evaluates and contrasts the effects of various SDN controllers on SDN. This report examines some SDN controllers or the network’s “brains,” shows how they differ from one another, and compares them to see which is best overall. The presentation of SDN controllers such as Ryu, ODL, and others is compared by utilizing the Mininet simulation environment. In this study, we offer a variety of controllers before introducing the tools used in the paper: Mininet. Then, we run an experiment to show how to use ODL to establish a custom network topology on a Mininet. The experimental results show that the O controller, with its larger bandwidth and reduced latency, outperforms other controllers in all topologies (both the default topology and a custom topology with ODL).

Exploring the Benefits of Technology Adoption on Business Performance among Unregistered SMEs: Insights from Nigeria

This study in its capacity explore the significance of technology adoption among unregistered small or micro businesses in the informal economy. To achieve this objective, initial observations were carried out by researchers over a time period on unregistered business owners operating in an informal economy. The study adopts an interview data collection method. The findings reveal that initially, these business owners had no intention of adopting technology into their business model. However, they did. Given this, the adopted technology (simple mobile phones) gave them some unprecedented opportunities over others in terms of work flexibility and expanded customer networks via referral. Unintentionally, the interview findings also reveal that with the simple technology adopted by this informal and unregistered businesses, customers were able to make business transactions from their comfort zone and enhance their individual security consciousness.

Rethinking customer engagement design: Using customer-mobilized engagement (CME) to grow business networks

The purpose of this paper is to propose a conceptual framework of customer-mobilized engagement (CME) pathways where customers actively identify, engage, and mobilize hidden (new) actors (and their resources) who are beyond the reach of the focal organization. As a large proportion of value stems from connections with multiple business networks, an organization's customer network becomes an important strategic asset contributing to competitive advantage and survival. Taking a propositional approach, we conceptualize the CME pathway and elaborate on factors critical to CME drawing on two organizations for illustrative purposes. We develop three propositions explaining how customer-mobilized engagement of hidden (new) actors, resources, and engagement platforms connect dynamically via the mobilizing customer. The reinterpreted communicative value proposition increases the contribution of hidden actors and enhances and expands value outcomes for actors within the CME pathway including increased sustainable competitive advantage for the focal organization. Adopting CME adds new resources, in different ways to deliver value to an organization's diverse business streams and grow their business networks. The CME pathway provides industrial marketing managers with an agile tool to guide the design of their offerings which can adapt across business contexts and time as the social, economic, political and technological environment changes.

Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking

3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.

Deep Learning in Network-Level Performance Prediction Using Cross-Layer Information

Wireless communication networks are conventionally designed in model-based approaches through utilizing performance metrics such as spectral efficiency and bit error rate. However, from the perspectives of wireless service operators, network-level performance metrics such as the 5%-tile user data rate and network capacity are far more important. Unfortunately, it is difficult to mathematically compute such network-level performance metrics in a model-based approach. To cope with this challenge, this work proposes a data-driven machine learning approach to predict these network-level performance metrics by utilizing customized deep neural networks (DNN). More specifically, the proposed approach capitalizes on cross-layer information from both the physical (PHY) layer and the medium access control (MAC) layer to train customized DNNs, which was considered impossible for the conventional model-based approach. Furthermore, a robust training algorithm called weighted co-teaching (WCT) is devised to overcome the noise existing in the network data due to the stochastic nature of the wireless networks. Extensive simulation results show that the proposed approach can accurately predict two network-level performance metrics, namely user average throughput (UAT) and acknowledgment (ACK)/negative acknowledgment (NACK) feedback with great accuracy.

Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran

Given the various advantages of electric vehicles compared to conventional gasoline vehicles in terms of energy efficiency and environmental pollution (among others), this paper studies the factors affecting customers’ willingness to purchase electric vehicles. An integrated discrete choice and agent-based approach is applied to model the customers’ choice for the valuation of electric vehicles based on the internal reference price. The agent-based model evaluates customers’ preferences for a number of personal and vehicle attributes, according to which vehicle they chose. Data from 376 respondents are collected to estimate a random-parameter logit model where customers are asked to reveal their preferences about five attributes of electric vehicles, including travel range, top speed, charge cost, government incentives, and price. The role of social networks of customers and their threshold purchase price is also examined in the agent-based model. The scenario simulation results indicate that the allocation of government incentives for electric vehicles, decreasing electric vehicle/non-electric vehicle price gap, expanding electric vehicle travel range, increasing gasoline prices, and enhancing electric vehicle top speed stimulate electric vehicle market shares, respectively.

Managers as knowledge carriers – Explaining firms’ internationalization success with manager mobility

While managerial mobility is ubiquitously seen as an integral part of the success in firms’ internationalization, discerning its empirical merits has been impaired by the paucity of quasi-experimental evidence, or adequate instrumental variables. To overcome these objective limitations, this paper proposes a novel identification strategy, which uses a control function based on on-the-job search theory to correct estimates for the presence of self-selected mobility flows. Our analysis confirms the finding that managers’ specific market experience matters for firms’ internationalization, especially when it derives from longer tenures at the former jobs.Regarding the attributes of managerial knowledge, our results reveal that on-the-job earned experience is at least as effective for firms’ internationalization as in born knowledge (i.e. origins) and that managers’ personal network of customers is an important asset in managers’ fund of expertise for the expansion into new markets.

Supply chains and the success of M&As: investigating the effect of structural equivalence of merging firms' supplier and customer bases

PurposeThis study investigates the extent to which structural equivalence between acquiring and target firms is associated with post-merger and acquisition (M&A) performance—a relationship that is proposed to be moderated by industry-level vertical relatedness between acquiring and target firms.Design/methodology/approachApplying social network analysis and regression, this study analyzes a buyer–supplier relationship network dataset of 279 M&A deals completed between 2010 and 2017 to test the hypotheses. Structural equivalence is measured as the proportion of common customers and suppliers between an acquiring firm and a target firm.FindingsSupporting a view about the importance of supply chains in explaining M&As outcomes, the results suggest that the structural equivalence in the supplier network is positively associated with post-M&A firm performance. The results also show that the effect of the structural equivalence in the customer network is moderated by vertical relatedness between two merging firms (i.e. structural equivalence contributes to post-M&A performance when vertical industry relatedness is high).Originality/valueThis study contributes to the M&A and supply network literature by investigating the performance implications of structural equivalence in supplier and customer networks, demonstrating the importance of taking a supply chain view when explaining M&As outcomes. Specifically, the authors suggest considering structural equivalence as a new type of relatedness between merging firms (i.e. relatedness in network resources in explaining post-M&A performance). It also indicates how industry-level vertical resource relatedness, which is about relatedness in internal resources between the two firms, could interact with firm-level network resource relatedness, which is about relatedness in external supply chain resources between the two firms, in affecting post-M&A performance.

Preictal-Interictal Classification for Seizure Prediction

Epilepsy is one of the most prevalent neurological disorders, affecting more than 50 million people worldwide. Seizures are a common symptom of epilepsy, which manifest abruptly and cause disruption in a patient’s life. Epileptic seizures can lead to serious injury, and people with untreatable epilepsy must live with the uncertainty of the next seizure occurrence. The ability to predict the occurrence of a seizure could alleviate many of the risks people with epilepsy face. Patients would have time to take precautions to reduce the risk of injury or prevent the seizure altogether. Most of the current approaches are focused on detecting seizures when they occur. While these approaches are useful, they do not provide any lead time to take preventive measures. It is widely known that the brain activity before an actual seizure is a strong indicator of an upcoming seizure. Therefore, we approach this problem from a prediction lens, by developing a classifier that can separate the pre-seizure state from regular brain activity. Development of such a classifier with high performance will provide a lead time to take preventive measures to handle an incoming seizure. The classification model used customized convolutional neural networks trained on short-time Fourier transform images to learn features from multi-channel intracranial electroencephalography (iEEG). The model was trained on a large publicly available dataset (SWEC-ETHZ iEEG database) containing over 2500 hours of EEG data from 18 patients. We have performed these experiments on the data of 9 patients in the database with varying results, highest being an AUC of 0.86.