Network-centric Approach Research Articles

Similarity Heuristics in the Indian Far Right: How the RSS Obscures Its Operational Scale

To conceal their activities, far-right networks manipulate similarity heuristics that suggest their constituent organizations are discrete and coherent. When an organization crafts a public image indicating that only those who wear the same uniforms and march in the same marches are part of an organization, it implies that those who do not, are not. This use of cognitive shortcuts assists far-right organizations in crafting their organizational boundaries to obscure internal divisions of labor. That these disguised internal divisions of labor exist is strong evidence to support a renewed focus on the intra-organizational dynamics of far-right organizations—a focus that pivots from a discursive to a materialist understanding of the far right. I use the case of the Rashtriya Swayamsevak Sangh (RSS), one of the world’s largest far-right organizations, to argue that similarity heuristics disguise far-right connectivity. Paying granular attention to the organizational boundary-making practices of the RSS demonstrates that the true organizational focus of the RSS is its managerial manifestation, rather than its cadre division, which is just one organization the managerial RSS manages. This key finding suggests that scholars must focus on the mechanics of the managerial RSS over the aesthetic phenomenon of the cadre RSS. Such a focus inevitably leads to a network-centric approach to the Indian far right that better captures the mechanics of its mobilization.

Digital Transformation of Incumbent Pipeline Firms through Platformization

ABSTRACT Advancements in digital technology necessitate a shift from traditional linear buyer-supplier value chains to a network-centric approach facilitated by digital platforms. Despite digital platforms being a dominant model for transformation, limited research has explored the enablers for incumbent pipeline firms to embrace platformization. To address this gap, this study reviews existing literature on digital transformation, identifying nine enablers for digital platformization: changing client behavior, information technology (IT) capabilities development, structural efficiencies, disruptive competitive forces, scope advantages, economic triggers, disruptive third-party technology, creation of autonomous corporate structures, and regulatory compliance. Case studies of platformization in diverse industries are analyzed using the modified total interpretive structural modeling (m-TISM) approach, resulting in a hierarchical model with five levels. This model delineates interrelationships among trigger events, external enablers, internal organizational enablers, process enablers, and outcome drivers. Furthermore, the study highlights that the creation and extraction of value from digital platforms are operationalized through e-business strategies, contributing insights to e-business literature by discussing the implications of these enablers on dimensions such as commerce, collaboration, communication, connection, and computing.

Network Motif Detection in the Network of Inflammatory Markers and Depression Symptoms among Patients with Stable Coronary Heart Disease: Insights from the Heart and Soul Study

Background. Network motif analysis is a technique used to explore recurrent and statistically significant subgraphs within a network. Applying a motif analysis to the complex network of inflammation and depression may yield nuanced insight into the specific interaction mechanisms between inflammatory markers and individual depression symptoms, which is our aim. Methods. This cross-sectional study is based on patients with stable coronary heart disease (CHD). A partial correlation network was initially constructed to link inflammatory markers, including C-reactive protein (CRP), Interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein-1 (MCP-1), individual depression symptoms, and covariates. A network-centric approach searched all possible non-isomorphic subgraph patterns of size k = 4 in the network. Results. Although CRP, IL-6, and TNF-α displayed an insignificant association with specific depression symptoms, the motif analysis revealed various subgraph patterns of interactions between depression symptoms associated with MCP-1. Generally, MCP-1 formed a closed loop with psychomotor problems and sleep disturbances, and this configuration was connected in various forms with other symptoms, particularly cognitive (e.g., feelings of worthlessness, concentration difficulty, and suicidal ideation) and neurovegetative/somatic (e.g., appetite changes and fatigue) symptoms. Moreover, MCP-1 was frequently associated with a closed-loop triangle comprising cognitive and neurovegetative/somatic symptoms but not with mood symptoms (e.g., loss of interest and feelings of sadness). Conclusions. The findings provide insight into how MCP-1 may be involved in the pathology of depression among patients with stable CHD in a more precise manner. This study also proposes future directions for research on depression.

Recruitment Promotion via Twitter: A Network-centric Approach of Analyzing Community Engagement Using Social Identity

With the proliferation of online technologies, social media recruitment has become an essential part of any company’s outreach campaign. A social media platform can provide marketing posts with access to a large pool of candidates and at a low cost. It also provides the opportunity to quickly customize and refine messages in response to the reception. With online marketing, the key question is: which communities are attracted by recruitment tweets on social media? In this work, we profile the Twitter accounts that interact with a set of recruitment tweets by the U.S. Army’s Recruitment Command through a network-centric perspective. By harnessing how users signal their affiliations through user information, we extract and analyze communities of social identities. From Social Identity Theory, these social identities can be critical drivers of behavior, like the decision to enlist in the military. With this framework, we evaluate the effectiveness of the U.S. Army’s recruitment campaign on Twitter, observing that these campaigns typically attract communities with military exposure like veterans or those that identify with professional careers and fitness (e.g., student, professionals, athletes). The campaign also attracts, but at a much lower level, interaction from those in the digital industries—data scientists, cybersecurity professionals, and so forth. When analyzing the accounts in terms of their degree of automation, we find a set of intent-unknown bot accounts interacting with the tweets, and that many of the recruitment accounts are perceived as automated accounts. These observations can aid in campaign refinement: targeting the digital community and getting a broader reach for online recruitment publicity campaigns.

Beam SNR Prediction Using Channel Charting

We consider a machine learning approach for beam handover in mmWave 5G New Radio systems, in which User Equipments (UEs) perform autonomous beam selection, conditioned on a used Base Station (BS) beam. We develop a network-centric approach for predicting beam Signal-to-Noise Ratio (SNR) from Channel State Information (CSI) features measured at the BS, which consists of two phases; offline and online. In the offline training phase, we construct CSI features and dimensionality-reduced Channel Charts (CC). We annotate the CCs with per-beam SNRs for different combinations of a BS beam and the corresponding best UE beam, and train models to predict SNR from CSI features for different BS/UE beam combinations. In the online phase, we predict SNRs of beam combinations not being used at the moment. We develop a low complexity out-of-sample algorithm for dimensionality reduction in the online phase. We consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors, Gaussian process regression, and neural network-based predictions. To evaluate the efficacy of the proposed framework, we perform simulations for a street segment with synthetically generated CSI. We investigate the complexity-accuracy trade-off for different dimensionality reduction techniques and different predictors. Our results reveal that nonlinear dimensionality reduction of CSI features with neural network prediction shows the best performance, and the performance of the best CSI-based prediction method is comparable to prediction based on using known physical location.

Leveraging demand-capacity balancing to reduce air traffic emissions and improve overall network performance

Improvements in European air traffic management (ATM) constitute one of the key levers to reduce aviation emissions in the future to reach ambitious goals. In this paper, we evaluate the benefits of improved network-centric ATM with coordinated optimisation of capacity, delays, re-routings and emissions vs the present fragmented ATM which has generated high delays and extensive re-routing due to sub-optimal capacity provision. In addition, we analyse the impact on flight efficiency of a novel trajectory-independent airport-pair charging scheme, which makes shortest routes the cheapest for Airspace Users (AUs), which is not always the case with the current charging scheme in Europe. We test and evaluate proposed changes in ATM using a case study based on real data covering 3,000–4,000 flights in large parts of Western European airspace. We find that network-centric ATM can reduce variable costs borne by AUs by 21% and in particular re-routing cost (and associated emissions) by almost 64%. Furthermore, airport-pair charging can save almost 11% of costs for AUs and up to 320,000 tons of CO2 emissions — if accompanied by adequate capacity changes that reflect the shift in demand towards shorter trajectories. We conclude that various stakeholder initiatives to reduce the fragmentation in European ATM and to move towards a more network-centric approach have merits and should be pursued by the policy makers.

Abstract A09: Targeting of FOXM1 recapitulates NPM1 mutations in chemo-sensitization of AML

Abstract NPM1 mutations are among the most common mutations in CN-AML (Cytogenetically Normal-AML) and are known to enhance chemosensitivity in AML patients. Across multiple co-operative group studies, in the absence of deleterious mutations, patients with NPM1 mutations show superior overall and relapse-free survival. The FOXM1 regulatory network is a major predictor of poor prognosis in human cancers of different origin, including AML. Early AML cell line and patient sample data from our lab demonstrated that nuclear NPM colocalizes with nuclear FOXM1 while mutant NPM sequesters FOXM1 in the cytoplasm. Applying RNAseq differential expression results from published FOXM1 knockout experiments on KG-1 cells and MLL-AF9-transformed mice to Beat AML RNAseq data, we developed a ~200-gene FOXM1 signature that was significantly associated with inferior CR/CRi rate (OR=0.44 [95% CI: 0.21–0.95], p=0.037) in wild type NPM1, wild type FLT3 de novo AML patients. Notably, this FOXM1 signature was not significantly associated with CR/CRi in mutated NPM1, wild type FLT3 patients (OR=1.78 [95% CI: 0.15–21.51], p=0.651). These data suggest that inactivation of FOXM1 may underlie the chemosensitivity conferred by the highly prevalent NPM1 mutation in favorable risk AML. Using our novel network-centric approach, we found a first-in-class small molecule (STL427944) that affects a pathway that limits FOXM1 expression and significantly disrupts the associated regulatory network. To directly address the role of FOXM1 in chemosensitivity of AML cells, we tested two FOXM1-specific inhibitory compounds: STL427944 and its derivative, STL001 (their structures will be disclosed). From a medicinal chemistry standpoint, STL427944 has two main metabolic liabilities, a hydrolytically labile furane-carboxylic acid phenolic ester and a hydrazone. We applied several paths of structural activity relationship (SAR) optimization to overcome these issues from removal of liabilities to bio-isosteric replacement to cyclizations. The ring replacement is likely to have significantly improved the overall stability observed in STL001, resulting in at least !0-fold more active compound with better ‘drug-like’ properties and thus enhanced potency. We showed that 500nM of STL001 partially suppressed expression of FOXM1 protein in AML, while 5mM of STL001 (versus 50mM of STL427944) fully suppressed FOXM1 and sensitized AML cells to venetoclax and cytarabine treatment. Ex vivo treatment of AML patient samples with STL427944 and STL001 resulted in inhibition of nuclear FOXM1 expression according to IHC and downregulation of canonical FOXM1 transcriptional targets as indicated by real-time PCR. This treatment potentiated the efficacy of venetoclax in inhibiting colony forming activity in AML patient samples. Our data suggest that targeting FOXM1 in AML patients with wild type NPM1 may recapitulate NPM1 mutations and will have a robust therapeutic effect leading to more favorable outcome for AML patients. Citation Format: Irum Khan, Sandjeev Raghuwanshi, Andy Kaempf, Jeff Tyner, Alex Domling, Carlos Camacho, Andrei Gartel. Targeting of FOXM1 recapitulates NPM1 mutations in chemo-sensitization of AML [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A09.

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.

Аппаратно-программная реализация инфраструктуры единого геоинформационного центра лесного хозяйства

The presented material is a continuation of the article on the concept of Unified Geoinformation Center (UGS) infrastructure, revealing the architecture of the information system model implementing the network-centric approach as the main aspect – the principle of interoperability. The network-centric approach in building the information system of UGS is embodied in the union of all objects of the sys-tem, both within and outside the perimeter in a single information space (network-centric environment), and interoperability is ensured by the proposed interaction technology based on the advanced message queuing protocol – AMQP, which provides for all objects of the network-centric environment the possibility of free mutual exchange of information, regardless of the functions they perform and their internal structure. On this basis, the scheme of the architecture of the information system of the unified geoinformation center of the forestry, with components both in its perimeter and outside it, is proposed.

IMPLEMENTATION OF PRINCIPLES OF NETWORK CENTRAL MANAGEMENT IN THE ACTIVITY OF MODERN BUSINESS STRUCTURES

The article examines the essence of network-centrism and network-centric management, as well as the features, capabilities, advantages and disadvantages of implementing the principles of network-centric management in the activities of modern business structures. The contribution of A. Sebrowski, J. Garstka, D. Alberts and F. Stein to the formation of the concept of network-centric management is considered. The origin of the concept of "network-centrism" and "network-centric management" is studied. The essence of the network-centric approach is investigated and the specifics of its use in the process of managing complex socio-economic systems are determined. It is proposed to improve the management of modern business structures by implementing the principles of network-centric management in their activities. The main levels at which business structures should be managed in accordance with the network-centric approach to management are identified. On the basis of the analysis of traditional principles of network-centrism, it is proposed to introduce into the management of business structures the principles of: formation of a single distributed information infrastructure, unimpeded information exchange; individual information management; interdependence and coherence of the elements of the management system, mandatory validation of information and intellectual integration of the elements of the management system of the business structure. The main advantages and disadvantages of implementing the principles of network-centric management in the activities of modern business structures are identified. The expediency of implementing the principles of network-centric management in the activities of modern business structures, in order to increase the effectiveness of their activities through effective management of information support based on the construction of an information network that allows interconnection of all elements of the management system using available information is substantiated.

WiFiTrace

Contact tracing is a well-established and effective approach for the containment of the spread of infectious diseases. While Bluetooth-based contact tracing method using phones has become popular recently, these approaches suffer from the need for a critical mass adoption to be effective. In this paper, we present WiFiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring, and utilizes them for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional methods, rather than to supplant them with new technology. We designed an efficient graph algorithm to scale our approach to large networks with tens of thousands of users. The graph-based approach outperforms an indexed PostgresSQL in memory by at least 4.5X without any index update overheads or blocking. We have implemented a full prototype of our system and deployed it on two large university campuses. We validated our approach and demonstrate its efficacy using case studies and detailed experiments using real-world WiFi datasets.

Discrete Networked Dynamic Systems with Eigen-Spectrum Gap: Analysis and Performance

This paper provides a detailed analysis and performance treatment of a class of discrete-time systems with an eigen-spectrum gap coupled over networks. We deploy tools from time-scale modeling (TSM) theory to develop rigorous reduced-order models to aid in the stability analysis of these multiple time-scale networked systems over fixed and undirected graph topology. We establish that the controller gain matrices can be determined by solving convex optimization problems in terms of finite linear matrix inequalities with prescribed $\mathbb{H}_\infty$ and $\mathbb{H}_2$ performance criteria. As demonstrated by simulation studies, the ensuing results provide designers with a network-centric approach to improve the performance and stability of such coupled systems.

СЕТЕЦЕНТРИЧЕСКИЙ ПОДХОД К УПРАВЛЕНИЮ СУБЪЕКТАМИ РЫНКА В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ЭЛЕКТРОЭНЕРГЕТИКИ

The article presents the results of research on the development of the structure, methods and technologies for managing the Unified Energy System (UES) of Russia in the context of the digital transformation of the electric power industry. The analysis of the experience of managing complex socio-technological systems (STS) on the example of the UES of Russia and modern achievements in digital technologies resulted in defining the main directions of the formation of a network-centric approach to ensuring the reliability of energy supply to consumers and the country’s energy security in normal operating conditions and in emergency situations. A comparative analysis of the structures is carried out and examples of practically implemented fragments of the above systems are presented. The article also presents the stratification of control loops and the structure of ontological modeling for the functioning of the entire UES control system. It defines and mathematically substantiates the algorithms for forming decisions for the managing the power facilities in the level range from of the National Crisis Management Center to the operational personnel responsible for maintaining the mode of power systems and power facilities. The given examples of operating systems, SISFEC (state information system of the fuel and energy complex) and SRMS (stability reserve monitoring systems)actually confirm the correctness of the selected solutions of the network-centric approach to managing the UES of Russia. The provisions set out in the article determine the direction in developing the digital control systems for STS.

Anonymization Framework for IoT Resource Discovery based on Edge Centric Privacy Model

As the efficacy of Internet of Things is expeditiously growing, maintaining privacy with respect users and applications has become a significant aspect. Since the data is getting generated at tremendous rate that includes Sensitive data (any data considered as private by the Data-owner) which has to be hidden, especially the data collected from the Crowd-Source. Due to resource-constrained sensing devices, IoT infrastructures use Edge devices for real-time data processing. Protecting sensitive data from malicious activity becomes a key factor, as all the communication flows through insecure channels. To develop security infrastructures for IoT and distributed Edge networks, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. The method presented herein is applied to an amusement park scenario, which is modeled as a typical smart IoT network. Here, data from sensors and social networks can boost smart lighting to provide citizens with an elegant and safe environment. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. Firstly, a standalone IoT-edge system is developed, and later, an integrated IoT-based edge-cloud system is designed to compare the systems’ effectiveness. The implementation results show a close correlation between the standalone edge and dual mode edge system. However, the edge-cloud system provides more flexibility and capability to counter the sensitive data streaming and analytics services within the constrained IoT framework. In this paper we have developed a system that uses fog computing approach to perform various tasks and filters the sensitive data, thus helps in preserving privacy.

Informatization of medical supply of the armed forces in military conflicts based on the concept of netcentric control

AIM: to determine the principles and assess the possibilities of informatization of medical support of the Armed Forces in military conflicts based on the concept of network-centric control.&#x0D; MATERIALS AND METHODS: A content analysis of domestic and foreign literary sources has been carried out for the identification and subsequent analysis of the organizational aspects of creating a network-centric model for managing medical support in general and medical personnel in particular, capable of performing the tasks assigned to it in high-tech military conflicts (wars).&#x0D; RESULTS: The definition of the concept of network-centric medical support is formulated as a system of measures aimed at increasing the capabilities of medical units in modern wars and military conflicts in order to reduce sanitary losses and return to service the maximum number of wounded and sick by creating a communication and information space and combining all medical systems. provision in a single network. The principles of using the network-centric approach to the construction of a promising management system for medical services are formulated. The results of approbation of a network-centric approach to solving problems of medical support of troops actions are presented. The tools for ensuring the consolidation and optimal use of the organizational potential and information and analytical resources of the medical service management bodies and military medical organizations are characterized.&#x0D; CONCLUSION: It is shown that the creation of a unified medical information system will significantly increase the efficiency of medical support without a quantitative increase in service. The authors give a report that it is difficult, but quite possible, to catch up with the leading foreign countries in terms of opportunities, availability and quality of medical supplies with network-centric control. For this, it is necessary in parallel to consider, develop and implement a program designed to create a system of network-centric control of military medical organizations and medical support of troops in general, to launch a process of elaborating a theory and testing ideas (bibliography: 20 refs).

Threat-Event Detection for Distributed Networks Based on Spatiotemporal Markov Random Field

Distributed threat-events are one of the main challenges faced in computer networks. Although a lot of research has been conducted for these issues, the situation has not been significantly improved. Different from existing victim-centric approaches, in this article we propose a new network-centric approach for the detection of distributed threat-events. The distributed network is treated as a holistic system that consists of spatially interconnected network elements. Network events are detected by the dynamic behavior analysis of the distributed networks. We develop a model consisting of two-layer random fields to describe the time-varying traffic forwarding behavior of the distributed networks. The bottom layer describes the interaction and influence of the network elements under the action of network events. Markovianity is adopted to characterize the spatiotemporal context of each network element’s behavior patterns. The top layer describes each network element’s traffic features driven by the underlying behavior patterns. A Gaussian mixture model is used to capture the statistical features of the network traffic for each behavior pattern. We derive algorithms for parameter estimation and event detection. Numerical experiments using real datasets and different network scenarios are presented to validate the proposed approach. Performance-related issues and comparison with related works are discussed.

A Protocol to Examine Surgical Team Communication in a Large Military Medical Center.

Preventable clinician communication errors are the most frequent root cause of surgical adverse events. Effective communication among surgical team members is an important overall indicator of clinical nursing excellence and is crucial to promote patient safety and reduce healthcare risk. The purpose of this publication is to describe our research protocol, which will comprehensively investigate surgical team communication and surgical team structure. There are two overall study objectives: (a) to characterize the network factors that influence perioperative clinician communication and (b) to identify how team structure shapes communication effectiveness. Specifically, we will study the association among clinician relationships, surgical team familiarity, surgical team size, and communication effectiveness. We will use an exploratory, prospective, cross-sectional, network-centric approach using social network analysis to determine how interdependent clinician relationships influence surgical communication patterns. Targeting an 80% response rate, we will use total population sampling to recruit all clinicians (nurses, surgeons, anesthetists, surgical technologists, students, residents) who directly provide surgical care in the operating room at a large military medical center. We will administer an electronic survey to surgical teams at the end of the surgical day to elicit clinician communication assessments and relational preferences. From the survey questions, we will develop six relational networks (interaction, close working relationship, socialization, advice seeking, advice giving, speaking up/voice) and three models that represent communication effectiveness for each participant and team. We will use various statistical methods to characterize the network factors that shape operating room clinician communication and identify how team structure shapes communication effectiveness. This study will start enrolling participants in 2020. We are among the first to comprehensively investigate operating room communication using social network analysis. The results of this study will provide valuable insight into nurse collaboration and communication in interprofessional teams, enable a thoughtful analysis of surgical staffing and perioperative team building, and inform future-targeted interventions to improve operating room communication weaknesses.

Dynamic Sectorization and parallel processing for device-to-device (D2D) resource allocation in 5G and B5G cellular network

Beyond Fifth Generation (B5G) network aims to provide a very high data rate with minimum latency to an ultra-dense user environment. To achieve this demand, the possible approaches are network-centric and device-centric approach. In a network-centric approach, a new frequency band is introduced, and the existing network infrastructure is modified. The device-centric approach does not require any modification in the existing network infrastructure, and the demand of B5G network is achieved through optimum resource allocation methodology. Device-to-Device (D2D) is an effective device-centric approach that supports the B5G network. In this paper, we propose a dynamic sectorization technique in which eNodeB (eNB) varies the number of sectors dynamically in the network and allocates the Resource Block (RB) to D2D users. Sectoring improves Signal-to-Interference-Noise-Ratio (SINR) and network performance. We derive an expression for the probability of successful transmission and threshold to make a decision on the number of sectors based on available RBs and D2D users in the network. Further, dynamic sectoring helps eNB to perform parallel processing for reducing the denial of the request. Simulation results validate the probability of successful transmission of D2D pairs with available RBs and the effectiveness of parallel processing to reduce the denial of request with improved SINR.

A User-centric Security Solution for Internet of Things and Edge Convergence

The Internet of Things (IoT) is becoming a backbone of sensing infrastructure to several mission-critical applications such as smart health, disaster management, and smart cities. Due to resource-constrained sensing devices, IoT infrastructures use Edge datacenters (EDCs) for real-time data processing. EDCs can be either static or mobile in nature, and this article considers both of these scenarios. Generally, EDCs communicate with IoT devices in emergency scenarios to evaluate data in real-time. Protecting data communications from malicious activity becomes a key factor, as all the communication flows through insecure channels. In such infrastructures, it is a challenging task for EDCs to ensure the trustworthiness of the data for emergency evaluations. The current communication security pattern of “communication before authentication” leaves a “black hole” for intruders to become part of communication processes without authentication. To overcome this issue and to develop security infrastructures for IoT and distributed Edge datacenters, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. A trusted controller is initialized to authenticate and establishes the secure channel between the devices before they start communication between themselves. The centralized controller draws a perimeter for secure communications within the boundary. Theoretical analysis and experimental evaluation of the proposed security model show that it not only secures the communication infrastructure but also improves the overall network performance.