Network Resources Research Articles

‘My life is not to be called a failure’: how family reputation in the origin affects return considerations among South Asian migrants in the Gulf

ABSTRACT Families are an important factor to consider when trying to understand if and when labor migrants decide to return to the origin. Research has mainly focused on how families provide incentives to return. During the COVID-19 pandemic, however, stories about return migration decisions among male South Asian migrants in the Gulf reveal that families can exert substantial pressure on migrants to stay abroad, even during times of crisis. Using in-depth interviews with maigrants in the UAE, returnees and family members between 2020 and 2022, we find that social expectations of migrants to maintain or build family reputation through actions abroad, in the context of broader communities of origin, is a key factor in deterring migrants from returning. Migrants and their families in the origin often seek to continue signaling a successful migration project, particularly by telling stories of entrepreneurial success or of being embedded in the destination through strong and resourceful networks. Returning early from a migration project, even during the pandemic, can be interpreted as a failure and risks negatively impacting the family's reputation. We observe that those who return early, despite family pressure to stay, face significant social sanctions from family and community.

Secondary flexibility market mechanism design and response behavior analysis among multi-microgrids with high proportional BTM-RERs

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as “Recipients” or “Providers”, is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes “SecFlex market organization–trading volume collation–response schedule optimization”. At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market.

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial

The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements.

Centralization of care for rare genetic syndromes associated with cancer: improving outcomes and advancing research on VHL disease.

Von Hippel-Lindau (VHL) disease is a rare genetic syndrome caused by a germline pathogenic variant in one VHL allele. Any somatic event disrupting the other allele induces VHL protein (pVHL) loss of function, ultimately leading to patients developing multiple tumours in multiple organs at multiple timepoints, and reducing life expectancy. Treatment of this complex, rare disease is often fragmented, as patients visit specialist clinicians in isolation at different medical centres. Consequently, patients can receive sub-optimal treatment that results in decreased quality of life and a poor experience of health care systems. In 2021, we established a comprehensive clinical centre at San Raffaele Hospital, Milan, devoted to VHL disease. The centre provides a structured programme for the diagnosis, surveillance and treatment of patients alongside research into VHL disease and involves a multidisciplinary team of dedicated physicians. This programme demonstrates the benefits of care centralization, including concentration of knowledge and services, synergy and multidisciplinary management, improved networking and patient resources, reducing health care costs, and fostering research and innovation. VHL disease provides an ideal model to assess the advantages of centralizing care for rare disease and represents an unparalleled opportunity to broaden our understanding of cancer biology in general.

Formation of structural holes in R&D context: the effect of inventors’ knowledge abilities and patenting output

Purpose This study aims to investigate the influence of inventors’ abilities to acquire external knowledge, provide broad and professional knowledge and patenting output (i.e. different types of inventors) on the formation of structural holes. Design/methodology/approach The authors collected 59,798 patents applied for and granted in the USA by 33 of the largest firms worldwide in the pharmaceutical industry between 1975 and 2014. A random-effects tobit model was used to test the hypotheses. Findings The inventors’ ability to acquire external knowledge contributes to the formation of structural holes. While inventors’ ability to provide broad knowledge positively affects the formation of structural holes, their ability to provide professional knowledge works otherwise. In addition, key inventors and industrious inventors are more likely to form structural holes than talents. Originality/value The results identify individual factors that affect the formation of structural holes and improve the understanding of structural hole theory. This study is unique in that most scholars have studied the consequences of structural hole formation rather than their antecedents. Studies on the origin of structural holes neglect the effect of inventors’ knowledge abilities and patenting output. By addressing this gap, this study contributes to a more comprehensive theoretical understanding of structural holes. The results can guide managers in managing structural holes in accordance with inventors’ knowledge abilities and patenting outputs, which optimize the allocation of network resources.

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Security paradigms for IoT in telecom networks: Conceptual challenges and solution pathways

The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: IoT, Telecom, Networks, Security.

Patient influencer: the impact of homophily on value co-creation behaviour in online health communities

ABSTRACT The world has witnessed a rapid rise of online health communities (OHCs) where patients can create a powerful network of support and resources to make a meaningful difference in people’s health lives. Despite this rapid rise, healthcare authorities and organisations are under enormous pressure to create a sustainable environment for OHCs. Prior studies revealed that encouraging patient value co-creation behaviour (VCB) is substantially beneficial for successful OHCs; however, what factors influence these patient behaviours to co-create value is still unknown. To fill this gap, this study uses the service-dominant logic perspective of value co-creation theory and discloses how homophily between patient influencers and patients in OHCs effect VCB. The data was collected through a self-administered survey from 422 patients who are users of OHC in China and was analysed via partial least squares structural equation modelling and fuzzy set qualitative comparative analysis (fsQCA). The results revealed that homophily positively influences VCBs, i.e. participation behaviour (PB) and citizenship behaviour (CB). A parasocial relationship only moderates the relations between homophily and (PB), whereas the influencer ethical interaction capability significantly moderates the relationship of both homophily with patient PB and CB. Furthermore, the fsQCA findings signify the robustness of the study model.

A synthetic indicator for structuring resilient public transport operation

ABSTRACT This study introduces a synthetic resilience indicator as a unified analytical tool to evaluate public transport resilience, assessing performance changes in bus networks against random disruptions. This methodology measures the cumulative impact of disruptions, considering the network’s capacity for maintaining planned functionality (robustness and reliability) and recoverability (i.e., route diversity), factoring in network topology, passenger flow, and resource diversity. This paper analyzes the impact of disruptions at network and station levels by applying an index to six benchmark networks. Our findings reveal: (1) networks with high station connectivity maintain robustness and flow; (2) a clear link between flow reduction and alternate routes highlights route diversity’s value; (3) interplay between topological and flow-related network attributes underlines the need for an aggregated analytical approach. The proposed index can be used as an optimization goal during route network design; informing operators regarding the impact of design choices on future network properties.

Product development the IKEA way – The role of target costing as a framing device to configure and combine resources in networks

Relying on two embedded case studies of product development within IKEA's industrial network, this paper examines the role of Target Costing as an accounting tool that has the capacity to frame development efforts into either exploitative or explorative projects. Such a framing affects, in turn, the configuration of the relevant network of resources and mediates the behavior of the actors involved via specific types of controls. We contribute to the IMP-inspired literature on innovation and product development as we add nuances to how the concepts of exploitative and explorative innovation paths play out on a network level, especially in relation to the resource dimension. In addition, the notion of the mediating role of accounting adds to how we can understand control and its consequences in an interorganizational network context. Our data consists of several sources, about 70 interviews with individuals working at IKEA and its partners. We also had access to internal company material such as costing calculations. Our study has practical implications as it can help managers identify which types of control to use and how these can be matched with different innovations strategies on a network level.

Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach

The emergence of network slicing as a flagship technology in 5G networks has not only enhanced network expansion and flexibility in resource management for service continuity, but also provided an avenue for establishing a viable market for resource sharing. To optimize the network's resource usage, stakeholders are encouraged to take pragmatic steps toward dynamic resource sharing. This paper designs a techno-economic model for the strategic interactions among multiple competing mobile virtual network operators (MVNOs) and their users in a trading marketplace. We formulate the dynamic pricing problem as a two-stage Stackelberg game, where the MVNOs are leaders, and the users are followers. In the first stage, the MVNOs compete to set their differentiated unit prices using a negotiation mechanism while considering system-level network load. Then, the users decide their purchasing volumes to match the prices of the MVNOs. We transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a multi-agent deep Q-network (MADQN) method that obtains an optimal solution for the formulated game. Simulation results and analysis reveal that the proposed algorithm achieves convergence under the competitive pricing scheme (CPS) and independent pricing scheme (IPS) while enhancing MVNOs and users' utilities at acceptable levels.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Optimization on land development intensity of new industrial towns based on carrying capacity of multi-modal traffic network

ABSTRACT In the context of China’s new urbanization, the establishment of ‘new industrial towns (NITs)’ has emerged as a pivotal strategy for facilitating urban development. However, the current construction of NITs confronts numerous challenges, including issues such as unreasonable land development intensity (LDI) and overburdened transportation infrastructure. Urgent attention and improvement are required to address the coordination gaps between urban land use and transportation systems. This research explores the traffic carrying capacity (TCC) of NITs at a multi-modal super-network level, developing a bi-level programming model for LDI optimization and proposing an improved genetic algorithm (GA) to solve it. Taking the NIT in Laiwu District, Jinan, China as a case study area, the results demonstrate that our approach maximizes the utilization of space–time resources in both bus and car networks, particularly benefiting the latter during morning peak hours, thereby achieving a more balanced supply-demand traffic equilibrium.

A hybrid framework for routing and channel assignment in WMN’s

Wireless mesh network (WMNs) can replace the physical existence of wired network; nevertheless, to solve the problem of traffic and congestion, they must carefully arrange radio resource assignment. In this paper, we focus on the major issues while transferring the data over the network and optimization of resources. WMN mainly suffers from congestion problem when numbers of channel transfer data at a same time which indirectly leads to overlapping channels and sometime data cannot be mitigated to destination. To overcome this problem and provide optimal solution we proposed hybrid channel assignment technique which offers four phase solution technique, in this technique firstly by mitigating the traffic than assigning the optimal path based on greedy and BFS. The purpose of this congestion control hybrid ant based greedy-BFS-load balancing (CCHAGBL) technique is to diminish the number of used slots, which is directly related to the overall resource assignment. We also provide optimal solution using Ant colony optimization technique with hybrid algorithm to achieve the network efficiency and resource utilization with randomly generated traffic. With the help of hybrid ant based greedy-BFS-load balancing (GBL) channel assignment algorithm throughput and packet delivery ratio is upgraded and delay is reduced to minimum.

Dynamic network-aware container allocation in Cloud/Fog computing with mobile nodes

The massive adoption of movable devices, created a new use cases such as Internet of Things (IoT), autonomous vehicles, spacecraft computing, etc. Not only end users, but also computing infrastructures can change their location. The applications are composed of many interdependent microservices with specific resource requirements. Latest state of the art in Cloud/Fog infrastructures is considering only the computational and network (latency, bandwidth) resources during scheduling of microservices, but in a static way. The network variability when the nodes are moving in space is not considered. This leads to increased total latency, hindering the Quality of Service (QoS) and network costs. Many researchers are focused only on a theoretical level. This paper proposes a novel technique for network-aware dynamic allocation of interdependent microservices on moving infrastructure nodes, applicable in practice. It is composed of a generic MILP optimization model and implementation in a Cloud/Fog platform. Several examples with sample Edge-Native application are obtained. The results show reduction in the total end-to-end network latency compared to the latest state of the art.

COMPUTATIONAL INTELLIGENCE BASED ROUTING SOLUTIONS FOR MOBILE AD HOC NETWORKS

Mobile Ad Hoc Networks (MANETs) refers to a wireless network where nodes dynamically establish and maintain connections with each other, directly without the need for a centralized infrastructure. MANETs are characterized by their decentralized nature, presenting challenges for routing protocols concerning rapid data transmission, minimized delay and Quality of Service (QoS) provisioning. Traditional routing protocols may struggle to perform optimally in MANETs due to frequent changes in network topology and resource constraints. The development of intelligent routing protocols tailored to the specific characteristics of MANETs is vital for dynamic and resource-constrained environments. Computational intelligence (CI) techniques have gained attention for designing intelligent routing protocols for MANETs. This paper provides a comprehensive overview of majorly used CI methodologies in MANETs. The paper reviews existing research on intelligent routing protocols based on CI techniques in MANETs and identifies future research directions in this field to address the unique challenges of MANETs and improve network performance and reliability.

A generalized detection framework for covert timing channels based on perceptual hashing

AbstractNetwork covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one‐dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one‐dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

Asymptotic voltage restoration in islanded microgrids via an adaptive asynchronous event-triggered finite-time fault-tolerant control

As the actuators of distributed generators involved in Microgrids (MGs) may experience different faults, it is highly recommended to design an appropriate secondary Fault Tolerant (FT) controller to provide resilience and robustness with respect to these malicious events. Meanwhile, the reduction of communication network workload from controller-to-actuator may be helpful to mitigate actuators stress. By virtue of the above, this article aims at introducing a novel distributed adaptive FT-Event-Triggered Control (FT-ETC) scheme for secondary voltage regulation control in AC islanded MG able to compensate faulty actuators effects while reducing the waste of communication network resources. The proposed control scheme is derived with the aid of the backstepping method and the ETC theory, whose combination ensures resilience with respect to possible faulty signals, while no knowledge of global information is required. Adaptive mechanisms are derived by exploiting Lyapunov theory, while Zeno-freeness property is proven by contradiction. Compared with the existing state-of-the-art on FT-ETC in islanded MG, here the main novelty relies in achieving the asymptotic rather than the practical stability of the voltage tracking errors despite an unhealthy environment, while the remaining adaptive signals involved in the closed-loop systems are bounded in finite-time. Finally, an extensive simulation analysis, also involving real-time Hardware In-the-Loop (HIL) experiments, confirms the theoretical derivation.

Preparation of final qualifying work at a university: the view of modern students

The problem and the aim of the study. The need to study and use the capabilities of network technologies to improve general cultural and professional levels is becoming a condition for training highly qualified specialists. To effectively use computer devices and digital tools, computational thinking is required. The purpose of the study is to explore the possibilities of using a professionally oriented network course for the development of computational thinking of engineers. Research methods. Computational thinking is considered by the authors as a mental process consisting of the sequential execution of a set of actions: activation from human memory of a system of images of objects, and connections between them; formulation of the problem taking into account uncertainty factors; drawing up a solution algorithm and its effective implementation. 68 bachelors from the first year of the Russian State Agrarian University named after K. A. Timiryazev were involved in the study (Russian Federation). Training program 23.03.01 – “Technology of transport processes”, profile – “Digital transport and logistics systems of road transport”. The online training course “Computer Science and Networks in the Industry” was developed by E. V. Shchedrina, and presented on the Moodle platform. Registration certificate No. 24877 dated August 28, 2021. To diagnose and assess the maturity of computational thinking, the author’s testing materials are used. For statistical processing of data, the Pearson's chisquared test was used. Results. A characteristic of the network resource is the execution of all calculation tasks on a computer using application software. When working with course materials, engineers perform a sequence of actions characteristic of computational thinking: analyze the text of a professionally oriented task (formulates the task as a computational problem), decompose the problem; compose and implement an algorithm, perform its analysis and evaluation. Statistically significant differences were determined in the qualitative changes that occurred in the system of training specialists (χ2 = 6.842; p < 0.05). In conclusion, the positive aspects of using a professionally oriented network course for the development of computational thinking of engineers are highlighted (for example, to gain experience in formulating a problem taking into account the uncertainty of the future, students analyze a corporate network, determine a subnet mask for different conditions, etc.).