Communication Networks Research Articles

Thinking Reflectively: Insights from Academic Leaders in Higher Education

Reflective thinking is essential for effective leadership in higher education, enabling academic leaders to address complex challenges and improve decision-making and problem-solving skills. This study explores the importance of reflective thinking for academic leaders, focusing on its role in fostering open-mindedness, critical evaluation, and continuous learning. The study adopted a mixed-method approach, with semi-structured interviews with 13 department heads and a survey of 130 academic administrators across universities in Islamabad. The findings of the study highlight the necessity of reflective thinking for strategic decision-making, conflict resolution, and the creation of a positive institutional culture. Reflective thinking encourages academic leaders to consider multiple perspectives, identify patterns, and challenge assumptions, thereby facilitating innovative solutions and adaptive strategies. Key elements identified include open-mindedness, full engagement, and accountability, which are essential for effective reflection. By addressing these challenges, the study proposes practical strategies for fostering reflective thinking skills, including participation in communication networks and continuous professional development. Overall, this study contributes to the field of higher education leadership by providing insights into the role of reflective thinking in improving decision-making and problem-solving. It advocates for the integration of reflective practices into leadership development programs and emphasizes the importance of fostering a culture of reflection to improve the overall effectiveness of academic institutions. Through continuous self-reflection and adaptive leadership, academic leaders can better navigate the complexity of their roles, ultimately benefiting faculty, staff, and students. Quantitative analysis revealed that 85% of the surveyed academic administrators identified reflective thinking as crucial in their leadership roles.

Radio-Frequency Electromagnetic Background Created by Mobile (Cellular) Communications

The technique of integral assessment of the radio frequency electromagnetic background (EMB) intensity created by wireless information services is presented, based on the forecast of the average electromagnetic loading on area created by radiations of spatially distributed base stations and mobile terminal devices. This loading can be determined both on the basis of a direct estimate of the average total irradiation power of the Earth’s surface by base stations or the average total radiation power of terminal devices per unit of its area, and on the basis of an estimate of the average area traffic capacity and known parameters of equipment and topology of mobile communication networks. Expressions to estimate the conditional average levels of EMB generated by stationary and mobile components of mobile communication systems, and the results of calculations illustrating the processes of its generation by these systems in different frequency ranges with different average area traffic capacity and different sizes of service areas of base stations are given. These calculations confirm the real danger of an unacceptable decrease in the level of electromagnetic safety of the population and deterioration of the ecology of the habitat without the implementation of scientifically based restrictions on the characteristics of mobile communication systems and services.

From Theory to Practice: Implementing Meta-Learning in 6G Wireless Infrastructure

The vision of the sixth generation of communication systems, commonly known as 6G, entails a connected world that provides ubiquitous connectivity and fosters the digital transformation of society. As the number of devices, services, and users continues to grow, intelligent solutions are expected to facilitate this transformation. This paper considers meta-learning as a pivotal paradigm for 6G systems, detailing its principles, algorithms, and theoretical underpinnings. The methodology involves integrating meta-learning with three potential 6G technologies: RF-based communication systems, optical communication systems, and molecular communication systems. The findings reveal the distinct characteristics of these technologies and demonstrate the potential benefits and challenges of incorporating meta-learning algorithms. Practical implications highlight how meta-learning can enhance the efficiency and adaptability of 6G systems, addressing the growing demand for intelligent and seamless communication networks.

Fiber To The Home (Ftth) Network Infrastructure Design Using Gigabyte Passive Optical Network (Gpon) Technology South Solok District

Fiber To The Home (FTTH) Network with Gigabit Passive Optical Network (GPON) Technology in South Solok Regency, supervised by Ibu Amelia Yolanda, ST as supervisor 1 and Ibuk Popy Maria, ST STT as supervisor 2. Development of Telecommunications technology as a necessity Currently, communication has developed rapidly, transmission media facilities are needed that are capable of transmitting information with large capacities and adequate data transfer speeds. By implementing GPON technology, FTTH services can reach users at up to speed. This research aims to design an FTTH network using GPON technology in the South Solok Regency area, obtain results of identification and analysis of communication network needs for the Nagari Lubuk Gadang Selatan community in South Solok Regency, obtain results of FTTH network infrastructure design using GPON technology for Nagari Lubuk Gadang Selatan Regency. South Solok.

Congestion control in internet of things (IoT) using auction theory

The Internet of Things (IoT) facilitates data transmission through communication networks, preventing congestion when input data rate exceeds output, and congestion control in computer networks modulates traffic entry. This paper proposes a fusion of auction theory with reinforcement learning as a means of managing congestion in the IoT. The proposed technique seeks to enhance network performance by utilizing object trustworthiness evaluation and auction-based route selection to manage congestion during data routing. The suggested method calculates the believability of objects by analyzing their historical performance in data forwarding and congestion avoidance, utilizing a learning automaton. The auction approach is employed to determine the most efficient ways for transmitting data. The IoT topology is initially organized into a collection of dependable links known as the Connected Dominating Set (CDS). Active objects employ the learning automata model to assess the reliability of their neighbors. The auction model ultimately chooses the optimal route based on characteristics such as credibility, energy, and delay. The experimental results demonstrate that the proposed methodology surpasses existing comparison methods in the initial scenario, exhibiting a 24.13% reduction in energy usage.

Analysis and selection of the structure of a multiprocessor computing system according to the performance criterion

Objectives. Analysis of the various architectures of computing systems (CSs) used in recent decades has allowed us to identify the most common structures. One of the key features is the use of mass-produced equipment to create data processing subsystems (for example, multicore processors and high-capacity semiconductor memory), as well as network equipment to build communication subsystems. This reduces hardware costs and allows typical or cluster configurations to be created, which is especially important for expensive CSs. The desire to achieve high computational speed and performance in such CSs requires minimizing the time to complete the task and balancing time delays both in data processing subsystems and in the communication subsystem which provides data transmission inside the CS. The aim of this work is to analyze computing modules (CMs) and structures on the basis of which the construction of cluster CSs is carried out.Methods. The main results of the work were obtained using methods of mathematical analysis and modeling.Results. The study considers the structure of modern multicore microprocessors as the basis for building CMs of cluster CSs. As the number of cores in the microprocessor structure increases, the communication network which unites them into a single structure becomes more complicated. It has been shown that in new developments of microprocessors, communication between cores is performed in the form of a network. The microprocessors themselves are MIMD structures in accordance with the well-known Flynn classification.Conclusions. The proposed method of selecting an effective structure of a CS allows us to obtain the optimal structure of a CS according to the criterion of performance.

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Deciphering the evolving niche interactome of human hematopoietic stem cells from ontogeny to aging.

Hematopoietic stem cells (HSC) reside within specialized microenvironments that undergo dynamic changes throughout development and aging to support HSC function. However, the evolving cell-cell communication networks within these niches remain largely unexplored. This study integrates single-cell RNA sequencing datasets to systematically characterize the HSC niche interactome from ontogeny to aging. We reconstructed single-cell atlases of HSC niches at different developmental stages, revealing stage-specific cellular compositions and interactions targeting HSC. During HSC maturation, our analysis identified distinct patterns of ligand-receptor interactions and signaling pathways that govern HSC emergence, expansion, and maintenance. HSC aging was accompanied by a decrease in supportive niche interactions, followed by an adaptive increase in interaction strength in old adult bone marrow. This complex aging process involved the emergence of interactions associated with inflammation, altered stem cell function, and a decline in the efficacy of key signaling pathways. Our findings provide a comprehensive understanding of the dynamic remodeling of the HSC niche interactome throughout life, paving the way for targeted interventions to maintain HSC function and promote healthy aging. This study offers valuable insights into the intricate cell-cell communication networks that govern HSC behavior and fate, with implications for hematological disorders and regenerative medicine.

Smooth muscle-specific deletion of cellular communication network factor 2 causes severe aorta malformation and atherosclerosis.

Cellular communication network factor 2 (CCN2) is a matricellular protein implicated in fibrotic diseases, with ongoing clinical trials evaluating anti-CCN2-based therapies. By uncovering CCN2 as abundantly expressed in non-diseased artery tissue, this study aimed to investigate the hypothesis that CCN2 plays a pivotal role in maintaining smooth muscle cell (SMC) phenotype and protection against atherosclerosis. Global- and SMC-specific Ccn2 knockout mouse models were employed to demonstrate that Ccn2 deficiency leads to SMC de-differentiation, medial thickening, and aorta elongation under normolipidaemic conditions. Inducing hyperlipidaemia in both models resulted in severe aorta malformation and a 17-fold increase in atherosclerosis formation. Lipid-rich lesions developed at sites of the vasculature typically protected from atherosclerosis development by laminar blood flow, covering 90% of aortas and extending to other vessels, including coronary arteries. Evaluation at earlier time points revealed medial lipid accumulation as a lesion-initiating event. Fluorescently labelled LDL injection followed by confocal microscopy showed increased LDL retention in the medial layer of Ccn2 knockout aortas, likely attributed to marked proteoglycan enrichment of the medial extracellular matrix. Analyses leveraging data from the Athero-Express study cohort indicated the relevance of CCN2 in established human lesions, as CCN2 correlated with SMC marker transcripts across 654 transcriptomically profiled carotid plaques. These findings were substantiated through in situ hybridization showing CCN2 expression predominantly in the fibrous cap. This study identifies CCN2 as a major constituent of the normal artery wall, critical in regulating SMC differentiation and aorta integrity and possessing a protective role against atherosclerosis development. These findings underscore the need for further investigation into the potential effects of anti-CCN2-based therapies on the vasculature.

Graph theoretic visualization of patient and health worker messaging in the EHR.

The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout. To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia. We quantified the burden of messages interacted with by each health worker type and visualized the communication patterns using graph theory. Our analysis included 76,694 conversations comprising 144,369 messages sent between 47,460 patients and 3,749 health workers across 85 healthcare specialties. On average, nurses/certified nursing assistants/medical assistants (nurses/CNA/MA) interacted with the most messages (350), followed by non-physician practitioners (NPP) (241), physicians (166), and support staff (155), with the average conversation involving 10.51 interactions before resolution. Network analysis of the communication flow revealed that each health worker was connected to approximately two other health workers (average degree = 2.10). In message sending, support staff led in closeness centrality (0.44), followed by nurses/CNA/MA (0.41), highlighting their key role in fast information spread. For message reception, nurses/CNA/MA (0.51) and support staff (0.41) also had the highest values, underscoring their vital role in the communication network on the receiving end as well. Our analysis demonstrates the feasibility of applying graph theory to understand communication dynamics between patients and health workers and highlights the burden of EHR-based messaging.

Software Defined Network Based Next Generation Mobile Communication Network Architecture

As mobile networks and network speeds become more prevalent, the demand for marketing strategies increases. The operators are thinking about this, and the development of 5G communication networks is one of their main concerns. As the need for higher transmission speeds increases, 5G networks face challenges of scalability and adaptability. The nextgeneration mobile network (NMCN-SDN) architecture proposed in this study is based on Software-Defined Networks (SDN). A new network model called computer-defined networking allows for dynamic network definition and programming. A network simulator is created to examine the efficiency of the built infrastructure under different network conditions, including throughput, latency, and resource consumption. In this paper, the comparison of end-toend latency between the standard communication architecture and the proposed NMCN-SDN architecture is done. The results show that the proposed architecture has less space in various conditions compared to existing communication architectures in various conditions.

Bridging the Gap: Insights From Telepractice Augmentative and Alternative Communication Services in the Digital Age

Purpose: The United States Society for Augmentative and Alternative Communication and the Patient-Provider Communication Network sought to quantify the impact of utilization of and access to telepractice for the provision of augmentative and alternative communication (AAC) services. The purpose of this article is to summarize findings from two surveys that explored the kinds of AAC services delivered remotely, barriers to AAC addressed through telepractice, and impact of the availability of remote AAC services on people with communication disabilities and their caregivers. Method: The study team developed two online surveys: (a) for speech-language pathologists (SLPs) and (b) for individual AAC clients (or family members in the case of pediatric clients). Results: Survey responses were obtained from 66 SLPs and 90 AAC clients/family members. Access to telepractice allowed clients/family members to overcome both access and financial barriers. Respondents indicated that the availability of AAC telepractice services reduced transportation barriers, increased access to services by appropriately trained clinicians, and improved the outcomes for individuals needing AAC. Conclusions: Telepractice has made it possible for individuals with significant medical challenges, as well as those residing in areas lacking qualified providers, to receive services. Given that both clinicians and clients/families indicated that travel to receive services posed a significant barrier to access medically necessary treatment, the anticipated December 2024 end of Centers for Medicaid and Medicare Services' authorization of reimbursement for telepractice services will be detrimental to many needing AAC services. Supplemental Material: https://doi.org/10.23641/asha.27885447

Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning

In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system.

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

A cooperative control strategy for balancing SoC and power sharing in multiple energy storage unit within DC microgrids

AbstractThis paper proposes a distributed cooperative control scheme for multiple energy storage unit (ESU) in DC microgrids to achieve the control objectives of SoC balancing, power sharing, and bus voltage recovery. In the primary control part, the proposed scheme constructs a control function between the SoC values of each ESU and the droop coefficients to dynamically adjust the droop coefficients. Through a communication network, information is exchanged with neighbouring ESUs to achieve SoC convergence. In the secondary control part, by exchanging power information with neighbouring ESUs, precise power distribution is achieved. Additionally, the proposed scheme maintains bus voltage stability. Finally, a DC microgrid simulation model and experimental platform were developed, demonstrating the feasibility and plug‐and‐play capability of the proposed control strategy through both simulation and experimental case tests.

Networked control systems induced delay modeling and stability analysis using Markov-regime-switching GARCH model

This article focuses on the stability analysis of networked control systems (NCSs). It is well-known that network-induced delays pose many control challenges due to their non-uniform and multifractal nature. With the growing integration of communication networks in control systems, the impact of time-varying and random delays on system performance becomes critical. Hence, it is essential to study their impact on the system’s stability. There are several network-induced delay modeling methods, resulting in constant, random, and probabilistic random delays, which provide the most realistic representation according to recent studies. This article introduces a new network-induced delay modeling method based on the Markov-regime-switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) process, which captures the intricate temporal dependence of random network delays. The resulting model offers a more accurate depiction of delay volatility behavior, accommodating sudden shifts between volatility regimes. Simulation results demonstrate that MRS GARCH-type modeling enables a more realistic representation of delay dynamics. We also conduct a stability analysis of NCSs modeled with the proposed method using linear matrix inequalities LMIs criteria derived from the Lyapunov-Krasovskii theorem. The simulation results show less conservatism than conventional models and provide a larger maximum allowable upper delay bound MAUDB.

Threading Statistical Disclosure Attack with EM: An Algorithm for Revealing Identity in Anonymous Communication Networks

Messages sent across multiple platforms can be correlated to infer users’ attitudes, behaviors, preferences, lifestyles, and more. Therefore, research on anonymous communication systems has intensified in the last few years. This research introduces a new algorithm, Threading Statistical Disclosure Attack with EM (TSDA-EM), that employs real-world data to reveal communication’s behavior in an anonymous social network. In this study, we utilize a network constructed from email exchanges to represent interactions between individuals within an institution. The proposed algorithm is capable of identifying communication patterns within a mixed network, even under the observation of a global passive attacker. By employing multi-threading, this implementation reduced the average execution time by a factor of five when using a dataset with a large number of participants. Additionally, it has markedly improved classification accuracy, detecting more than 79% of users’ communications in large networks and more than 95% in small ones.

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks

The rapid expansion of artificial intelligence (AI) integrated with the Internet of Things (IoT) has fueled the development of various smart devices, particularly for smart city applications. However, the heterogeneity of these devices necessitates a robust communication network capable of maintaining a consistent traffic flow. This paper employs Machine Learning (ML) models to classify continuously received network parameters from diverse IoT devices, identifying necessary adjustments to enhance network performance. Key network traffic parameters, such as packet data, are transmitted through gateways via specialized tools. Six different ML techniques with default parameters were used: Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Stochastic Gradient Descent Classifiers (SGDC), to classify the traffic of the environment (IoT / non IoT). The models' performance was evaluated in a real-time smart laboratory environment comprising 38 IoT devices from various vendors with the following metrics: Accuracy, F1-score, Recall and Precision. The RF model achieved the highest Accuracy of 95.6%. Also the Binary Particle Swarm Optimizer (BPSO) was used across the RF. The results demonstrated that the BPSO-RF with hyperparameter optimization enhanced the Accuracy from 95.6% to 99.4%.

Asymmetric LKF approach for stability analysis of multi-area load frequency control systems with time-varying delays

ABSTRACT The widespread use of communication networks in power systems to send and receive control signals causes time delays in the feedback loop. Improper handling of these delays might degrade controller performance or cause system instability. This work presents new stability criteria that are less conservative in analysing the influence of delays on the stable operation of PI controller-based multi-area power systems with uncertainties and disturbances. An asymmetric Lyapunov-Krasovskii functional technique is used to reduce conservatism by relaxing the condition that all matrices associated with the LKF formulation need not be symmetric or have positive definiteness. In contrast, the symmetric LKF requires all matrices to be symmetric. Two tight-bound integral inequalities are implemented to compute the quadratic integral terms included in the derivative of asymmetric LKF. The less conservative stability conditions are derived for multi-area LFC systems subjected to delays, parameter uncertainties, and disturbances by utilising asymmetric LKF. The superiority of the proposed stability criterion is determined both theoretically and through simulation. According to the analysis, the proposed stability criterion provides higher delay margins than existing techniques.