Total Network Research Articles

An energy minimization strategy based on transmission time and re‐clustering (TTRC) for wireless sensor network

AbstractThe Wireless Sensor Network (WSN) is an emerging technology with diverse applications, ranging from industrial settings to environmental monitoring, healthcare, and remote surveillance. In this paper, the focus lies on the minimization of energy consumption during packet transmission, achieved through two primary strategies: (1) Reduction of the transmission distance for packets through the implementation of re‐clustering. (2) Allocation of increased time to those child nodes which necessitate less energy for transmission. Simulation outcomes demonstrate that the proposed transmission time and re‐clustering (TTRC) algorithm effectively harnesses in excess of 99% of the total network energy, marking an improvement of 14.05% to 15.57% over existing approaches. Furthermore, the TTRC algorithm achieves a substantial enhancement in packet delivery, ranging from 32.31% to 58.54%, coupled with a latency reduction spanning from 21.47% to 32.00%. Moreover, the network's lifestime is prolonged by 28.57% to 28.99% through the application of this algorithm.

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

A modularity total environment network model for sustainable forest transportation

Forest road network planning and development is important in providing accessibility to remote forest areas for timber harvesting, transportation to markets, and recreational activities, as well as preventing environmental degradation, soil erosion, water pollution, and an increased risk of forest fires and wildlife habitat fragmentation. Within the context that careful forest road transportation planning and design promotes sustainable forest management and development, this paper develops a multilayer network model for supporting sustainable forest transportation development. The model builds on a total environment conceptualization and the network paradigm, and it is composed of several layers, each containing information from an environmental aspect or a forest road network land use. By using community detection analysis from network science, the model provides insights into the decomposition of the forest network into functional areas, highlights the importance of places that connect different communities to maintain market integration, and provides a list of policies and good practices for the resulting communities. Overall, this paper presents a quantitative methodological framework that can be used for sustainable forest transportation.

Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis

ObjectiveTemporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE. MethodsWe collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs—regions with high connectivity. ResultsThe language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE. SignificanceOur study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.

ESMA: Towards elevating system happiness in a decentralized serverless edge computing framework

Due to the rapid growth in the adoption of numerous technologies, such as smartphones and the Internet of Things (IoT), edge and serverless computing have started gaining momentum in today's computing infrastructure. It has led to the production of huge amounts of data and has also resulted in increased network traffic, which if not managed well can cause network congestion. To address this and maintain the quality of service (QoS), in this work, a novel dispatch (destination selection) algorithm called Egalitarian Stable Matching Algorithm (ESMA) for faster data processing has been developed while also considering the best use of server resources in a decentralized Serverless-Edge environment. This will allow us to effectively utilize the enormous volumes of data that are generated. The proposed algorithm has been able to achieve lower overall dissatisfaction scores for the entire system. Individually, the client's happiness as well as the server's happiness have improved over the baseline. Moreover, there has been a drop of 25.7% in the total execution time and the total network resources consumed are lower as compared to the baseline algorithm as well as random-allocation algorithm.

Total network controllability analysis discovers explainable drugs for Covid-19 treatment

BackgroundThe active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets.ResultsWe developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach’s effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients.ConclusionsOur findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Social Networking Site Use, Self-Compassion, and Attitudes Towards Cosmetic Surgery in Young Australian Women

This study aimed to explore the relationship of total social networking site (SNS) use and attitudes towards cosmetic surgery in young Australian women through the moderating role of self-compassion. Recent research has identified the potentially protective factor of self-compassion in body image concerns; however, few researchers have further explored the components of self-compassion in relation to SNS use. As such, the positive (self-kindness, common humanity, and mindfulness) and negative (self-judgement, isolation, and over-identification) components of self-compassion were further investigated. The present study utilised a cross-sectional correlational design with self-reported data to determine if self-compassion moderated the relationship between SNS use and attitudes towards cosmetic surgery. A sample of 238 young Australian women, aged 18 to 29 (M = 22.1, SD = 2.9), were recruited. Participants completed an online questionnaire measuring total daily SNS use, self-compassion, and attitudes towards cosmetic surgery. SNS use was significantly negatively correlated with self-compassion and positively correlated with cosmetic surgery. Self-compassion was significantly negatively correlated with attitudes towards cosmetic surgery; however, contrary to expected, self-compassion was not a significant moderator of SNS use and attitudes towards cosmetic surgery. The components of self-compassion were further explored to better understand this relationship, with over-identification significantly predicting more positive attitudes towards cosmetic surgery. The present study highlights the relationship between SNS use and attitudes towards cosmetic surgery. As over-identification relates to ruminating in feelings of discomfort, the results may suggest that SNS users who are more likely to fixate on their appearance dissatisfaction are more likely to have positive attitudes towards cosmetic surgery. Given the popularity of SNS use, this media may pose as an opportune platform to disseminate appearance modification interventions for young adult women. Future research that is causational in nature may be able to confirm these suggested implications.

HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA

To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep learning and traditional methods like RankIQA, BRISQUE, and SSIM, various partitioning schemes were compared to explore the highest-quality imaging heterogeneous processing scheme. The UNet-specific deep-learning processing unit (DPU) based on a field programmable gate array (FPGA) provided a 14.67× acceleration ratio for the total network and for deconvolution and max pool, the calculation latency was as low as 2.46 ms and 97.10 ms, achieving an impressive speedup ratio of 46.30× and 36.49× with only 4.04 W power consumption. The HISP consisting of a DPU and the FPGA-implemented traditional image signal processor (ISP) submodules, which scored highly in the image quality assessment system, with a single processing time of 524.93 ms and power consumption of only 8.56 W, provided a low-cost and fully replicable solution for edge image processing in extremely low illumination and high noise environments.

Interference Aware Joint Power Control and Routing Optimization in Multi-UAV FANETs

In this work, we enhance the total network throughput performance of a FANET system consisting of multiple UAVs and a ground control station (GCS) by optimizing the multihop routing structure and total power budget required for communication in the presence of inter-UAV interference. To achieve this, an optimization problem is formulated to maximize the network throughput within a given power budget. However, due to the non-convex and integer nature of the problem, it is NP-hard. Hence, it is dealt with differently by solving two daughter problems iteratively, i.e., (1) power allocation (PA) and (2) optimal link selection (LS) for the given solution of PA. These subproblems are solved iteratively by utilizing alternating optimization to obtain the global optimal solution of the main problem. The study presents various numerical results, which validate the effectiveness of the proposed routing structure and provide insights into the impact of interference on system parameters.

An approach to internal threats detection based on sentiment analysis and network analysis

Years into the insider threat, it remains an universal challenge to predict and defend. Concerning this problem, there has been a multitude of solutions, including the detection of sentiment fluctuations of an entity to predict its abnormality degree. Previous research solely focus on the analysis of individual anomalies, but ignore the sociality of individual sentiments. Therefore, in this paper, we propose an approach to internal threats detection based on sentiment and network analysis. Above all, we use Natural Language ToolKit (NLTK) to analyze the sentiment of each preprocessed email, and thus we establish sentiment communication network. The graphs are reconstructed into series and reshaped as matrices. By the singular value decomposition (SVD), the matrix is decomposed into base networks, and we analyze the change in importance of important base networks over time to detect the company’s internal conditions. When anomalies occur, we establish combination networks which consist of base networks and can represent the abnormal character patterns of this time. Then compare the eigenvector centrality of both combination networks and total network to identify the employees related to this anomaly. The experiment shows that it can locate the anomaly-related employees accurately, and according to the network graph, the top few of anomaly-related employees are associated in group internal threats. Our approach provides the anomaly-related rank of employees and the abnormal character pattern of the sentiment communication network at the moment of anomaly.

Potential regulatory genes of light induced anthocyanin accumulation in sweet cherry identified by combining transcriptome and metabolome analysis.

Anthocyanins exist widely in various plant tissues and organs, and they play an important role in plant reproduction, disease resistance, stress resistance, and protection of human vision. Most fruit anthocyanins can be induced to accumulate by light. Here, we shaded the "Hong Deng" sweet cherry and performed an integrated analysis of its transcriptome and metabolome to explore the role of light in anthocyanin accumulation. The total anthocyanin content of the fruit and two of its anthocyanin components were significantly reduced after the shading. Transcriptome and metabolomics analysis revealed that PAL, 4CL, HCT, ANS and other structural genes of the anthocyanin pathway and cyanidin 3-O-glucoside, cyanidin 3-O-rutinoside, and other metabolites were significantly affected by shading. Weighted total gene network analysis and correlation analysis showed that the upstream and middle structural genes 4CL2, 4CL3, and HCT2 of anthocyanin biosynthesis may be the key genes affecting the anthocyanin content variations in fruits after light shading. Their expression levels may be regulated by transcription factors such as LBD, ERF4, NAC2, NAC3, FKF1, LHY, RVE1, and RVE2. This study revealed for the first time the possible role of LBD, FKF1, and other transcription factors in the light-induced anthocyanin accumulation of sweet cherry, thereby laying a preliminary foundation for further research on the role of light in anthocyanin accumulation of deep red fruit varieties and the genetic breeding of sweet cherry.

On three shades of partial protection in elastic optical networks

Gone have been the days when the allocation of spectrum resources for a demand is over-provisioned due to worst-case based designs and the optical transmission technologies are based on the fixed spectral grid, modulation format and/or line-rate schemes. Elastic optical networks technologies featuring adaptive spectrum allocation mechanisms have been becoming the architecture and technology of choice for future core networks to meet explosive traffic growths in a cost and energy-efficient manner. Partial protection strategies based on the observation that in failure events, a service can tolerate a certain amount of degradation and therefore by reducing the protection traffic in the network, better spectrum utilization could be attained. Such concept has been widely studied in the traditional wavelength division multiplexing (WDM) context and yet has been somehow faded due to the fact that fixed transmission technologies provide limited room for spectral improvements. This paper aims to renew the interest in partial protection and re-adapt it to the context of elastic optical networks. The evolutionary perspective we lay out in this paper identifies a new route for achieving greater spectral efficiency in a pragmatic way by simply differentiating the protection services for each demand rather than the uniform treatment for all demands. As such, we present a new research problem entitled, routing, modulation level, spectrum and protection service assignment which is essentially an extension of the well-established one, that is, routing, modulation level, and spectrum assignment as the (partial) protection service for each demand is taken into account and optimized. Three variants of that problem reflecting shades of applying partial protection are covered in details. Specifically, the first one considers the intuitive case as the amount of protection traffic for each demand is provided as the input to the network planning process while the second one is dedicated to the special case of enforcing the same level of partial protection for all demands. More interesting case emerges in the third variant where given the service level agreement for the total protected network traffic, we provide the optimal solution that determines the protection service for each individual demand so as to minimize the spectral occupancy. Extensive numerical simulations have been conducted in the realistic COST239 network topology and it has been revealed that a remarkable spectral saving could be achieved thanks to introducing partial protection services. Moreover, our results have demonstrated that the demand-wise approach far outperforms the one-size-fits-all one and the difference could be up to 25% in our studied cases.

Proteome and Interactome Linked to Metabolism, Genetic Information Processing, and Abiotic Stress in Gametophytes of Two Woodferns.

Ferns and lycophytes have received scant molecular attention in comparison to angiosperms. The advent of high-throughput technologies allowed an advance towards a greater knowledge of their elusive genomes. In this work, proteomic analyses of heart-shaped gametophytes of two ferns were performed: the apomictic Dryopteris affinis ssp. affinis and its sexual relative Dryopteris oreades. In total, a set of 218 proteins shared by these two gametophytes were analyzed using the STRING database, and their proteome associated with metabolism, genetic information processing, and responses to abiotic stress is discussed. Specifically, we report proteins involved in the metabolism of carbohydrates, lipids, and nucleotides, the biosynthesis of amino acids and secondary compounds, energy, oxide-reduction, transcription, translation, protein folding, sorting and degradation, and responses to abiotic stresses. The interactome of this set of proteins represents a total network composed of 218 nodes and 1792 interactions, obtained mostly from databases and text mining. The interactions among the identified proteins of the ferns D. affinis and D. oreades, together with the description of their biological functions, might contribute to a better understanding of the function and development of ferns as well as fill knowledge gaps in plant evolution.

Temperature zone diagram method for designing the total site exchanger network

Temperature zone diagram method for designing the total site exchanger network

Dynamics of Thermospheric Traveling Atmospheric Disturbance During a Geomagnetic Storm

AbstractTraveling atmospheric disturbance (TAD) plays an important role in the energy and momentum transfer from the lower atmosphere to the upper atmosphere, and from high‐ to low‐latitudes. It is common to observe TADs propagating toward low latitudes because of enhanced Joule heating and/or the Lorentz force at the high‐latitude ionosphere during storm time. However, energy or momentum variation associated with their equatorward propagation remains unclear. Two geomagnetic storms occurred on 7–8 September 2017 and upper atmospheric disturbances are observed by the Swarm satellites and the Global Navigation Satellite System Total Electron Content network. We conduct a model simulation and term analysis of the energy equation to investigate the dominant terms of TAD. Adiabatic heating, conduction heating, and advection heating dominate the energy budget of TAD. Adiabatic heating plays an important role in the energy budget by transferring the most energy of TAD. An anti‐phase relationship between adiabatic and conduction heating is found in the propagation of these TADs. An in‐phase relationship between adiabatic and advection heating is also found. Physical processes behind these anti‐phase and in‐phase relationships are illustrated with a schematic. Finally, based on the dominant terms and the relationships between them, the whole process of generation, propagation, and dissipation of TAD is given.

Efficient automated car parking system based modified internet of spatial things in smart cities

The technological advances of smart cities have been progressively increasing to improve the quality of life to humans, especially in urban mobility. Parking appears to be a major issue, with residents needing to find a suitable parking space among many parking areas, resulting in time and fuel waste as well as environmental pollution. We propose in this paper a new automated system model that integrates reinforcement learning (RL), Q-learning, and image processing algorithms based on modified Internet of Spatial Things (IoST) architecture to optimize automated parking in smart cities. For demonstrating the efficiency of the proposed model, iFogSim simulation is used to reduce network usage and latency. Moreover, it deploys heterogeneous devices in multi layers and different scenarios. The experimental results show that the suggested system for automated car parking in fog-based placement-IoST network is feasible and effective. it minimizes latency and the total network usage compared to the cloud-based placement of the implemented system.

Designing a resilient and green coal supply chain network under facility disruption and demand volatility

Few studies have addressed strategic-level disruptions and operational-level risks for the coal industry in the literature on mining supply chain management. In many developing countries, the coal industry plays a leading role in electric power generation and primarily contributes to carbon emissions. This study aims to design a resilient and green coal supply chain network (RG-CSCN) considering strategic-level disruptions, operational-level risks, and carbon emissions. A practical stochastic mixed-integer programming model is developed to optimise both economic and environmental objectives (i.e., the total network cost and the total carbon emissions cost). Our proposed model involves five resilience strategies: facility fortification, reserving emergency inventory, direct-to-port delivery, establishing reliable distribution centres, and multiple transportation routes. Based on real-world data from the coal industry, we conduct extensive computational experiments to evaluate the impacts of strategic-level disruptions and operational-level risks and to validate the applicability of the proposed five resilience strategies and their synergistic performance in coal supply chain management. Moreover, sensitivity analyses are carried out to examine the influences of different customer service levels and carbon tax policies in various scenarios.

Load balancing clustering and routing for IoT‐enabled wireless sensor networks

AbstractInternet of things (IoT) devices are equipped with a number of interconnected sensor nodes that relies on ubiquitous connectivity between sensor devices to optimize information automation processes. Because of the extensive deployments in adverse areas and unsupervised nature of wireless sensor networks (WSNs), energy efficiency is a significant aim in these networks. Network survival time can be extended by optimizing its energy consumption. It has been a complex struggle for researchers to develop energy‐efficient routing protocols in the field of WSNs. Energy consumption, path reliability and Quality of Service (QoS) in WSNs became important factors to be focused on enforcing an efficient routing strategy. A hybrid optimization technique presented in this paper is a combination of fuzzy c‐means and Grey Wolf optimization (GWO) techniques for clustering. The proposed scheme was evaluated on different parameters such as total energy consumed, packet delivery ratio, packet drop rate, throughput, delay, remaining energy and total network lifetime. According to the results of the simulation, the proposed scheme improves energy efficiency and throughput by about 30% and packet delivery ratio and latency by about 10%, compared with existing protocols such as Chemical Reaction Approach based Cluster Formation (CHRA), Hybrid Optimal Based Cluster Formation (HOBCF), GWO‐based clustering (GWO‐C) and Cat Swarm Optimization based Energy‐Efficient Reliable sectoring Scheme with prediction algorithms (P_CSO_EERSS). The study concludes that the protocol suitable for creating IoT monitoring system network lifetime is an important criteria.

A multi-objective solution framework for the assembly inventory routing problem considering supply risk and carbon offset policies

This study aims to provide novel insights into the multi-objective optimisation of an inbound assembly inventory routing problem (IRP) by considering customer demand uncertainty, stochastic supply failure risks, and a carbon offset policy. An inbound IRP network generally comprises several geographically dispersed suppliers, vehicle depots, and assembly plants. In this study, a new mixed-integer nonlinear optimisation model is formulated considering two objective functions: (i) minimising the total supply network cost and (ii) maximising a low-risk supplier priority function with practical constraints. A hybrid non-dominated sorting genetic algorithm-II was developed to obtain near-optimal Pareto solutions for various small, medium, and large IRP instances. The Pareto solutions were compared with the efficient solution obtained using the ε-constraint method. Finally, the best Pareto solution was obtained using the multi-criteria decision-making technique. The results indicate that implementing supply risk-mitigation strategies leads to higher overall supply network costs and carbon emissions. However, a sensitivity analysis based on factorial simulation experiments indicates that a supply risk-mitigation strategy is advantageous if component prices are low and supply capacity, fuel price, and demand variations are high. In addition, the lower the demand variation, the lower the expected overall network cost and carbon emissions.