Scalable Network Research Articles

Scalability and Performance Analysis of LoRaWAN Networks for IoT Implementation: Comprehensive Review and Research Agenda

The surge in IoT devices has created the need for a scalable and high-performance network infrastructure capable of handling massive data flows and diverse application requirements. Among the various wireless technologies available, with long-range communication capabilities, low power consumption, and cost-effectiveness, the Low-Power Wide-Area Network (LPWAN) has emerged as a highly popular choice for numerous applications. This literature review aims to provide a comprehensive overview of recent developments in scalability and performance analysis of LoRaWAN networks for IoT implementation by reviewing existing research and identifying key challenges and opportunities for the research agenda. In the research on LoRaWAN performance in IoT, five characteristics have been proven and become factors for using LoRaWAN in IoT: low energy power usage, wide coverage, low cost, stable data transmission speed, and system reliability. However, not many have discussed its security. Based on the literature review, LoRaWAN does have some characteristics that make it suitable for large-scale IoT applications. This review will contribute to advancing knowledge in this field, enabling the development of scalable and high-performance IoT systems to meet the demands of the growing IoT ecosystem

How do social networks, perception of social isolation, and loneliness affect depressive symptoms among Japanese adults?

This study aims to elucidate the complex relationship among social isolation, loneliness, and perception of social isolation and its influence on depressive symptoms by evaluating a hypothetical model. This understanding is essential for the formulation of effective intervention strategies. We conducted an online survey on Japanese adults (N = 3,315) and used the six-item Lubben Social Network Scale to assess the size of their social networks. We employed a single question to gauge their perception of social isolation. Loneliness was assessed using the three-item UCLA Loneliness Scale, and depressive symptoms were examined using the Patient Health Questionnaire-9. Structural equation modeling was employed to test the hypothesized model. The final model demonstrated satisfactory fit with data (χ2 (1) = 3.73; not significant; RMSEA = 0.03; CFI = 1.00; TLI = 1.00). The size of social network demonstrated a weak negative path to loneliness and depressive symptoms (β = -.13 to -.04). Notably, a strong positive association existed between perception of social isolation and loneliness (β = .66) and depressive symptoms (β = .27). Additionally, a significant positive relationship was found between loneliness and depressive symptoms (β = .40). Mediation analysis indicated that perception of social isolation and loneliness significantly intensified the relationships between social networks and depressive symptoms. Results indicate that interventions of psychological approaches, such as cognitive-behavioral therapy, are effective in reducing the perception of social isolation and loneliness, which may lead to the prevention of depressive symptoms. Future longitudinal studies are expected to refine and strengthen the proposed model.

Cross-cultural adaptation and psychometric evaluation of the social frailty scale in Iranian older adults

BackgroundSocial frailty is a holistic concept encompassing various social determinants of health. Considering its importance and impact on health-related outcomes in older adults, the present study was conducted to cross-culturally adapt and psychometrically evaluate the Social Frailty Scale in Iranian older adults in 2023.MethodsThis was a methodological study. The translation and cross-cultural adaptation of the Social Frailty Scale 8-item (SFS-8) was conducted according to Wild’s guideline. Content and face validity were assessed using qualitative and quantitative methods. Then, 250 older adults covered by comprehensive health centers were selected using multistage random sampling. Participants completed the demographic questionnaire, the Abbreviated Mental Test score, the SFS-8, and the Lubben Social Network Scale. Construct validity was assessed by principal component analysis (PCA) and known-group comparisons. The Mann‒Whitney U test was used to compare social frailty scores between the isolated and non-isolated older adults. Internal consistency, equivalence, and stability were assessed using the Kuder-Richardson method, the intraclass correlation coefficient (ICC), the standard error of measurement (SEM), and the minimum detectable change (MDC). The ceiling and floor effects were also assessed. The data were analyzed using JASP 0.17.3.ResultsThe ratio and index of content validity and the modified kappa coefficient of all the items were 1.00. The impact score of the items was greater than 4.6. PCA identified the scale as a single component by removing two questions that could explain 52.9% of the total variance in the scale score. The Persian version of the Social Frailty Scale could distinguish between isolated and non-isolated older adults (p < 0.001). The Kuder–Richardson coefficient, ICC, SEM, and MDC were 0.606, 0.904, 0.129, and 0.358, respectively. The relative frequencies of the minimum and maximum scores obtained from the scale were 34.8 and 1.2, respectively.ConclusionsThe Persian version of the Social Frailty Scale (P-SFS) can be used as a valid and reliable scale to assess social frailty in Iranian older adults.

SUDC: Synchronous Update with the Division and Combination of SRv6 Policy

With the expansion of network scale, new network services are emerging. Segment Routing over IPv6 (SRv6) can meet the diverse needs of more new services due to its excellent scalability and programmability. In the intelligent 6-Generation (6G) scenario, frequent SRv6 Traffic Engineering (TE) policy updates will result in the serious problem of unsynchronized updates across routers. Existing solutions suffer from issues such as long update cycles or large data overhead. To optimize the policy-update process, this paper proposes a scheme called Synchronous Update with the Division and Combination of SRv6 Policy (SUDC). Based on the characteristics of the SRv6 TE policy, SUDC divides the policies and introduces Bit Index Explicit Replication IPv6 Encapsulation (BIERv6) to multicast the policy blocks derived from policy dividing. The contribution of this paper is to propose the policy-dividing and combination mechanism and the policy-dividing algorithm. The simulation results demonstrate that compared with the existing schemes, the update overhead and update cycle of SUDC are reduced by 46.71% and 46.6%, respectively. The problem of unsynchronized updates across routers has been further improved.

Effect of Meta-Cognitive Strategy-Based Activities on Executive Functioning, Social Networking, and Instrumental Activities of Daily Living Among Women.

Background Aging has an impact on women's quality of life and is closely correlated with deteriorating social support systems and cognitive abilities. This project intends to improve women's performance, independence, and quality of life by using meta-cognitive strategy-based activities to improve executive functioning and their capacity to maintain social networks. The ultimate objective is to keep older women's cognitive abilities from declining and to give them more independence in carrying out essential everyday tasks. By enhancing their executive functioning skills, women will be better equipped to navigate daily challenges and maintain their autonomy. Additionally, the project aims to provide a supportive environment for older women to engage in social activities and strengthen their social connections, ultimately leading to a more fulfilling and enriched quality of life. Aims and objectives The study aims to evaluate the impact of meta-cognitive strategy-based activities on executive functioning, social networking, and instrumental activities of daily living. This study is specifically for women. Materials and method This is aquasi-experimental designwith convenience sampling and selected samples(n=70), who were then splitinto two groups: an experimental group (n=35) and a control group (n=35) based on the inclusion and exclusion criteria. The experimental group received intervention based on meta-cognitive strategies through various activities, while the control group did not receive any specific intervention except health education. Pre- and post-tests were conducted to measure the changes. Executive functioning was assessed using the Brief Executive Function Assessment Tool (BEAT), social networking was assessed using the Lubben Social Networking Scale, and instrumental activities of daily living were assessed using the Lawton Brody Instrumental Activities of Daily Living Scale. The duration of the study was six months,three sessions per week, lasting 45 minutes to an hour each. The statistical analysis was done with significance at a 5% alpha level using IBM SPSS version 29.0 (Armonk, NY: IBM Corp). Results The pre-test and post-test data were analyzed using the Wilcoxon signed-rank test and the Mann-Whitney test.Since the p-value of the experimental group was 0.00 for the Lubben Social Networking Scale, the BEAT, and the Lawton Brody Instrumental Activities of Daily Living Scale, the alternate hypothesis was accepted. Thus, the results showed significant improvement in executive functioning, social networking, and instrumental daily living activities in the experimental group compared to the control group. Conclusion The clinically significant finding of this study was that the participants were enthusiastic and motivated to engage in meta-cognitive strategy-based activities; furthermore, there was a significant improvement in the experimental group when compared with the control group in executive functioning and social networking abilities. This will enhance functions like instrumental activities of daily living and occupational performance. Meta-cognitive strategy-based activities appeared beneficial for improving the cognitive functions and quality of life of women. Although the findings from the studies arepositive for therapeutic and health benefits, more clinical trials are needed in order to prove the effect of meta-cognitive strategy-based activities as a therapeutic approach.

When Optimization Meets AI: An Intelligent Approach for Network Disintegration with Discrete Resource Allocation

Network disintegration is a fundamental issue in the field of complex networks, with its core in identifying critical nodes or sets and removing them to weaken network functionality. The research on this problem has significant strategic value and has increasingly attracted attention, including in controlling the spread of diseases and dismantling terrorist organizations. In this paper, we focus on the problem of network disintegration with discrete entity resources from the attack view, that is, optimizing resource allocation to maximize the effect of network disintegration. Specifically, we model the network disintegration problem with limited entity resources as a nonlinear optimization problem and prove its NP-hardness. Then, we design a method based on deep reinforcement learning (DRL), Net-Cracker, which transforms the two-stage entity resource and network node selection task into a single-stage object selection problem. Extensive experiments demonstrate that compared with the benchmark algorithm, Net-Cracker can improve the solution quality by about 8∼62%, while enabling a 30-to-160-fold speed up. Net-Cracker also exhibits strong generalization ability and can find better results in a near real-time manner even when the network scale is much larger than that in training data.

User tendency-based rating scaling in online trading networks.

Social networks often involve the users rating each other based on their beliefs, abilities, and other characteristics. This is particularly common in e-commerce platforms where buyers rate sellers based on their trustworthiness. However, the rating tends to vary between users due to differences in their individual scoring criteria. For example, in a transaction network, a positive user may give a high rating unless the transaction was unsatisfactory while a neutral user may give a mid-rating, consequently giving the same numeric score to different levels of satisfaction. In this paper, we propose a novel method called user tendency-based rating scaling, which adjusts the current rating (its score) based on the pattern of past ratings. We investigate whether this rating scaling method can classify between "good users" and "bad users" in online trade social networks better when compared with using the original rating scores without scaling. Classifying between good users and bad users is especially important for anonymous rating networks like Bitcoin transaction networks, where users' reputations must be recorded to preclude fraudulent and risky users. We evaluate the proposed rating scaling method by performing user classification, link prediction, and clustering tasks, using three real-world online rating network datasets. We use both the original ratings and the scaled ratings as weights of graphs and use a weighted graph embedding method to find node representations that reflect users' positive and negative information. The experimental results showed that using the proposed rating scaling method outperformed using the original (i.e., unscaled) ratings by up to 17% in classification accuracy, and by up to 2.5% in link prediction based on the AUC ROC measure, and by up to 21% in the clustering tasks based on the Dunn-index.

The geographies and complexities of online networks in the off-street sex market

Exploitation and human trafficking in sex markets tend to include both online and offline spaces. Understanding the scale, complexity and geography of networks is important in policing human trafficking and online escort adverts are often used to identify organised crime in this context. This article aims to make a methodological contribution to how data relating to online networks in the sex market can be collected and analysed. Through the application of web scraping, social network analysis and principal component analysis, the digital traces of 15,016 online networks operating on an adult services website were analysed in relation to their complexity and geographical patterning. The findings suggest that structural and geographical characteristics are useful for understanding the heterogeneity of online networks. Analysing networks, as opposed to assessing escort adverts, offers a more robust approach to understanding the sex market, which is more sensitive to the continuum of experiences encapsulated therein.

ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations.

In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms. The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.

Social Networks and Communication Impairment: Racial Differences Among Stroke Survivors

Purpose: Recent evidence highlights the importance of social networks—the composition of social relationships—and their characteristics in stroke recovery. This study explores the relationship between social networks and poststroke communication outcomes. Method: A cohort of 30 non-Hispanic Black ( n = 12) and non-Hispanic White ( n = 18) adult stroke survivors completed the Stroke Impact Scale (SIS) Communication subtest and the Stroke Social Network Scale (SSNS). The SSNS captures elements of social networks including satisfaction and contact with friends, relatives, children, and groups. The relationship between the SIS Communication subtest scores and SSNS scores was explored using ordinal Bayesian regression estimation models adjusting for sex, age, time postonset, marital status, and stroke type. Results: Average SIS Communication scores differed ( t = 2.07, p = .335) between White ( M = 81.75, SD = 22.90) and Black ( M = 95.71, SD = 4.95) participants, but SSNS Comprehensive scores were similar between the two groups ( t = −0.89, p = 1.00). SSNS children subtest scores were higher among the Black than the White ( t = −1.73, p = .0473) participants. Similarly, satisfaction subtest scores were comparatively higher among Black than White participants ( t = 1.85, p = .451). SSNS Satisfaction, Friends, and Groups subtest scores were positively associated with SIS Communication, indicating strong associations with lower impairment. SSNS Children subtest scores were negatively related to SIS, indicating higher impairment. Although most subtest associations were similar for both groups, the associations with SSNS Children and Friends subtests differed between racial groups. Conclusions: Levels of communication impairment were correlated with child, friendship, and group attachments among participants. However, strong social ties to children and friends had different associations among racial groups.

Fully Distributed Economic Dispatch with Random Wind Power Using Parallel and Finite-Step Consensus-Based ADMM

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy.

Leveraging Model Scaling and Butterfly Network in the Bone Scan Image Segmentation

As we all know, cancer is one of the leading causes of death worldwide and the second leading cause of death overall. This is why regular screenings or health checks are necessary to detect cancer lesions early. Since bone scan images have become the primary means of detecting the emergence of cancer lesions on bone, high segmentation accuracy is essential for establishing the model of some predefined regions in bone scan images where cancer metastasis was predicted to appear. Consequently, robust localization and identification of the specific region in bone scan images are required for automated metastasis detection. To this end, we propose Efficient-BtrflyNet, a new deep learning-based architecture for skeleton segmentation of whole-body bone scan images. The proposed architecture exploits the benefits of EfficientNet’s model scaling and the encoder–decoder design of butterfly-type networks. We added EfficientNetB7 to the encoder section to obtain more specific features. The proposed architecture simultaneously processes anterior and posterior whole-body bone scan images. Using 37 bone scan images, we evaluated the performance of our proposed skeleton segmentation system using the Dice score. Efficient-BtrflyNet achieves superior segmentation performance compared to the existing representative method.

Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring.

Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

Impact of the Russia–Ukraine Conflict on Global Marine Network Based on Massive Vessel Trajectories

Maritime transportation plays a vital role in global trade, and studying the resilience of the global maritime network is crucial for ensuring its sustainable development. Currently, the ongoing conflict between Russia and Ukraine has garnered significant global attention. However, there is a lack of specific research on the impact of the conflict on maritime shipping, particularly the resilience of the global maritime network. This paper proposes a resilience assessment framework under the influence of significant events by combining complex network metrics and network performance indicators from the resilience triangle model. It quantitatively evaluates the resilience changes in the global maritime network before and after the outbreak of the Russia–Ukraine conflict. The experiment utilizes real automatic identification system (AIS) maritime trajectory data to quantify and visualize the changes in global maritime traffic during a 20-day period before and after the conflict, constructing the global maritime network for resilience calculations. The research findings indicate the following changes occurred after the Russia–Ukraine conflict. Firstly, the global maritime industry experienced overall growth, with increased ship transportation between ports. Transportation in certain regions was negatively affected, with a significant decrease in ship activities in the Black Sea and Adriatic Sea areas. The positions of Russia and Ukraine in the world maritime industry noticeably declined. Secondly, the network connectivity, network size, and network density of the global maritime network significantly increased, indicating an enhanced network resilience. According to our quantitative results, from a topological perspective, we observed the following changes: network connectivity increased by 27.2%, network scale increased by 36.6%, network density increased by 32.4%, and network resilience increased by 18.6%. Thirdly, the global maritime network is characterized by a high degree of heterogeneity, and the impact of conflicts on the heterogeneity of the shipping network is not significant. Finally, the network exhibited a slower performance decline under random attacks, while deliberate attacks led to a sharp decline. Due to the adaptive nature of the maritime network, the resilience of the network improves in terms of its topology following the outbreak of conflicts. After conflict incidents, the rate of performance decline during simulated attacks is lower compared to the pre-conflict period.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

5G QoS model for Time-Sensitive Networking

Abstract Flexible production processes and applications for Industry 4.0 expect a seamless, deterministic communication over scalable infrastructure networks that rely on a combination of wired and wireless technologies. Time-Sensitive Networking (TSN) and 5G are considered key technologies to meet the communication requirements of Industry 4.0. To integrate 5G into TSN, the 3rd Generation Partnership Project (3GPP) specified the 5G system (5GS) as Ethernet bridge, to exchange time synchronization and Quality of Service (QoS) requirement information between wired TSN entities and 5GS. QoS mapping between TSN and 5G is non-trivial and requires a deeper analysis of relevant parameters and QoS processing. In this paper, different solution approaches for QoS mapping between TSN and 5G are presented and discussed. The contribution covers fundamentals and prerequisites on the way to a 5G QoS model for TSN, which has not yet been addressed in 3GPP nor IEEE.

An Efficient and Scalable Framework for Decentralized Finance Application Using Blockchain Interoperability

In recent years, blockchain technology has drawn a lot of attention, especially in the field of decentralised finance (De-Fi). However, scalability problems have come to light as a significant obstacle to the broad use of blockchain-based applications. To solve the issue of scalability, this paper has created a decentralised finance application with three main components: the addition of more liquidity to the swapping application, the implementation of a Polygon Proof of Stake bridge to enable efficient asset transfers, and the ability to transfer tokens between accounts seamlessly regardless of network agnosticism. The first feature, network agnostic capabilities for interoperability, facilitates token transfers between blockchain networks, allowing users to access and transact across them with ease The second component, the Polygon Proof-of-Stake bridge, makes asset transfers more efficient by taking advantage of the Polygon network's scalability advantages, which drastically lower transaction costs and processing times. Finally, adding more liquidity to the swapping programme makes it more scalable by guaranteeing that there is enough money for transactions, which prevents delays and bottlenecks. The scalability issue with blockchain technology is efficiently resolved by adding these three characteristics to the decentralised finance application, creating new opportunities for the mass acceptance and utilisation of blockchain-based financial services.

Investigation on Vision System: Digital FPGA Implementation in Case of Retina Rod Cells.

The development of prostheses and treatments for illnesses and recovery has recently been centered on hardware modeling for various delicate biological components, including the nervous system, brain, eyes, and heart. The retina, being the thinnest and deepest layer of the eye, is of particular interest. In this study, we employ the Nyquist-Based Approximation of Retina Rod Cell (NBAoRRC) approach, which has been adapted to utilize Look-Up Tables (LUTs) rather than original functions, to implement rod cells in the retina using cost-effective hardware. In modern mathematical models, numerous nonlinear functions are used to represent the activity of these cells. However, these nonlinear functions would require a substantial amount of hardware for direct implementation and may not meet the required speed constraints. The proposed method eliminates the need for multiplication functions and utilizes a fast, cost-effective rod cell device. Simulation results demonstrate the extent to which the proposed model aligns with the behavior of the primary rod cell model, particularly in terms of dynamic behavior. Based on the results of hardware implementation using the Field-Programmable Gate Arrays (FPGA) board Virtex-5, the proposed model is shown to be reliable, consume 30 percent less power than the primary model, and have reduced hardware resource requirements. Based on the results of hardware implementation using the reconfigurable FPGA board Virtex-5, the proposed model is reliable, uses 30% less power consumption than the primary model in the worth state of the set of approximation method, and has a reduced hardware resource requirement. In fact, using the proposed model, this reduction in the power consumption can be achieved. Finally, in this article, by using the LUT which is systematically sampled (Nyquist rate), we were able to remove all costly operators in terms of hardware (digital) realization and achieve very good results in the field of digital implementation in two scales of network and single neuron.

Integrated Host- and Content-Centric Routing for Efficient and Scalable Networking of UAV Swarm

Efficient and scalable networking is a key enabler of the Unmanned Aerial Vehicle (UAV) swarms, wherein multiple UAVs cooperatively execute complicated tasks. Despite the intermittent connections due to the UAV mobility, stable paths may exist temporally in periods like formation keeping, which is rarely considered or utilized in existing UAV routing designs. In this paper, we propose an integrated host- and content-centric routing (IHCR) mechanism to harness the advantages of both routing mechanisms. Specifically, the routing information of stable paths is reused in a host-centric manner to reduce the flooding for path exploring. In addition, the route failure detection and re-routing are content-centric to adjust to the topology dynamics. The challenges lie in the inherent contradiction between host-centric and content-centric routing mechanisms (e.g., naming spaces) and the tradeoff between path reusing and re-routing. To overcome these challenges, we appropriately incorporate node names into content names and then fully exploit reusable paths via time-based route failure detection and delayed forwarding. Packet-level simulation results show that IHCR increases the packet delivery ratio by 60.1%, and enlarges the achievable network scale by 4.2 times compared to state-of-the-art routing mechanisms.