Stability Of Networks Research Articles

Enhancing Reliability and Stability of BLE Mesh Networks: A Multipath Optimized AODV Approach.

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments.

Evolutionarily conserved core microbiota as an extended trait in nitrogen acquisition strategy of herbaceous species.

Microbiota have co-evolved with plants over millions of years and are intimately linked to plants, ranging from symbiosis to pathogenesis. However, our understanding of the existence of a shared core microbiota across phylogenetically diverse plants remains limited. A common garden field experiment was conducted to investigate the rhizosphere microbial communities of phylogenetically contrasting herbaceous families. Through a combination of metagenomic sequencing, analysis of plant economic traits, and soil biochemical properties, we aimed to elucidate the eco-evolutionary role of the core rhizosphere microbiota in light of plant economic strategies. We identified a conserved core microbiota consisting of 278 taxa that was closely associated with the phylogeny of the plants studied. This core microbiota actively participated in multiple nitrogen metabolic processes and showed a strong correlation with the functional potential of rhizosphere nitrogen cycling, thereby serving as an extended trait in the plant nitrogen acquisition. Furthermore, our examination of simulated species loss revealed the crucial role of the core microbiota in maintaining the rhizosphere community's network stability. Our study highlighted that the core microbiota, which exhibited a phylogenetically conserved association with plants, potentially represented an extension of the plant phenotype and played an important role in nitrogen acquisition. These findings held implications for the utilization of microbiota-mediated plant functions.

Depression and Anxiety among Migrant Older Adults during the COVID-19 Pandemic in China: Network Analysis of Continuous Cross-Sectional Data.

Many Chinese migrant older adults are more prone to mental health problems due to their "migrant" status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included 'Afraid something will happen' (A2), 'Irritability' (A6), 'Panic' (A7), 'Feeling of worthlessness' (D6), 'Anhedonia' (D1), and 'Feeling of fear' (A5). The major bridge symptoms included 'Feeling of fear' (A5), 'Panic' (A7), 'Irritability' (A6), 'Fatigue' (D4), 'Anhedonia' (D1), and 'Depressed or sad mood' (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

Measured harmonics modelling and study of their impact on the transient stability of electrical networks using MATLAB software platform

Measured harmonics modelling and study of their impact on the transient stability of electrical networks using MATLAB software platform

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Turing patterns for explaining spatial distribution in nature mostly focus on continuous media and existing networks, but only few attempts exist to study them on systems with high-order interactions. Considering that high-order interactions have a particularly significant impact on rumor propagation, this study establishes a generalized reaction-diffusion rumor propagation model based on a multiplex network, where simplicial complexes are employed to describe high-order structures. It aims to provide the spatial distribution patterns of the population participating in rumor propagation and identify the structural factors that affect such patterns. We theoretically provide the necessary conditions for Turing instability in single-layer and multiplex networks by considering high-order interactions. In the numerical simulation, we demonstrate that the Turing pattern could be controlled by adjusting the diffusion coefficient, high-order structure intensity, and average degree of the network. The results indicate that: (i) in a single-layer network, the Turing pattern only exists when high-order interactions appear, and the difference in diffusion rate plays a decisive role, (ii) in a multiplex network, the Turing pattern can still be observed under the same diffusion rates, which are affected by the difference in higher-order intensity between the two layers, and (iii) in existing networks, the average degree of the network has an important impact on Turing pattern. These findings contribute toward comprehending the impact of network structure on pattern formation, particularly the high-order interactions on the Turing pattern.

A Neural Network Approach for Solving Weighted Nonlinear Complementarity Problems

In this paper, we consider the weighted nonlinear complementarity problem which is the extension of the general complementarity problems and contains a wide class of optimization problems. Based on the weighted complementarity function, we reformulate the weighted nonlinear complementarity problem as an unconstrained minimization problem and present the steepest descent-based neural network to solve it. Under mild conditions, we derive some results related to the asymptotic stability of the neural network. Numerical experiments indicate that the proposed algorithm is quite effective.

RDSC: Range-Based Device Spatial Clustering for IoT Networks.

The growth of the Internet of Things (IoT) has become a crucial area of modern research. While the increasing number of IoT devices has driven significant advancements, it has also introduced several challenges, such as data storage, data privacy, communication protocols, complex network topologies, and IoT device management. In essence, the management of IoT devices is becoming more and more challenging, especially with the limited capacity and power of the IoT devices. The devices, having limited capacities, cannot store the information of the entire environment at once. In addition, device power consumption can affect network performance and stability. The devices' sensing areas with device grouping and management can simplify further networking tasks and improve response quality with data aggregation and correction techniques. In fact, most research papers are looking forward to expanding network lifetimes by relying on devices with high power capabilities. This paper proposes a device spatial clustering technique that covers crucial challenges in IoT. Our approach groups the dispersed devices to create clusters of connected devices while considering their coverage, their storage capacities, and their power. A new clustering protocol alongside a new clustering algorithm is introduced, resolving the aforementioned challenges. Moreover, a technique for non-sensed area extraction is presented. The efficiency of the proposed approach has been evaluated with extensive experiments that gave notable results. Our technique was also compared with other clustering algorithms, showing the different results of these algorithms.

The Contagion of Debt Default Risk in Energy Enterprises Considering Carbon Price Fluctuations

Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger.

Adaptive decision-making with deep Q-network for heterogeneous unmanned aerial vehicle swarms in dynamic environments

With the continuous advancement of Unmanned Aerial Vehicle (UAV) swarm technology, the field of adaptive decision-making for heterogeneous UAV swarms across a spectrum of tasks is rapidly emerging as a focal point of research. This domain holds significant importance for enhancing the operational efficiency and responsiveness of UAV swarms in complex and dynamic environments. This paper addresses this challenge by introducing an innovative adaptive task decision-making method, which leverages Deep Q-Network (DQN) algorithms, specifically tailored to meet the unique requirements of heterogeneous UAV swarms. We initially construct a mathematical model that transforms the intricate swarm decision-making issues into a standard Markov Decision Process (MDP), and meticulously design the state space, action space, reward function, and state transition function to cater to the decision-making needs of UAV swarms. Utilizing this model, we further integrate a hybrid experience replay mechanism, coupled with DQN algorithms for offline training, enabling the efficient transformation of training outcomes into a network model that is directly applicable to real-time online decision-making. This approach markedly enhances the decision-making adaptability and dynamic adaptability of UAV swarms when confronted with a variety of tasks. During the offline training phase, we also employ a hybrid experience replay mechanism and target network approximation techniques to bolster the network's stability and training efficiency, ensuring high reliability in online decision-making. Comparative simulations with existing DQN algorithms reveal the distinct superiority of our method in addressing adaptive task decision-making challenges. Moreover, through 1000 comprehensive simulation experiments, we further validate the stability and effectiveness of our method across diverse task environments, providing a solid theoretical foundation and technical support for the practical application and proliferation of UAV swarm technology.

Spatial correlation network and influencing factors analysis of supply efficiency of basic public service (SEBPS) in rust belt regions of China: An empirical study from Northeast China

Equalizing basic public service is crucial for achieving social fairness and justice. Promoting the improvement of public service can be achieved through the improvement of supply efficiency and cross-regional coordinated development. We employed the Super-SBM model, modified gravity model, and social network analysis method to empirically study the spatial correlation network characteristics of supply efficiency of basic public service (SEBPS) based on the data of 34 prefecture-level cities in the three provinces of Northeastern China from 2011 to 2020. The results revealed the overall efficiency of SEBPS in the study area is relatively low, and the supply efficiency is polarized. There is significant non-equilibrium between regions, and the regional difference moment gradually expands over time. There exists a strong spatial correlation between SEBPS in each city, and the overall correlation effect is weak but the network stability has improved. Each city had different roles and statuses in the spatial correlation network, creating a hierarchical structure. The level of geospatial proximity had a positive impact on the spatial correlation and spillover of the network. The level of economic development and infrastructure configuration hurt the network, and there is obvious heterogeneity between cities. The differences in industrial structure, opening up level, urbanization level, population density, the level of fiscal decentralization and technological innovation and personal income level among cities affected the SEBPS network. This study not only provides a new network research perspective and theoretical foundation for public service policies in China's rust belt region but also serves as a reference for urban development in similar regions.

Tai Chi Practice Buffers Aging Effects in Functional Brain Connectivity.

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann-Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory-motor (i.e., SMN/VIN) networks.

Disruption of bacterial interactions and community assembly in Babesia-infected Haemaphysalis longicornis following antibiotic treatment

BackgroundA previous study highlighted the role of antibiotic-induced dysbiosis in the tick microbiota, facilitating the transstadial transmission of Babesia microti from nymph to adult in Haemaphysalis longicornis. This study builds on previous findings by analyzing sequence data from an earlier study to investigate bacterial interactions that could be linked to enhanced transstadial transmission of Babesia in ticks. The study employed antibiotic-treated (AT) and control-treated (CT) Haemaphysalis longicornis ticks to investigate shifts in microbial community assembly. Network analysis techniques were utilized to assess bacterial interactions, comparing network centrality measures between AT and CT groups, alongside studying network robustness and connectivity loss. Additionally, functional profiling was conducted to evaluate metabolic diversity in response to antibiotic treatment.ResultsThe analysis revealed notable changes in microbial community assembly in response to antibiotic treatment. Antibiotic-treated (AT) ticks displayed a greater number of connected nodes but fewer correlations compared to control-treated (CT) ticks, indicating a less interactive yet more connected microbial community. Network centrality measures such as degree, betweenness, closeness, and eigenvector centrality, differed significantly between AT and CT groups, suggesting alterations in local network dynamics due to antibiotic intervention. Coxiella and Acinetobacter exhibited disrupted connectivity and roles, with the former showing reduced interactions in AT group and the latter displaying a loss of connected nodes, emphasizing their crucial roles in microbial network stability. Robustness tests against node removal showed decreased stability in AT networks, particularly under directed attacks, confirming a susceptibility of the microbial community to disturbances. Functional profile analysis further indicated a higher diversity and richness in metabolic capabilities in the AT group, reflecting potential shifts in microbial metabolism as a consequence of antimicrobial treatment.ConclusionsOur findings support that bacterial interaction traits boosting the transstadial transmission of Babesia could be associated with reduced colonization resistance. The disrupted microbial interactions and decreased network robustness in AT ticks suggest critical vulnerabilities that could be targeted for managing tick-borne diseases.

Time-optimal open-loop set stabilization of Boolean control networks

We show that for stabilization of Boolean control networks (BCNs) with unobservable initial states, open-loop control and close-loop control are not equivalent. An example is given to illustrate the nonequivalence. Enlightened by the nonequivalence, we explore open-loop set stabilization of BCNs with unobservable initial states. More specifically, this issue is to investigate that for a given BCN, whether there exists a unified free control sequence that is effective for all initial states of the system to stabilize the system states to a given set. The criteria for open-loop set stabilization is derived and for any open-loop set stabilizable BCN, every time-optimal open-loop set stabilizer is proposed. Besides, we obtain the least upper bounds of two integers, which are respectively related to the global stabilization and partial stabilization of BCNs in the results of two literature articles. Using the methods in the two literature articles, the least upper bounds of the two integers cannot be obtained.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

Mechanotransduction alterations in tissue-engineered tumor models for new drug interventions

Mechanotransduction is a collection of pathways in which the cells reprogram themselves by sensing mechanical stimuli. Cells use biological cues to interpret the physiological stresses and respond to changing conditions by modifying the cellular and ECM architecture. This feedback loop regulates a variety of cellular processes, including migration, growth, differentiation, and death, which is essential for the network stability to work together in a coordinated manner. The effect of stress on cancer progression and the role of mechanics as a critical inducer in determining the cancer cell fate has been studied. This review discusses the progression of cancer cells to epithelial to mesenchymal transitions. It examines tumor microenvironment models, such as spheroids, bio-printing, and microfluidics, and how they recapitulate the tumor microenvironment. These offer certain benefits and help replicate the fundamental behavior in vivo conditions. We further discuss mechanosensing, the associated signaling molecules, and how it modulates the cancer drug resistance and transduction pathways that implicate cancer treatment. The difficulties with the existing methods and the prospects for additional study that may be applied in this area are discussed, and how they allow for new therapeutic development.

A study on the spatial correlation network structure and its influencing factors of coupling coordination between FDI flow network and carbon transfer network in the belt and road initiative countries

Clarifying the spatial correlation characteristics and influencing factors of coupling coordination between Foreign Direct Investment (FDI) flow networks and carbon transfer networks in countries along the Belt and Road Initiative is of utmost importance for the formulation of regional carbon governance strategies and the establishment of a high-quality Green Silk Road. This study used a comprehensive approach combining social network analysis and coupling coordination model to measure the coupling coordination degree of FDI flow networks and carbon transfer networks of 67 Belt and Road countries from 2010 to 2016. In addition, a modified gravity model is used to characterize the spatial correlation network structure of coupling coordination between the two networks, and the QAP regression analysis method is applied to investigate the factors influencing the spatial association network. The results are as follows: 1) The spatial correlation network of coupling coordination between the two networks has good accessibility and relatively high overall network stability. 2) Countries such as Qatar and the United Arab Emirates occupy central positions in the network, while Bahrain and Jordan are positioned on the periphery of the network. 3) The spatial correlation network can be divided into three sectors: net outflow, net inflow, and bidirectional overflow sectors. 4) Spatial adjacency, bilateral investment treaties, economic development, and institutional quality have significant positive effects on the spatial association network, while the industrial structure and the level of infrastructure development have a significant negative impact. This study proposes an indicator system for the coupling coordination between FDI flow networks and carbon transfer networks. The aim is to investigate the coupling coordination relationship between FDI flow networks and carbon emission transfer networks in countries along the Belt and Road Initiative, providing important guidance for the formulation of regional cooperative carbon emission reduction strategies in other regions.

Research on hydrogen distribution characteristics in town hydrogen-doped methane pipeline

The study of hydrogen concentration distribution law of hydrogen-doped methane pipeline is directly related to the safety and stability of hydrogen-doped methane pipeline network. Based on the theory of fluid dynamics, this paper established a model of hydrogen-doped methane pipeline and simulated the operation and shutdown status of hydrogen-doped methane pipeline by adopting the computational fluid dynamics method and selecting the mixture multiphase model and standard k - ε turbulence model. This paper investigates the hydrogen concentration distribution law in hydrogen-doped methane pipelines as well as the influence law of different hydrogen-doping ratios, operating flow velocities, operating pressures, shutdown time and gas usage on the hydrogen concentration distribution in gas pipeline. The results show that: under the operation condition, there is a weak uneven distribution of hydrogen in the pipeline, the hydrogen-doping ratio, flow velocity, pressure on the hydrogen volume fraction of the change in the 0.9% or less, the effect can be ignored; in the shutdown status, there is a clear stratification phenomenon, the hydrogen-doping ratio increased from 10 to 25%, the change in the volume fraction of hydrogen in the 11.2% or less, a positive correlation; with the extension of the shutdown time to 900s, the pipeline firstly appeared obvious stratification phenomenon in the branch pipe, the thickness of the gas with hydrogen volume fraction above 40% on the upper wall surface of the branch pipe increased to 0.7 mm, and after the shutdown time was extended to 10 h, obvious stratification phenomenon appeared in the main pipeline, and the volume fraction of hydrogen near the top of the main pipe of about 16.5 mm was above 30%, which was positively correlated; In the shutdown status, the shutdown time has the greatest effect on the stratification phenomenon in the pipe, followed by the hydrogen-doping ratio, and the gas usage has the least effect.

Environmental DNA metabarcoding reveals the influence of environmental heterogeneity on microeukaryotic plankton in the offshore waters of East China Sea

Microeukaryotic plankton are essential to marine food webs and biogeochemical cycles, with coastal seas playing a critical role in aquatic ecosystems. Understanding the diversity of microeukaryotic plankton, deciphering their community structure and succession patterns, and identifying the key factors influencing these dynamics remain central challenges in coastal ecology. In this study, we examine patterns of biodiversity, community structure, and co-occurrence using environmental DNA (eDNA)-based methods. Our results show a linear correlation between α-diversity and distance from the shore, with nutrient-related factors, especially inorganic nitrogen, being the primary determinants of the spatial distribution of plankton communities. Alternation of coastal habitat have shifted the succession patterns of coastal eukaryotic plankton communities from stochastic to deterministic processes. Additionally, our observations indicate that the topology and structure of eukaryotic plankton symbiotic patterns and networks are significantly influenced by environmental heterogeneity such as nutrients, which increase the vulnerability and decrease the stability of offshore ecological networks. Overall, our study demonstrates that the distribution of microeukaryotic plankton communities is influenced by factors related to environmental heterogeneity.

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Blockchain-Machine Learning Fusion for Enhanced Malicious Node Detection in Wireless Sensor Networks

In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.