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Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. [ABSTRACT FROM AUTHOR]

Hierarchical networks typically get rated to be not assortative on the basis of the degrees of the end vertices of the edges. However, degree centrality is not cognizant of the location of the nodes in the hierarchy. Rather, we propose the use of hierarchy-aware metrics (such as the k-core index of a node: the largest value of k for which the node would be part of a k-core and not part of any k′-core, where k′ > k and the number of k-core levels for which there exists a non-empty k-core for the network) for assortativity calculations. We first determine the assortativity index ( A s s I kcore ) of a network on the basis of the k-core indexes of the vertices. We then conduct principal component analysis (PCA) on a dataset of the A s s I kcore and the number of k-core levels for a suite of 77 real-world networks. We quantify the hierarchical k-core assortativity of a network as the weighted sum of the entries in the two principal components of this dataset. [ABSTRACT FROM AUTHOR]

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

Swift identification of a set of influential load nodes within power systems is an important task for effective modern power grid operation and security. This paper suggests a suitable approach of Node Strength (NS) or Bus Strength (BS) scheme for effective and swift identification of sets of influential load nodes within power systems. This effective algorithm relies on the Network Structural Characteristics Theory (NSCT) of power systems. The NS scheme uses circuit theory laws to present the linear relationship among the network structure, the voltage and current in the network. The traditional Complex Network Theory (CNT) or Centrality-Based (Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC), PageRank Centrality (PRC) and Eigenvector Centrality (EC)) methods for identifying influential load nodes within power systems are also presented in the paper. Thereafter, the numerical values of the NS and each of the CNT-based methods associated with the load nodes, are determined. The relative importance of each load node is then ascertained through ranking (in the order of magnitude or priority) of the values obtained for NS and each of the CNT-based methods. Based on the order of priority, the sets of influential load nodes in the network are identified. Furthermore, performance analyses, using statistical correlation coefficient as well as structural risk assessment, are used to establish the best method suitable for quick identification of a set of influential load nodes in the system. The results obtained are corroborated with the network robustness assessment to further validate the proposed approach. The study uses IEEE 5-bus, IEEE 14-bus and Nigerian 28-bus networks as case studies. The comparison of results obtained show that the NS or BS scheme suggested is more suitable for quick identification of sets of influential load nodes in a relatively large-sized power network. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle Ad hoc network (UANET), increasing the network size will increase the average number of hops between nodes, making network management and routing protocol operation more difficult. Clustering structure can be used to optimize network management and improve network scalability. This paper proposed a fast and stable weighted clustering algorithm to address the instability and poor robustness of cluster structures caused by the high mobility of drones. Compared with existing weighted clustering algorithms, this algorithm improved the selection of four indicators: link retention rate, node degree and relative speed. It proposed an efficient cluster maintenance mechanism to address the cluster oscillation caused by cluster head node disconnection in battlefield and emergency scenarios. Finally, it verified the performance of this algorithm through simulation. The results show that compared with existing improved weighted clustering algorithms, this algorithm can effectively reduce clustering time, and quickly recover in the event of cluster head nodes dropping, making it more suitable for network deployment in complex environments. [ABSTRACT FROM AUTHOR]

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay. [ABSTRACT FROM AUTHOR]

Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology.

The traditional Graph Embedding algorithm and graph neural network model only use the attribute information or feature information of the nodes themselves when classifying the nodes in the network, but rarely use the structural information of the nodes in the network. It is also a problem worthy of further study to introduce the node network structure information to improve the classification accuracy when aggregating the graph neural network. Therefore, based on the GraphSage model, a new aggregation function was designed and a new GraphSage-Degree model was proposed on the node degree and node importance in the network. Firstly, the model obtained the importance of the nodes in the neighbourhood based on the node degree, then aggregated the features of the nodes relied on the importance, so that the important nodes in the network can aggregate as much feature information as possible, and set a hyperparameter D related to the node degree in the GraphSage-Degree, which can be adjusted to achieve the best classification state on different data sets.The GraphSage-Degree was tested on three publicly available datasets, Cora, Citeseer and Pubmed. The average improvement values of macro-F1 compared respectively with other methods are 8.72%, 10.37% and 8.29%, with the maximum improvement value of 38.84% on Pubmed. The average improvement values of micro-F1 are 8.97%, 11.16%, and 6.9%, respectively, with a maximum boost value of 38.39% on Pubmed. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs), are characterized by their difficulty in establishing end-to-end paths and and large message propagation delays. To control network overhead costs, reduce message delays, and improve delivery rates in DTNs, it is essential to not only delete messages that have reached their destination but also to more precisely determine appropriate relay nodes. Based on the above goals, this paper constructs a multi-copy relay node selection router algorithm based on Q-lambda reinforcement learning with reference to the idea of community division (QLCR). In community division, if a node has the highestdegree, it is considered the core node, and nodes with similar interests and structural properties are divided into a community. Node degree refers to the number of nodes associated with the node, indicating its importance in the network. Structural similarity determines the distance between nodes. The selection of relay nodes considers node degree, interests, and structural similarity. The Q-lambda reinforcement learning algorithm enables each node to learn from the entire network, setting corresponding reward values based on encountered nodes meeting the specified conditions. Through iterative processes, the node with the most cumulative reward value is chosen as the final relay node. Experimental results demonstrate that the proposed algorithm achieves a high delivery rate while maintaining low network overhead and delay.

Network reliability reflects a system's ability to perform specified functions under specified topological and traffic conditions. Network reliability is the weighted sum of connection reliability and travel time reliability. Based on complex network theory, a new method was proposed to calculate the travel time reliability of road networks. The topology model of a regional highway network in China was built using the dual method. After a random attack or deliberate attack, node sizes in the sub-network can be used to reflect the node importance for network connection reliability. Some conclusions were drawn after the change in travel time coefficient and delay coefficient. The increases in the two coefficients will accelerate the decrease of travel time reliability of the highway network. After a comparison among three methods of travel time reliability, including variation coefficient, the misery indexes, and the new equation, the new method was further verified. The influence factors of highway network reliability were analyzed under the condition of different highway blockage and congestion. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is an auto‐configured and baseless wireless network system to detect the physical scenarios or atmospheric situations like temperature, sound, pressure, pollutants, and so on. These investigations can be aided for the smooth transmission of data along with the networks. The current smart world exploits WSN in most applications, such as technical applications, industrial applications, low‐power embedded systems, and other smart communications. Even though it has so many applications, the available limited supply resource causes ill effects while transmitting large amounts of data. Hence, we can deem energy efficiency as a crucial factor that affects the performances of WSN. Processes such as routing, and for long‐lasting life span WSN requires more energy. Hence, to have an effective routine and enhanced life span of WSN, we propose a novel energy‐efficient routing protocol based on a hybrid improved whale artificial ecosystem optimization algorithm. To attain an energy‐efficient network it is ineluctable to choose an optimized cluster head and other nodes. These can be performed by evaluating various fitness functions of WSN. Henceforth, the nodes are organized and better routing can be obtained based on the fitness function values. This also improves the energy efficiency of the WSN and has been utilized to select the other forwarding nodes. Experimental analyses are conducted based on the deemed three scenarios which are based on the positioning of the base station (BS) and compared with other prior works. In all aspects, our proposed work has better performances than the other state‐of‐art works. [ABSTRACT FROM AUTHOR]

Summary: Mobile ad hoc network (MANET) is an infrastructure‐less network. Every Node act as a sender as well as receiver while sending a packet. Neighbor coverage probabilistic rebroadcasting (NCPR) protocol plays a momentous role in MANET for determining a route by helping the transitional nodes to onward a packet when the mobile nodes are not in the transmission space. This protocol minimizes the interference among node and increases the network capacity. There occurs a delay in the network while sending a packet via failure route or channel. Before sending a packet in the network, all the neighbor nodes have to be verified to avert congestion in the network. Quality of service (QoS) plays an important role in the development of MANET. In this research work, delay and neighbor connectivity‐based constrain is focused. The impact of node mobility during interference and delay is examined using network coding‐based topology control algorithm. By using this algorithm, irrelevant links are identified and detached from the topology. The simulation result shows that neighbor coverage established topology control algorithm for delay (NCTCAD) can reduce the delay and upturn the performance in MANETs. [ABSTRACT FROM AUTHOR]

Recently time series prediction based on network analysis has become a hot research topic. However, how to more accurately forecast time series with good efficiency is still an open question. To address this issue, we propose an efficient time series forecasting method based on the DC algorithm and visibility relations on the vertexes set. Firstly, the time series is mapped into the network by the DC algorithm, which is a more efficient approach to generate the visibility graph. Then, we use the variation trends (slope) of those nodes that have visibility relation with the last node to get the preliminary predictive values. Afterward, the value of the last node is adopted to obtain the revised predictive values, which are assigned different weights according to node degree and time distance to get the final weighted result. To better demonstrate the prediction performance and applicability of the proposed method, the proposed method is applied to different time series data sets. The empirical results show that the proposed method could provide a higher level of forecasting accuracy than many methods with relatively lower time complexity.

For the analysis of urban networks indicators, like average node degree, connectivity and betweenness centrality, are widely used. Their values are calculated for a catchment area of the network. This research investigates if and to what extent the size of the catchment area influences the indicator's values. A methodology for determining the size of catchment area is proposed and recommendations for its appropriate size for pedestrian network analysis are offered. For the mathematical deduction of the relationship between the size of the catchment area and the indicator values an idealized regular network is used as a reference model. We found that the size effect on indicator values is prominent. It decreases exponentially as the size of the catchment area increases. The size effect is notable until a side length of the catchment area that equals 50 times the average street length, while the variation is still acceptable if it is 30 to 40 times the average street length. The typical average street length in cities is about 100 meters, a catchment area larger than 4000 x 4000m² is not necessary. This reference model can also be used to evaluate real networks and to compare them with an idealized case. [ABSTRACT FROM AUTHOR]

Content-centric networking (CCN) becomes an imminent Internet architecture that emphasizes the data-centric approach for information retrieval instead of searching for the hosts in the network. CCN offers data (content) caching and its distribution capabilities to reduce average latency, the load of data servers, and improve network bandwidth utilization during content delivery. In this direction, the performance of node degree centrality and distance-based autonomous caching strategies have been investigated. Then, a novel content caching strategy called content placement based on normalized node degree and distance (CPNDD) is proposed. The CPNDD scheme considers content provider distance and node degree centrality parameters together to select the optimal cache locations for comprehensive utilization of the available caching capacities and a further reduction in average latency during content retrieval. The weights for these caching parameters have been determined via widespread simulations on the Abilene networks. The proposed caching scheme is implemented and tested in the simulated environment of ndnSIM and compared with peer competing schemes in CCN. The execution outcomes are examined for different caching capacities (50 and 100), Zipf popularity skewness factors (0.7 and 1.0) and request rates (50/s and 100/s). The simulation executions illustrate that CPNDD caching strategy escalates the cache hit probability and hop-reduction ratio up-to 8 % and 9 % as compared to existing schemes. Hence, the proposed content placement scheme improves the performance of CCN networks considerably and makes it suitable for the applications of Industry 4.0. [ABSTRACT FROM AUTHOR]

Survivable virtual optical network (VON) mapping is researched to overcome the ossification of current network architectures. In this paper, we study the survivable VON mapping problem in elastic optical network (EON) with the objective of minimizing the average resource consumption of VON request. We propose a protective algorithm, namely, coordinated mapping algorithm based on minimum spanning tree (CMST), to provide the dedicated protection against the single physical link failure. In CMST, for virtual node mapping, we not only consider the resources constraint but also the node degree constraint. For virtual link mapping, we adopt the coordinated mapping method for part of the virtual nodes and virtual links. And we provide the backup path for the minimum spanning tree link of virtual topology to reduce the resource consumption. Simulation results showed that CMST can reduce the VON request blocking probability and average network resource consumption. And it can increase the revenue of physical network. [ABSTRACT FROM AUTHOR]

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy. [ABSTRACT FROM AUTHOR]

It is crucial to identify the important nodes in the network. On the basis of the K-shell algorithm, this study researched the recognition of important nodes in complex networks. The study introduces concepts of edge weight and influence coefficient, the design of an IKS algorithm, and an analysis of its recognition effect in Zachary network and real micro blog network. The results showed that the partition results of the K-shell algorithm were coarse, while the partition results of the IKS algorithm were refined; the IKS algorithm could sort the important nodes accurately on the basis of the K-shell algorithm, and its rationality was higher than that of the closeness centralization and PaperRank algorithm. The partition results in the microblog network also verified the effectiveness of the improved method. The experimental results show that the IKS algorithm is reliable in the important node identification, which is beneficial to the recognition of important nodes in complex network. [ABSTRACT FROM AUTHOR]

Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t-test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees ( p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/)., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t -test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees (p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD. [ABSTRACT FROM AUTHOR]

In FANET, the high-speed movement of nodes will result in frequent updates of the network topology, which makes network management more complicated. Clustering can increase network capacity and reuse spatial resources, which is an effective way to optimize network management. In the large-scale and high-speed mobile environment .this paper proposed a multiparameter weighted clustering algorithm. This algorithm introduced the clustering index in the maximum speed similarity algorithm into the weighted clustering algorithm. and improved the link retention, node degree difference and node residual energy to select the network node with largest weight as the cluster head, by considering these four parameters synthetically. The simulation results show that the clustering algorithm can reduce the number of clusters and the switching rate between clusters, which improves the stability of clustering, and can extend the minimum survival time of nodes, which improves the overall endurance of the network. [ABSTRACT FROM AUTHOR]

People like to communicate with others who have similar opinions in daily life. "Open" individuals are willing to accept peers' opinions, whereas "conservative" individuals are more closed to others' opinions. Based on this reality, an opinion dynamic model is proposed with a time-varying trust threshold related to the node degree, which refers to the number of individuals that can be communicated directly. Similar opinions within the trust thresholds at the beginning will eventually form a consensus opinion. Individuals who have common neighbors converge to consensus faster than ones who have no common neighbors. The network size impacts the convergent speed of the opinion cluster and the trust threshold cluster. Individuals in small groups are more stressed than in large ones when they try to insist their opinions. A group with half of the individuals categorized as doubting and the other half categorized as trusting exhibits consistent confidence but experiences difficulty in reaching a consensus: "harmony but difference" may be a criterion for unifying a group. [ABSTRACT FROM AUTHOR]

Recently fuzzy based hierarchical routing protocols such as F-MCHEL and F-SCH were proposed to improve the lifetime of a wireless sensor network. In certain cases F-MCHEL results in several small sized clusters increasing the inter cluster communication cost. This is due to the negligence of node degree in cluster head selection. The performance of F-SCH is observed to be better than F-MCHEL because super cluster head selection is based on centrality and mobility. Though it minimized inter cluster energy, the randomness in LEACH based cluster head selection inhibits the benefits of multilevel routing. Hence, with an objective to minimize the total energy consumed by the network, fuzzy based two-level hierarchical protocol is proposed. The objective is achieved by appropriate selection of cluster head and super cluster head. Cluster head selection is based on battery power, centrality and node degree while super cluster head selection is based on battery power, centrality and mobility of cluster heads. Experiments are carried out both in simulation and hardware to validate the proposal. The performance of the network in terms of first node, half node and last node death is studied and the proposal is compared with F-MCHEL and F-SCH. The results reveal the efficiency of the proposal. [ABSTRACT FROM AUTHOR]