Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs

In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.

Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic algorithms.This adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par with the competitive CH selection schemes under different number of data transmission rounds.The statistical analysis of the proposed HSWOA-DCP scheme also confirmed its energy stability with respect to ANOVA test.

Evolution and Governance of the Structure of Marine Economic Networks in China’s Coastal Areas: Based on Sea-related A-share Listed Companies

Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.

Changes of China’s Status in the Global System and Its Influencing Factors:A Multiple Contact Networks Perspective

Clarifying China’s position in the global system is an important logical basis for developing national diplomacy.Although much research has been done on China’s development status,most studies have been based on country comparisons or institutional en-vironment.In today’s networked era in which the global economy,trade,personnel,and information are closely connected,studies on China’s global position and its status changes and influencing factors in multiple contact networks are still insufficient.In this study,from the perspective of diverse global contact networks,we constructed economic,cultural,and political influence indices to explore the changes and influencing factors on China’s status in the global system from 2005 to 2018.The results show that during the study period,China’s global influence in the fields of economic ties,cultural exchanges,and political contacts increased significantly,but its influ-ence in the fields of cultural exchanges and political contacts lagged far economic ties.The pattern of China’s economic influence on various economies around the world has shown a transformation from an‘upright pyramid’to an‘inverted pyramid’structure.The proportion of these economies in low-influence zones has decreased from more than 60%in 2005 to less than 20%in 2018.China’s cultural and political influence on various economies around the world has increased significantly;however,for the former,the percentage of high-influence areas is still less than 20%,whereas for the latter the percentage of these economies in medium-and high-influence areas is still less than 50%.Analyses such as a scatter plot matrix show that geographical proximity,economic globalization,close cooperation with developing countries,and a proactive and peaceful foreign policy are important factors in improving China’s status in the diverse global network system.

Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Habits on Social Networks at Workplace: A Survey of Motivations and Behaviour

This article explores the use of social networks by workers in Abidjan, Côte d’Ivoire, with particular emphasis on a descriptive or quantitative analysis aimed at understanding motivations and methods of use. More than five hundred and fifty questionnaires were distributed, highlighting workers’ preferred digital channels and platforms. The results indicate that the majority use social media through their mobile phones, with WhatsApp being the most popular app, followed by Facebook and LinkedIn. The study reveals that workers use social media for entertainment purposes and to develop professional and social relationships, with 55% unable to live without social media at work for recreational activities. In addition, 35% spend on average 1 to 2 hours on social networks, mainly between 12 p.m. and 2 p.m. It also appears that 46% believe that social networks moderately improve their productivity. These findings can guide marketing strategies, training, technology development and government policies related to the use of social media in the workplace.

Lifetime Prolonging Algorithms for Underwater Sensor Networks

Underwater acoustic modem technology has attained a level of maturity to support underwater acoustic sensor networks （UASNs） which are generally formed by acoustically connected sensor nodes and a surface station providing a link to an on-shore control center. While many applications require long-term monitoring of the deployment area, the batterypowered network nodes limit the lifetime of UASNs. Therefore, designing a UASN that minimizes the power consumption while maximizing lifetime becomes a very difficult task. In this paper, a method is proposed to determine the optimum number of clnstens throngh combining an application-specific protocol architecture and underwater acoustic communication model so as to reduce the energy dissipation of UASNs. Deploying more sensor nodes which work alternately is another way to prolong the lifetime of UASNs, An algorithm is presented for selecting sensor nodes and putting them into operation in each round, ensuring the monitoring to the whole given area. The present results show that the algorithm can help prolong system lifetime remarkably when it is applied to other conventional approaches for sensor networks under the condition that the sensor node density is high.

Weak node protection to maximize the lifetime of wirelesssensor networks

Wireless sensor networks （WSN） provide an approachto collecting distributed monitoring data and transmiting them tothe sink node. This paper proposes a WSN-based multi-hop networkinfrastructure, to increase network lifetime by optimizing therouting strategy. First, a network model is established, an operatingcontrol strategy is devised, and energy consumption characteristicsare analyzed. Second, a fast route-planning algorithm isproposed to obtain the original path that takes into account the remainingenergy of communicating nodes and the amount of energyconsumed in data transmission. Next, considering the amount ofenergy consumed by an individual node and the entire network,a criterion function is established to describe node performanceand to evaluate data transmission ability. Finally, a route optimizingalgorithm is proposed to increase network lifetime by adjusting thetransmission route in protection of the weak node （the node withlow transmission ability）. Simulation and comparison experimentalresults demonstrate the good performance of the proposed algorithmsto increase network lifetime.

Data Aggregation Scheduling with Guaranteed Lifetime and Efficient Latency in Wireless Sensor Networks

In scenarios of real-time data collection of long-term deployed Wireless Sensor Networks （WSNs）, low-latency data collection with long net- work lifetime becomes a key issue. In this paper, we present a data aggregation scheduling with guaran- teed lifetime and efficient latency in WSNs. We first Construct a Guaranteed Lifetime Mininmm Ra- dius Data Aggregation Tree （GLMRDAT） which is conducive to reduce scheduling latency while pro- viding a guaranteed network lifetime, and then de-sign a Greedy Scheduling algorithM （GSM） based on finding the nmzximum independent set in conflict graph to schedule he transmission of nodes in the aggregation tree. Finally, simulations show that our proposed approach not only outperfonm the state-of-the-art solutions in terms of schedule latency, but also provides longer and guaranteed network lifetilre.

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems： routing problem at network layer and resource allocation problem at medium access control （MAC） layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.

A VMIMO-Based Cooperative Routing Algorithm for Maximizing Network Lifetime

Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm(VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.

Fuzzy Based Adaptive Clustering to Improve the Lifetime of Wireless Sensor Network

The objective of the recently proposed fuzzy based hierarchical routing protocol F-SCH is to improve the lifetime of a Wireless Sensor Network. Though the performance of F-SCH is better than LEACH, the randomness in CH selection inhibits it from attaining enhanced lifetime. CBCH ensures maximum network lifetime when CH is close to the centroid of the cluster. However, for a widely distributed network, CBCH results in small sized clusters increasing the inter cluster communication cost. Hence, with an objective to enhance the network lifetime, a fuzzy based two-level hierarchical routing protocol is proposed. The novelty of the proposal lies in identification of appropriate parameters used in Cluster Head and Super Cluster Head selection. Experiments for different network scenarios are performed through both simulation and hardware to validate the proposal. The performance of the network is evaluated in terms of Node Death. The proposal is compared with F-SCH and the results reveal the efficacy of the proposal in enhancing the lifetime of network.

The application of artificial neural networks to the inversion of the positron lifetime spectrum

A new method of processing positron annihilation lifetime spectra is proposed. It is based on an artificial neural network （ANN）-back propagation network （BPN）. By using data from simulated positron lifetime spectra which are generated by a simulation program and tested by other analysis programs, the BPN can be trained to extract lifetime and intensity from a positron annihilation lifetime spectrum as an input. In principle, the method has the potential to unfold an unknown number of lifetimes and their intensities from a measured spectrum. So far, only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes. The present study aims to design the network. Besides, the performance of this method requires both the accurate design of the BPN structure and a long training time. In addition, the performance of the method in practical applications is dependent on the quality of the simulation model. However, the chances of satisfying the above criteria appear to be high. When appropriately developed, a trained network could be a very efficient alternative to the existing methods, with a very short identification time. We have used the artificial neural network codes to analyze data such as the positron lifetime spectra for single crystal materials and monocrystalline silicon. Some meaningful results are obtained.

Design of maximizing homogeneous and heterogeneous clustered sensor network lifetime

Aimed at the problem of unbalanced energy existed in sensor networks, the clustered method is employed to enhance the efficient utilization of limited energy resources of the deployed sensor nodes. In this paper, we describe the network lifetime as a function of the communication and data aggregation energy consumption and analyze the lifetime of different transmission schemes in the homogeneous and heterogeneous sensor networks. The analysis carried out in this paper can provide the guidelines for network deployment and protocol design in the future applications.

Cost Minimization of Wireless Sensor Networks with Unlimited-lifetime Energy for Monitoring Oil Pipelines

Cyber-physical-system (CPS) has been widely used in both civil and military applications. Wireless sensor network (WSN) as the part and parcel of CPS faces energy problem because sensors are battery powered, which results in limited lifetime of the network. To address this energy problem, we take advantage of energy harvesting device (EHD) and study how to indefinitely prolong oil pipeline monitoring network lifetime by reasonable selecting EHD. Firstly, we propose a general strategy worst case-energy balance strategy (WC-EBS), which defines worst case energy consumption (WCEC) as the maximum energy sensor node could expend for oil pipeline monitoring WSN. When the energy collected by EHD is equal or greater than WCEC, network can have an unlimited lifetime. However, energy harvesting rate is proportional to the price of EHD, WC-EBS will cause high network cost. To reduce network cost, we present two optimization strategies, optimization workloadenergy balance strategy (OW-EBS ) and optimization first nodeenergy balance strategy (OF-EBS). The main idea of OW-EBS is to cut down WCEC by reducing critical node transmission workload; OF-EBS confirms critical node by optimizing each sensor node transmission range, then we get the optimal energy harvesting rate in OF-EBS. The experimental results demonstrate that OF-EBS can indefinitely extend network lifetime with lower cost than WC-EBS and OW-EBS, and energy harvesting rate P in each strategy satisfies POF-EBS ≤POW-EBS ≤PWC-EBS. © 2014 Chinese Association of Automation.

A Chain-Based Routing Protocol to Maximize the Lifetime of Wireless Sensor Networks

Energy conservation is a key issue in the design of systems based on wireless sensor networks. Clustering routing protocols have been developed in order to reduce the network traffic toward the sink and therefore prolong the network lifetime. An alternative of clustering is to build chains instead of clusters. In this context, we propose a routing protocol for Wireless Sensor Networks (WSN). It is based on constructing multiple chains in the direction of the sink. The first node of each chain sends data to the closest node in the same chain. This latter collects, aggregates and transmits data to the next closest node. This process repeats until reaching the last node, which aggregates and transmits data directly to the sink. An improvement of this approach is proposed. It works as follows: In addition to forming multiple chains as previously, it constructs a main chain, which includes leader node of each chain. Since, initially all main chain nodes have the same amount of power, the nearest node to the sink aggregates data from others then transmits it to the sink. In the next transmission, main chain node having the higher residual energy performs this task. Compared with the first approach, simulation results show that improvement approach consumes less energy and effectively extends the network lifetime.

Analysis of Lifetime of Large Wireless Sensor Networks Based on Multiple Battery Levels

Due to the limited transmission range, data sensed by each sensor has to be forwarded in a multi-hop fashion before being delivered to the sink. The sensors closer to the sink have to forward comparatively more messages than sensors at the periphery of the network,and will deplete their batteries earlier. Besides the loss of the sensing capabilities of the nodes close to the sink, a more serious consequence of the death of the first tier of sensor nodes is the loss of connectivity between the nodes at the periphery of the network and the sink;it makes the wireless networks expire. To alleviate this undesired effect and maximize the useful lifetime of the network, we investigate the energy consumption of different tiers and the effect of multiple battery levels, and demonstrate an attractively simple scheme to redistribute the total energy budget in multiple battery levels by data traffic load. We show by theoretical analysis, as well as simulation, that this substantially improves the network lifetime.

Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

In a wireless sensor network(WSN),data gathering is more effectually done with the clustering process.Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network.Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station.Moreover,existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes.Here,an improved clustering approach is anticipated to attain energy efficiency by implementingMapReduction for regulatingmapping and reducing complexity in routing mechanisms for eliminating redundancy and overlapping.In order to optimize the network performance,this work considers intelligent behaviors’to adapt with network changes and to introduce computational intelligence ability.In the proposed research,improved teaching learning based optimization is used to evaluate the coordinates of target nodes and nodes upgradation for determining energy consumption.Node upgradation is performed by integratingMap reduction to attain modification in Hop size of nodes.This variation reduces communication complexities.Therefore,network lifetime is increased,and redundancy is reduced.While comparingwith existing approaches here,sleep and wake-up nodes are considered for data transmission.The proposed algorithm clearly demonstrates 50%,16%&12%improvement in nodes lifetime,residual energy and throughput respectively compared to other models.Also it shows progressive improvement in reducing average waiting time,average queuing time and average energy utilization as 30%,20%and 46%respectively.Simulation has been done in NS-2 environment for distributed heterogeneous networks.

Extending the Network Lifetime of Wireless Sensor Networks Using Residual Energy Extraction—Hybrid Scheduling Algorithm

Wireless sensor networks (WSNs) are mostly deployed in a remote working environment, since sensor nodes are small in size, cost-efficient, low-power devices, and have limited battery power supply. Because of limited power source, energy consumption has been considered as the most critical factor when designing sensor network protocols. The network lifetime mainly depends on the battery lifetime of the node. The main concern is to increase the lifetime with respect to energy constraints. One way of doing this is by turning off redun-dant nodes to sleep mode to conserve energy while active nodes can provide essential k-coverage, which improves fault-tolerance. Hence, we use scheduling algorithms that turn off redundant nodes after providing the required coverage level k. The scheduling algorithms can be implemented in centralized or localized schemes, which have their own advantages and disadvantages. To exploit the advantages of both schemes, we employ both schemes on the network according to a threshold value. This threshold value is estimated on the performance of WSN based on network lifetime comparison using centralized and localized algorithms. To extend the network lifetime and to extract the useful energy from the network further, we go for compromise in the area covered by nodes.