Collaborative Charging Scheduling in Wireless Charging Sensor Networks

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.TwoMCs can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

How does network intermediary affect collaborative innovation?Evidence from Chinese listed companies

Purpose:This study aims to explore how network intermediaries influence collaborative innovation performance within inter-organizational technological collaboration networks.Design/methodology/approach:This study employs a mixed-method approach,combining quantitative social network analysis with regression techniques to investigate the role of network intermediaries in collaborative innovation performance.Using a patent dataset of Chinese industrial enterprises,the research constructs the collaboration networks and analyzes their structural positions,particularly focusing on their role as intermediaries,characterized by betweenness centrality.Negative binomial regression analysis is employed to assess how these network characteristics shape innovation outcomes.Findings:The study reveals that firms in intermediary positions enhance collaborative innovation performance,but this effect is nuanced.A key finding is that network clustering negatively moderates the intermediary-innovation relationship.Highly clustered networks,while fostering local collaboration,may limit the innovation potential of intermediaries.On the other hand,relationship strength,measured by collaboration intensity and trust among firms,positively moderates the intermediary-innovation link.Research limitations:This study has several limitations that present opportunities for further research.The reliance on quantitative social network analysis may overlook the complexity of intermediaries’roles,and future studies could benefit from incorporating qualitative methods to better understand cultural and institutional factors.Additionally,cross-country comparisons are needed to assess the consistency of these dynamics in different contexts.Practical implications:The study offers practical insights for firms and policymakers.Organizations should strategically position themselves as network intermediaries to access diverse information and resources,thereby improving innovation performance.Building strong trust helps using network intermediary advantages.For firms in highly clustered networks,it is important to seek external partners to avoid limiting their exposure to new ideas and technologies.This research emphasizes the need to balance network diversity with relationship strength for sustained innovation.Originality/value:This research contributes to the literature by offering new insights into the role of network intermediaries,presenting a comprehensive framework for understanding the interaction between network dynamics and firm innovation.

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

COLLABORATIVE TRACKING VIA PARTICLE FILTER IN WIRELESS SENSOR NETWORKS

Target tracking is one of the main applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of the sensor nodes. A framework and analysis for collaborative tracking via particle filter are presented in this paper. Collaborative tracking is implemented through sensor selection, and results of tracking are propagated among sensor nodes. In order to save communication resources, a new Gaussian sum particle filter, called Gaussian sum quasi particle filter, to perform the target tracking is presented, in which only mean and covariance of mixands need to be communicated. Based on the Gaussian sum quasi particle filter, a sensor selection criterion is proposed, which is computationally much simpler than other sensor selection criterions. Simulation results show that the proposed method works well for target tracking.

Social Networks and Citizen Participation in the Collaborative Community Policing ——A Case Study of S Community in Beijing

It is of great significance to enhance collaborative community policing for crime prevention and better community-police relationships. Understanding the relational structure of collaborative community policing is necessary to pinpoint the pattern of interactions among key actors involved in community policing and improve the effectiveness of network governance. Based on 234 surveys of citizens of S Community in Beijing from April 2017 to May 2017, this paper empirically examines the characteristics of formal network and informal network of citizen participation in the collaborative community policing. Beijing is widely known for its active involvement of neighborhood volunteers in different types of community policing. We focused on four different types of interpersonal work relationships in this study： workflow, problem solving, mentoring and friendship, among resident committees, neighborhood administrative offices, media, police station, business security personnel, neighborhood volunteers, and security activists. The nature of relationships between individuals in networks can be treated as from instrumental ties to expressive ties. Expressive ties cover relationships that involve the exchange of friendship, trust, and socio-emotional support. We extended this intra-organizational insight into a community policing inter-organizational context. The collaborative network showed the trend of the distributed network. The clustering analysis showed that in the workflow network, we should make thll use of the close interaction between the citizens and activists in the community. Meanwhile, in the problem-solving network, mentoring network and friendship network, interactions between citizens and neighborhood committee are weak.

An Event-Triggered Energy-Efficient Clustering in Collaborative Beamforming for Wireless Sensor Networks

In this paper, we present a protocol, CEWEC （Collaborative, Event-Triggered, Weighted, Energy-Efficient Clustering） , based on collaborative beamfor^ning. It is designed for wireless sensor nodes to realize the long-distance transmission. In order to save the energy of sensor nodes, a node ＂wakes up ＂when it has data to be uploaded. In our protocol, multi-layer structure is adopted： trigger-node layers, clusterhead-node layers, child- node layers. The number of child nodes and clusterheads depends on the distance of transmission. Clusterheads are selected according to the node 5 s weight which is based on its residual energy and distance to the trigger node. The main characteristic of this protocol is that clusterheads can directly communication with each other without the large-scale base station and antennas. Thus, the data from the trigger node would be able to be shared within the multi-layer structure. Considering the clustering process, energy model, and success rate, the simulation results show that the CEWEC protocol can effectively manage a large number of sensor nodes to share and transmit data.

Potential friendship discovery in social networks based on hybrid ensemble multiple collaborative filtering models in a 5G network environment

At present, 5G network technology is being applied to various social network modes, and it can provide technical and traffic support for social networks. Potential friendship discovery technology in 5G-enabled social networks is beneficial for users to make potential friends and expand their range of activities and social hierarchy, which is highly sought after in today's social networks and has great economic and application value. However, the sparsity of the dominant user association dataset in 5G-enabled social networks and the limitations of traditional collaborative filtering algorithms are two major challenges for the friend recommendation problem. Therefore, in order to overcome these problems regarding previous models, we propose a Hybrid Ensemble Multiple Collaborative Filtering Model (HEMCF) for discovering potential buddy relationships. The HEMCF model draws on a special autoencoder method that can effectively exploit the association matrix between friends and additional information to extract a hidden representation of users containing global structural information. Then, it uses the random walk-based graph embedding algorithm DeepWalk to extract another hidden representation of users in the buddy network containing local structural information. Finally, in the output module, the HEMCF model stacks and multiplies the two types of hidden representations of users to ensure that the information mentioned above is concentrated in the final output to generate the final prediction value. The magnitude of the prediction value represents the probability of the users being friends, with larger values representing a high probability of the two users being friends, and vice versa. Experimental results show that the proposed method boosts the accuracy of the relationship prediction over baselines on 3 real-world public datasets dramatically.

Information-Driven Collaborative Processing for Diffusive Source Estimation in Wireless Sensor Networks

This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.

Spatial Structure,Hierarchy and Formation Mechanisms of Scientific Collaboration Networks:Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative,and remarkable progress has been made since 2013.Taking the 65 countries along the Belt and Road(BRI countries)as the research areas and using collaborated Web of Science(WOS)core collection papers to construct an international scientific collaboration matrix,the paper explores the spatial structure,hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road.The results show that:1)Beyond the Belt and Road regions(BRI regions),Central&Eastern Europe,China and West Asia&North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions.China has the dominant role over other BRI countries in generating scientific links.The overall spatial structure has changed to a skeleton structure consisting of many dense regions,such as Europe,North America,East Asia and Oceania.2)Within the Belt and Road regions,Central&Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries.The spatial structure of scientific collaboration networks has transformed from the‘dual core’composed of China and the Central&Eastern Europe region,to the‘multi-polarization’composed of‘one zone and multi-points’.3)The hierarchical structure of scientific collaboration networks presents a typical‘core-periphery’structure,and changes from‘single core’to‘double cores’.4)Among the formation mechanisms of scientific collaboration networks,scientific research strength and social proximity play the most important roles,while geographical distance gradually weakens the hindrance to scientific collaboration.

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

Challenge-based collaborative intrusion detection in software-defined networking: An evaluation

Software-Defined Networking(SDN)is an emerging architecture that enables a computer network to be intelligently and centrally controlled via software applications.It can help manage the whole network environment in a consistent and holistic way,without the need of understanding the underlying network structure.At present,SDN may face many challenges like insider attacks,i.e.,the centralized control plane would be attacked by malicious underlying devices and switches.To protect the security of SDN,effective detection approaches are indispensable.In the literature,challenge-based collaborative intrusion detection networks(CIDNs)are an effective detection framework in identifying malicious nodes.It calculates the nodes'reputation and detects a malicious node by sending out a special message called a challenge.In this work,we devise a challenge-based CIDN in SDN and measure its performance against malicious internal nodes.Our results demonstrate that such a mechanism can be effective in SDN environments.

Community detection on elite mathematicians’collaboration network

Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

China's landscape in oncology drug research:perspectives from research collaboration networks

Objective： Better understanding of China＇s landscape in oncology drug research is of great significance for discovering anti-cancer drugs in future. This article differs from previous studies by focusing on Chinese oncology drug research communities in co-publication networks at the institutional level. Moreover, this research aims to explore structures and behaviors of relevant research units by thematic community analysis and to address policy recommendations. Methods： This research used social network analysis to define an institutions network and to identify a community network which is characterized by thematic content. Results： A total of 675 sample articles from 2008 through 2012 were retrieved from the Science Citation Index Expanded （SCIE） database of Web of Science, and top institutions and institutional pairs are highlighted for further discussion. Meanwhile, this study revealed that institutions based in the Chinese mainland are located in a relatively central position, Taiwan＇s institutions are closely assembled on the side, and Hong Kong＇s units located in the middle of the Chinese mainland＇s and Taiwan＇s. Spatial division and institutional hierarchy are still critical barriers to research collaboration in the field of anti-cancer drugs in China. In addition, the communities focusing on hot research areas show the higher nodal degree, whereas communities giving more attention to rare research subjects are relatively marginalized to the periphery of network. Conclusions= This paper offers policy recommendations to accelerate cross-regional cooperation, such as through developing information technology and increasing investment. The brokers should focus more on outreach to other institutions. Finally, participation in topics of common interest is conducive to improved efficiency in research and development （R＆D） resource allocation.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

Contextualized Analysis of Social Networks:Collaboration in Scientific Communities

Currently, the collaboration in scientific communities has been studied in order to explain, among other things, the knowledge diffusion. The quality of Graduate Programmes is often associated with the scientific collaboration. This paper discusses how scientific collaboration processes can be identified and characterized through social and complex networks. For this purpose, collaboration networks of bibliographic production, research projects, and committees of PhD theses and Masters’ dissertations by researchers from a graduate program in computational modeling were studied. The data were obtained from CAPES’ reports of the period from 2001 to 2009. Among the studied indices, centrality indices indicate the presence of prominent researchers who influence others and promptly interact with other researchers in the network. The indices of complex networks reveal the presence of the small-world (i.e. these networks are favorable to increase coordination between researchers) phenomenon and indicate a behavior of scale-free degree distribution (i.e. some researchers promote clustering more than others) for one of the studied networks.

Probe on Network-based Collaborative Maintenance Model for After-sales Equipment

In order to make equipment run safely, economically and continuously, some new maintenance models were put forward to improve the equipment after-sale maintenance service, such as E-maintenance, third-party maintenance, etc. To certain extent, the models solved the problem of the distance between the manufacturer and customer and the dispersion of the maintenance technologies, however, those resources are still widely distributed and do not collaborate efficiently. In this paper, a network-based collaborative maintenance service model was proposed for after-sales equipment to solve the problem of maintenance resources integration. Concretely, equipment designers, maintainers, spare parts suppliers and maintenance experts were grouped together to establish dynamic alliance. The leader of the alliance is the manufacturer under guaranty period or equipment user exceeding the guaranty period. The process of maintenance service was divided into three stages which are fault diagnosis, maintenance decision and maintenance implementation. The sub-alliances were established to carry out maintenance work at each stage. In addition, the business process of network-based collaborative maintenance was analyzed and collaborative business system for equipment＇s after-sales collaborative maintenance service was designed. In the end, an informational economics model of network-based collaborative maintenance was established to demonstrate the effectiveness of this maintenance model.

The Performance Analysis for Distributed Collaborative Beamforming Transmission of Clustered Mobile Sensor Network

Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.

Three vertex degree correlations of fixed act-size collaboration networks

A rate equation approach was presented for the exact computation of the three vertex degree correlations of the fixed act-size collaboration networks.Measurements of the three vertex degree correlations were based on a rate equation in the continuous degree and time approximation for the average degree of the nearest neighbors of vertices of degree k,with an appropriate boundary condition.The rate equation proposed can be generalized in more sophisticated growing network models,and also extended to deal with related correlation measurements.Finally,in order to check the theoretical prediction,a numerical example was solved to demonstrate the performance of the degree correlation function.

Collaborative work model based on peer-to-peer network

In this paper, we incorporated peer-to-peer (P2P) concept with agent technology and put forward a collaborative work model based on peer-to-peer network (P2PCWM) after investigating into business demands of modern enterprises and problems prevailing in mainstream collaborative work systems based on central server. Theoretically, the P2PCWM can effectively overcome the problems in a conventional system with a central server and meet the practical demands of modern businesses. It is distinguished from other systems by its features of equality, openness, promptness, fairness, expandability and convenience.