Blockchain-Based Data Acquisition with Privacy Protection in UAV Cluster Network

The unmanned aerial vehicle(UAV)self-organizing network is composed of multiple UAVs with autonomous capabilities according to a certain structure and scale,which can quickly and accurately complete complex tasks such as path planning,situational awareness,and information transmission.Due to the openness of the network,the UAV cluster is more vulnerable to passive eavesdropping,active interference,and other attacks,which makes the system face serious security threats.This paper proposes a Blockchain-Based Data Acquisition(BDA)scheme with privacy protection to address the data privacy and identity authentication problems in the UAV-assisted data acquisition scenario.Each UAV cluster has an aggregate unmanned aerial vehicle(AGV)that can batch-verify the acquisition reports within its administrative domain.After successful verification,AGV adds its signcrypted ciphertext to the aggregation and uploads it to the blockchain for storage.There are two chains in the blockchain that store the public key information of registered entities and the aggregated reports,respectively.The security analysis shows that theBDAconstruction can protect the privacy and authenticity of acquisition data,and effectively resist a malicious key generation center and the public-key substitution attack.It also provides unforgeability to acquisition reports under the Elliptic Curve Discrete Logarithm Problem(ECDLP)assumption.The performance analysis demonstrates that compared with other schemes,the proposed BDA construction has lower computational complexity and is more suitable for the UAV cluster network with limited computing power and storage capacity.

Distributed Satellite Cluster Network:a Survey

The ability of the monolithic satellite,satellite orbit(especially GEO),and radio resource are very limited,so the development of distributed satellite cluster network(DSCN) receives more and more worldwide attention.In this paper,DSCN is surveyed and the study status of DSCN architecture design is summarized.The formation flying of spacecrafts,reconfiguration,networking,and applied research on distributed satellite spacecraft are described in detail.The DSCN will provide a great technology innovation for space information network,satellite communications,satellite navigation,deep space exploration,and space remote sensing.In addition,this paper points out future trends of the DSCN development.

A Hybrid Approach for Predicting Probability of Default in Peer-to-Peer (P2P) Lending Platforms Using Mixture-of-Experts Neural Network

Peer-to-peer (P2P) lending offers an alternative way to access credit. Unlike established lending institutions with proven credit risk management practices, P2P platforms rely on numerous independent variables to evaluate loan applicants’ creditworthiness. This study aims to estimate default probabilities using a mixture-of-experts neural network in P2P lending. The approach involves coupling unsupervised clustering to capture essential data properties with a classification algorithm based on the mixture-of-experts structure. This classic design enhances model capacity without significant computational overhead. The model was tested using P2P data from Lending Club, comparing it to other methods like Logistic Regression, AdaBoost, Gradient Boosting, Decision Tree, Support Vector Machine, and Random Forest. The hybrid model demonstrated superior performance, with a Mean Squared Error reduction of at least 25%.

Relationships and Evolving Networks of Rural Manufacturing Clusters: A Case Study in Yucheng County, Henan Province of China

The evolution of networks in rural industrial clusters,in particular in the context of China has been paid more attention to in the world.Applying the theory and techniques of social network analysis (SNA),this study is with particular regard to the business network relationships and their evolutionary dynamics of steel measuring tape manufacturing clustered in Nanzhuang Village,Yucheng County of Henan Province,China,which is important for better understanding the industrial and regional development in less developed rural areas.From data collected by comprehensive questionnaire survey in 2002 and mass interviews with 60 enterprises and assembling families and several government authorities in 2002,2003,2004,2005 and 2008,four types of networks are identified: spin-off,consulting,communication and cooperative.The characteristic of these networks is outlined in detail.Compared with the high-tech clusters of typical developed areas,the networks that have evolved in traditional manufacturing clusters are more affected by emotive linkages.The cluster networks are shown to exhibit a polycentric hierarchical structure.The family relationships are the dominate spin-off channels of enterprises,while the supply and demand relationships and the mobility of the skilled workers are also important paths of network learning,and the cooperation relationships are comparatively stable.Besides the root enterprises,the middle-sized enterprises are comparatively more active than small-sized enterprises,and the intermediary agencies and the service institutions act as bridges of the inter-enterprises cooperation.By analysis of the structure of networks and the interactions between the networks,the four stages of network evolution are also identified.The four stages are dominated by the family networks,the internal division production networks,the local innovation networks and the global supply networks respectively,and they play different roles in cluster development.

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.

Energy efficient clustering algorithm based on neighbors for wireless sensor networks

In this paper, an energy efficient clustering algorithm based on neighbors （EECABN） for wireless sensor networks is proposed. In the algorithm, an optimized weight of nodes is introduced to determine the priority of clustering procedure. As improvement, the weight is a measurement of energy and degree as usual, and even associates with distance from neighbors, distance to the sink node, and other factors. To prevent the low energy nodes being exhausted with energy, the strong nodes should have more opportunities to act as cluster heads during the clustering procedure. The simulation results show that the algorithm can effectively prolong whole the network lifetime. Especially at the early stage that some nodes in the network begin to die, the process can be postponed by using the algorithm.

A routing algorithm based on clusters and balanced use of energy for wireless sensor networks

In a sensor network, data collected by different sensors are often correlated because they are observations of related phenomena. Efficient sensor data fusion is one of the most important issues in building real sensor networks. To balance energy cost, how to select a cluster head is a key problem that must be addressed. In this paper, we use a compression-centric data collection algorithm for use in wireless sensor networks. Also, we propose a balanced cluster head selection algorithm in each cluster. Simulation results are used to investigate the performance of the algorithm. Compared to the exhaustive search solutions, the proposed algorithm shows a significant improvement in power consumption.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

A Power- and Coverage-aware Clustering Scheme for Wireless Sensor Networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.

Improved Clustering and Resource Allocation for Ultra-Dense Networks

To reduce the interference among small cells of Ultra-Dense Networks(UDN),an improved Clustering-Assisted Resource Allocation(CARA)scheme is proposed in this paper.The proposed scheme is divided into three steps.First,an Interference-Limited Clustering Algorithm(ILCA)based on interference graph corresponding to the interference relationship between Femtocell Base Stations(FBSs),is proposed to group FBSs into disjoint clusters,in which a pre-threshold is set to constrain the sum of interference in each cluster,and a Cluster Head(CH)is selected for each cluster.Then,CH performs a twostage sub-channel allocation within its associated cluster,where the first stage assigns one sub-channel to each user of the cluster and the second stage assigns a second sub-channel to some users.Finally,a power allocation method is designed to maximize throughput for a given clustering and sub-channel configuration.Simulation results indicate that the proposed scheme distributes FBSs into each cluster more evenly,and significantly improves the system throughput compared with the existing schemes in the same scenario.

Cluster synchronization in a network of non-identical dynamic systems

Cluster synchronization in a network of non-identical dynamic systems is studied in this paper, using two-cluster synchronization for detailed analysis and discussion. The results show that the common intercluster coupling condition is not always needed for the diffusively coupled network. Several sufficient conditions are obtained by using the Schur unitary triangularization theorem, which extends previous results. Some numerical examples are presented for illustration.

MRBCH: A Multi-Path Routing Protocol Based on Credible Cluster Heads for Wireless Sensor Networks

Wireless sensor networks are widely used for its flexibility, but they also suffer from problems like limited capacity, large node number and vulnerability to security threats. In this paper, we propose a multi-path routing protocol based on the credible cluster heads. The protocol chooses nodes with more energy remained as cluster heads at the cluster head choosing phase, and then authenticates them by the neighbor cluster heads. Using trust mechanisms it creates the credit value, and based on the credit value the multi-path cluster head routing can finally be found. The credit value is created and exchanged among the cluster heads only. Theoretical analysis combined with simulation results demonstrate that this protocol can save the resource, prolong the lifetime, and ensure the security and performance of the network.

Clustering approach based on hierarchical expansion for community detection of scientific collaboration network

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network. The characteristics of achievements information related to scientific and technological domains are analyzed,and then an ontology that represents their latent collaborative relations is built to detect clusters from the collaboration network. A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained. A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper. This work also lays out a novel insight into the exploitation of scientific collaboration network to better classify achievements information.

Clustering routing algorithm of wireless sensor networks based on Bayesian game

To avoid uneven energy consuming in wireless sen- sor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.

Synchronizability of Two-Layer Cluster Ring Networks

Multilayer network is a frontier direction of network science research. In this paper, the cluster ring network is extended to a two-layer network model, and the inner structures of the cluster blocks are random, small world or scale-free. We study the influence of network scale, the interlayer linking weight and interlayer linking fraction on synchronizability. It is found that the synchronizability of the two-layer cluster ring network decreases with the increase of network size. There is an optimum value of the interlayer linking weight in the two-layer cluster ring network, which makes the synchronizability of the network reach the optimum. When the interlayer linking weight and the interlayer linking fraction are very small, the change of them will affect the synchronizability.

Optimal Energy Efficiency Resource Allocation Strategy for Cognitive Clustering Network under PUEA Attack

5G has pushed the use of radio spectrum to a new level,and cognitive clustering network can effectively improve the utilization of radio spectrum,which is a feasible way to solve the growing demand for wireless communications.However,cognitive clustering network is vulnerable to PUEA attack,which will lead to the degradation of system detection performance,thereby reducing the energy efficiency.Aiming at these problems,this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack.A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance.We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem,which is solved by Lagrangian function and Karush-Kuhn-Tucker condition.Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed.Simulation results indicate that proposed method is effective when subjected to PUEA attacks,and the impact of different parameters on energy efficiency is analyzed.

Survey of Clustering Schemes in Mobile Ad hoc Networks

Mobile ad-hoc networks (MANETs) are a specific kind of wireless networks that can be quickly deployed without pre- existing infrastructures. They are used in different contexts such as collaborative, medical, military or embedded applications. However, MANETs raise new challenges when they are used in large scale network that contain a large number of nodes. Subsequently, many clustering algorithms have emerged. In fact, these clustering algorithms allow the structuring of the network into groups of entities called clusters creating a hierarchical structure. Each cluster contains a particular node called cluster head elected as cluster head according to a specific metric or a combination of metrics such as identity, degree, mobility, weight, density, etc. MANETs has drawbacks due to both the characteristics of the transmission medium (transmission medium sharing, low bandwidth, etc.) and the routing protocols (information diffusion, path finding, etc.). Clustering in mobile ad hoc networks plays a vital role in improving resource management and network performance (routing delay, bandwidth consumption and throughput). In this paper, we present a study and analyze of some existing clustering approaches for MANETs that recently appeared in literature, which we classify as: Identifier Neighbor based clustering, Topology based clustering, Mobility based clustering, Energy based clustering, and Weight based clustering. We also include clustering definition, review existing clustering approaches, evaluate their performance and cost, discuss their advantages, disadvantages, features and suggest a best clustering approach.

Effects of internal noise on the spiking regularity of a clustered Hodgkin–Huxley neuronal network

Spiking regularity in a clustered Hodgkin–Huxley（HH） neuronal network has been studied in this letter. A stochastic HH neuronal model with channel blocks has been applied as local neuronal model. Effects of the internal channel noise on the spiking regularity are discussed by changing the membrane patch size. We find that when there is no channel blocks in potassium channels, there exist some intermediate membrane patch sizes at which the spiking regularity could reach to a higher level. Spiking regularity increases with the membrane patch size when sodium channels are not blocked. Namely, depending on different channel blocking states, internal channel noise tuned by membrane patch size could have different influence on the spiking regularity of neuronal networks.