SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Security Implications of Edge Computing in Cloud Networks

Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.

Cloud Service Provisioning Based on Peer-to-Peer Network for Flexible Service Sharing and Discovery

In this paper, we present an approach to establish efficient and scalable service provisioning in the cloud environment using P2P-based infrastructure for storing, sharing and discovering services. Unlike most other P2P-based approaches, it allows flexible search queries, since all of them are executed against internal database presenting at each overlay node. Various issues concerning using this approach in the cloud environment, such as load-balancing, queuing, dealing with skewed data and dynamic attributes, are addressed in the paper. The infrastructure proposed in the paper can serve as a base for creating robust, scalable and reliable cloud systems, able to fulfill client’s QoS requirements, and at the same time introduce more efficient utilization of resources to the cloud provider.

Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources.In this paper,we study the task scheduling problem in the hierarchically deployed edge cloud.We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the problem.Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based solution.JNNHSP takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling solution.In-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process.Furthermore,Android malware is increasing on a daily basis.So,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile devices.In this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud environment.The pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud platforms.The presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter tuning.To detect and classify the malware,the GCNN model is exploited in this work.In order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

Chaotic Metaheuristics with Multi-Spiking Neural Network Based Cloud Intrusion Detection

Cloud Computing(CC)provides data storage options as well as computing services to its users through the Internet.On the other hand,cloud users are concerned about security and privacy issues due to the increased number of cyberattacks.Data protection has become an important issue since the users’information gets exposed to third parties.Computer networks are exposed to different types of attacks which have extensively grown in addition to the novel intrusion methods and hacking tools.Intrusion Detection Systems(IDSs)can be used in a network to manage suspicious activities.These IDSs monitor the activities of the CC environment and decide whether an activity is legitimate(normal)or malicious(intrusive)based on the established system’s confidentiality,availability and integrity of the data sources.In the current study,a Chaotic Metaheuristics with Optimal Multi-Spiking Neural Network-based Intrusion Detection(CMOMSNN-ID)model is proposed to secure the cloud environment.The presented CMOMSNNID model involves the Chaotic Artificial Bee Colony Optimization-based Feature Selection(CABC-FS)technique to reduce the curse of dimensionality.In addition,the Multi-Spiking Neural Network(MSNN)classifier is also used based on the simulation of brain functioning.It is applied to resolve pattern classification problems.In order to fine-tune the parameters relevant to theMSNN model,theWhale Optimization Algorithm(WOA)is employed to boost the classification results.To demonstrate the superiority of the proposed CMOMSNN-ID model,a useful set of simulations was performed.The simulation outcomes inferred that the proposed CMOMSNN-ID model accomplished a superior performance over other models with a maximum accuracy of 99.20%.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

A Hierarchical Trust Model for Peer-to-Peer Networks

Trust has become an increasingly important issue given society’s growing reliance on electronic transactions.Peer-to-peer(P2P)networks are among the main electronic transaction environments affected by trust issues due to the freedom and anonymity of peers(users)and the inherent openness of these networks.A malicious peer can easily join a P2P network and abuse its peers and resources,resulting in a large-scale failure that might shut down the entire network.Therefore,a plethora of researchers have proposed trust management systems to mitigate the impact of the problem.However,due to the problem’s scale and complexity,more research is necessary.The algorithm proposed here,HierarchTrust,attempts to create a more reliable environment in which the selection of a peer provider of a file or other resource is based on several trust values represented in hierarchical form.The values at the top of the hierarchical form are more trusted than those at the lower end of the hierarchy.Trust,in HierarchTrust,is generally calculated based on the standard deviation.Evaluation via simulation showed that HierarchTrust produced a better success rate than the well-established EigenTrust algorithm.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

Point cloud upsampling generative adversarial network based on residual multi-scale off-set attention

Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.

Peer-to-Peer Networks 2

The development of network resources changes the network computing models. P2P networks, a new type of network adopting peer-to-peer strategy for computing, have attracted worldwide attention. The P2P architecture is a type of distributed network in which all participants share their hardware resources and the shared resources can be directly accessed by peer nodes without going through any dedicated servers. The participants in a P2P network are both resource providers and resource consumers. This article on P2P networks is divided into two issues. In the previous issue, P2P architecture, network models and core search algorithms were introduced. The second part in this issue is analyzing the current P2P research and application situations, as well as the impacts of P2P on telecom operators and equipment vendors.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

“Half of the Node Records Are Forged?”: The Problemof Node Records Forgery in Ethereum Network

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security.In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Research on Construction Scheme of Cloud Platform for Core Network Equipment of Railway 5G Private Network

This paper studies and analyzes the rigorous requirements of railway 5G private network core network(5GC)equipment based on network function virtualization(NFV)technology in terms of reliability,security,latency and other aspects of communication cloud,compares cloud platform schemes with different decoupling modes,and proposes that railway 5GC should be implemented by software and hardware integration scheme or software and hardware two-layer decoupling scheme.At the same time,the redundancy and disaster recovery schemes and measures that can be taken by 5GC based on cloud platform are proposed.Finally,taking the products of ZTE Corporation as an example,the implementation architecture of railway 5GC cloud platform in 1+1 redundancy mode is given.It serves as a reference for the engineering construction of 5G-R core network.

Performance of Fixed Channel Assignment for Uplink Transmission and Direct Peer-to-Peer Communications in Multihop Cellular Networks

In this paper, we propose a clustered multihop cellular network （cMCN） architecture and study its performance using fixed channel assignment （FCA） scheme for uplink transmission. The proposed cMCN using FCA can be applied with some reuse factors. An analytical model based on Markov chain is developed to analyze its performance and validated through computer simulation. And then, we implement direct peer-to-peer communication （DC） in cMCN by considering more reasonable conditions in practice. DC means that two calls communicate directly instead of going through base stations. The results show that cMCN with FCA can reduce the call blocking probability significantly as compared with the traditional single-hop cellular networks with FCA and can be further reduced by using DC.

A Credit-Based Approach for Overcoming Free-Riding Behaviour in Peer-to-Peer Networks

The underlying premise of peer-to-peer(P2P)systems is the trading of digital resources among individual peers to facilitate file sharing,distributed computing,storage,collaborative applications and multimedia streaming.So-called free-riders challenge the foundations of this system by consuming resources from other peers without offering any resources in return,hindering resource exchange among peers.Therefore,immense effort has been invested in discouraging free-riding and overcoming the ill effects of such unfair use of the system.However,previous efforts have all fallen short of effectively addressing free-riding behaviour in P2P networks.This paper proposes a novel approach based on utilising a credit incentive for P2P networks,wherein a grace period is introduced during which free-riders must reimburse resources.In contrast to previous approaches,the proposed system takes into consideration the upload rate of peers and a grace period.The system has been thoroughly tested in a simulated environment,and the results show that the proposed approach effectively mitigates free-riding behaviour.Compared to previous systems,the number of downloads from free-riders decreased while downloads by contributing peers increased.The results also show that under longer grace periods,the number of downloads by fast peers(those reimbursing the system within the grace period)was greater than the number of downloads by slow peers.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

Peer-to-Peer Networks

The development of network resources changes network computing models. P2P networks, a new type of network adopting peer-to-peer strategy for computing, have attracted world-wide attention. P2P architecture is a type of distributed network in which all participants share their hardware resources and the shared resources can be directly accessed by peer nodes without the necessity of going through any dedicated servers. The participants in a P2P network are both resource providers and resource consumers. This article on P2P networks will be divided into two issues. In this issue, P2P architecture, network models and core search algorithms are introduced. And the second part in the next issue will analyze the current P2P research and application situations, as well as the impact of P2P on telecom operators and equipment vendors.