Strengthening network slicing for Industrial Internet with deep reinforcement learning

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology.Guaranteeing robust network slicing is essential for Industrial Internet,but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions(NFs)composing the slice.Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties.Towards this end,we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements.State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution.Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Analysis of Mobile and Internet Network Coverage: Propagation of Electromagnetic Waves and Concept of Digital Divide in Burundi

Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output layer.Finally, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

Unweighted Voting Method to Detect Sinkhole Attack in RPL-Based Internet of Things Networks

The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.

Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

As the Internet of Things(IoT)endures to develop,a huge count of data has been created.An IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause accidents.As typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network environments.Machine learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable accuracy.This article introduces a new Bird Swarm Algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT platform.The main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT platform.The BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain this.Next,to detect intrusions,the WNN model is utilized.At last,theWNNparameters are optimally modified by the use of BSA.Awidespread experiment is performed to depict the better performance of the BSAWNNID technique.The resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical resources.By grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the organization.Nevertheless,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions negatively.In this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based IoT.Three consecutive segments are suggested.For the covering of adjusted clusters,an ideal number of clusters is determined first.Then,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor hubs.Cluster heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster members.Specifically,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long time.The assessment results demonstrate that the IEPA performs better than existing conventions.

Network Learning-Enabled Sensor Association for Massive Internet of Things

The massive Internet of Things(IoT)comprises different gateways(GW)covering a given region of a massive number of connected devices with sensors.In IoT networks,transmission interference is observed when different sensor devices(SD)try to send information to a single GW.This is mitigated by allotting various channels to adjoining GWs.Furthermore,SDs are permitted to associate with anyGWin a network,naturally choosing the one with a higher received signal strength indicator(RSSI),regardless of whether it is the ideal choice for network execution.Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs.Recently,in remote IoT networks,the utilization of machine learning(ML)strategies has arisen as a viable answer to determine the effect of various models in the system,and reinforcement learning(RL)is one of these ML techniques.Therefore,this paper proposes the use of an RL algorithm for GW determination and association in IoT networks.For this purpose,this study allows GWs and SDs with intelligence,through executing the multi-armed bandit(MAB)calculation,to investigate and determine the optimal GW with which to associate.In this paper,rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations,which include different IoT network topologies.The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art(RSSI-based)and related research approaches.

DDoS Attack Detection Method for Space-Based Network Based on SDN Architecture

With the development of satellite communications,the number of satellite nodes is constantly increasing,which undoubtedly increases the difficulty of maintaining network security.Combining software defined network(SDN) with traditional space-based networks provides a new class of ideas for solving this problem.However,because of the highly centralized network management of the SDN controller,once the SDN controller is destroyed by network attacks,the network it manages will be paralyzed due to loss of control.One of the main security threats to SDN controllers is Distributed Denial of Service(DDoS) attacks,so how to detect DDoS attacks scientifically has become a hot topic among SDN security management.This paper proposes a DDoS attack detection method for space-based networks based on SDN architecture.This attack detection method combines the optimized Long Short-Term Memory(LSTM) deep learning model and Support Vector Machine(SVM),which can not only make classification judgments on the time series,but also achieve the purpose of detecting and judging through the flow characteristics of a period of time.In addition,it can reduce the detection time as well as the system burden.

Mobile Communication Voice Enhancement Under Convolutional Neural Networks and the Internet of Things

This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Caching Strategies in NDN Based Wireless Ad Hoc Network:A Survey

Wireless Ad Hoc Networks consist of devices that are wirelessly connected.Mobile Ad Hoc Networks(MANETs),Internet of Things(IoT),and Vehicular Ad Hoc Networks(VANETs)are the main domains of wireless ad hoc network.Internet is used in wireless ad hoc network.Internet is based on Transmission Control Protocol(TCP)/Internet Protocol(IP)network where clients and servers interact with each other with the help of IP in a pre-defined environment.Internet fetches data from a fixed location.Data redundancy,mobility,and location dependency are the main issues of the IP network paradigm.All these factors result in poor performance of wireless ad hoc networks.The main disadvantage of IP is that,it does not provide in-network caching.Therefore,there is a need to move towards a new network that overcomes these limitations.Named Data Network(NDN)is a network that overcomes these limitations.NDN is a project of Information-centric Network(ICN).NDN provides in-network caching which helps in fast response to user queries.Implementing NDN in wireless ad hoc network provides many benefits such as caching,mobility,scalability,security,and privacy.By considering the certainty,in this survey paper,we present a comprehensive survey on Caching Strategies in NDN-based Wireless AdHocNetwork.Various cachingmechanism-based results are also described.In the last,we also shed light on the challenges and future directions of this promising field to provide a clear understanding of what caching-related problems exist in NDN-based wireless ad hoc networks.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Node Sociability Based Intelligent Routing for Post-Disaster Emergency Networks

In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are ineffective.Although the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission delays.To address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios.First,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values.Second,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values.Finally,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

Enhanced Mechanism for Link Failure Rerouting in Software-Defined Exchange Point Networks

Internet Exchange Point(IXP)is a system that increases network bandwidth performance.Internet exchange points facilitate interconnection among network providers,including Internet Service Providers(ISPs)andContent Delivery Providers(CDNs).To improve service management,Internet exchange point providers have adopted the Software Defined Network(SDN)paradigm.This implementation is known as a Software-Defined Exchange Point(SDX).It improves network providers’operations and management.However,performance issues still exist,particularly with multi-hop topologies.These issues include switch memory costs,packet processing latency,and link failure recovery delays.The paper proposes Enhanced Link Failure Rerouting(ELFR),an improved mechanism for rerouting link failures in software-defined exchange point networks.The proposed mechanism aims to minimize packet processing time for fast link failure recovery and enhance path calculation efficiency while reducing switch storage overhead by exploiting the Programming Protocol-independent Packet Processors(P4)features.The paper presents the proposed mechanisms’efficiency by utilizing advanced algorithms and demonstrating improved performance in packet processing speed,path calculation effectiveness,and switch storage management compared to current mechanisms.The proposed mechanism shows significant improvements,leading to a 37.5%decrease in Recovery Time(RT)and a 33.33%decrease in both Calculation Time(CT)and Computational Overhead(CO)when compared to current mechanisms.The study highlights the effectiveness and resource efficiency of the proposed mechanism in effectively resolving crucial issues inmulti-hop software-defined exchange point networks.

Ultra reliability and massive connectivity provision in integrated internet of military things(IoMT)based on tactical datalink

One of the major challenges arising in internet of military things(IoMT)is accommodating massive connectivity while providing guaranteed quality of service(QoS)in terms of ultra-high reliability.In this regard,this paper presents a class of code-domain nonorthogonal multiple accesses(NOMAs)for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system(JTIDS).In the considered scenario,a satellite equipped with Nr antennas servers K devices including vehicles,drones,ships,sensors,handset radios,etc.Nonorthogonal coded modulation,a special form of multiple input multiple output(MIMO)-NOMA is proposed.The discussion starts with evaluating the output signal to interference-plus-noise(SINR)of receiver filter,leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered.The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved.The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying(MSK)and Gaussian MSK(GMSK),two pivotal modulation formats in IoMT standards such as Link-16 and JITDS.Numerical results show that near capacity performance is offered.Fortunately,the performance is obtained using simple forward error corrections(FECs)of higher coding rate than existing schemes do,while the transmit power is reduced by 6 dB.The proposed design finds wide applications not only in IoMT but also in deep space communications,where ultra reliability and massive connectivity is a keen concern.