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.

Scope of machine learning applications for addressing the challenges in next-generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users.Due to this popularity,there has been a huge rise in mobile data volume,applications,types of services,and number of customers.Furthermore,due to the COVID-19 pandemic,the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home.This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks.The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic,such as real-time live streaming of videos,audios,text,images etc.,at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers.Next-generation wireless networks(NGWNs,i.e.5G networks and beyond)are being developed to accommodate the service qualities mentioned above and many more.However,achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers.It requires the analysis of a huge volume of network data(structured and unstructured)received or collected from heterogeneous devices,applications,services,and customers and the effective and dynamic management of network parameters based on this analysis in real time.In the ever-increasing network heterogeneity and complexity,machine learning(ML)techniques may become an efficient tool for effectively managing these issues.In recent days,the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain.This study discusses current wireless network research,brief discussions on ML methods that can be effectively applied to the wireless networking domain,some tools available to support and customise efficient mobile system design,and some unresolved issues for future research directions.

Powering Next-Generation Broadband Networks: ZTE's World-First Flexible, Configurable Router

16 September 2013, Shenzhen--ZTE today unveiled the world＇s first flexible, reconfigurable terabit router that allows customers to build the highest-performance broadband networks. The terabit router supports the deployment of multiple line cards with processing capabilities of 10 Gbps to 1 Tbps. It also supports the deployment of modules that can scale throughput from 200 Gbps to 18 Tbps. For easy installation in a range of environments, the router interfaces are flexible and the component design is loose-coupled. This allows customers to customize networks to their needs and promotes adaptability, consistency, and continuity.

Data Secure Storage Mechanism of Sensor Networks Based on Blockchain

As the number of sensor network application scenarios continues to grow,the security problems inherent in this approach have become obstacles that hinder its wide application.However,it has attracted increasing attention from industry and academia.The blockchain is based on a distributed network and has the characteristics of non-tampering and traceability of block data.It is thus naturally able to solve the security problems of the sensor networks.Accordingly,this paper first analyzes the security risks associated with data storage in the sensor networks,then proposes using blockchain technology to ensure that data storage in the sensor networks is secure.In the traditional blockchain,the data layer uses a Merkle hash tree to store data;however,the Merkle hash tree cannot provide non-member proof,which makes it unable to resist the attacks of malicious nodes in networks.To solve this problem,this paper utilizes a cryptographic accumulator rather than a Merkle hash tree to provide both member proof and non-member proof.Moreover,the number of elements in the existing accumulator is limited and unable to meet the blockchain’s expansion requirements.This paper therefore proposes a new type of unbounded accumulator and provides its definition and security model.Finally,this paper constructs an unbounded accumulator scheme using bilinear pairs and analyzes its performance.

Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

The evolving“Industry 4.0”domain encompasses a collection of future industrial developments with cyber-physical systems(CPS),Internet of things(IoT),big data,cloud computing,etc.Besides,the industrial Internet of things(IIoT)directs data from systems for monitoring and controlling the physical world to the data processing system.A major novelty of the IIoT is the unmanned aerial vehicles(UAVs),which are treated as an efficient remote sensing technique to gather data from large regions.UAVs are commonly employed in the industrial sector to solve several issues and help decision making.But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs.Federated learning(FL)becomes a recent advancement of machine learning(ML)which aims to protect user data.In this aspect,this study designs federated learning with blockchain assisted image classification model for clustered UAV networks(FLBIC-CUAV)on IIoT environment.The proposed FLBIC-CUAV technique involves three major processes namely clustering,blockchain enabled secure communication and FL based image classification.For UAV cluster construction process,beetle swarm optimization(BSO)algorithm with three input parameters is designed to cluster the UAVs for effective communication.In addition,blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers.Finally,the cloud server uses an FL with Residual Network model to carry out the image classification process.A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach.The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and self-management.The special structure of WSN brings both convenience and vulnerability.For example,a malicious participant can launch attacks by capturing a physical device.Therefore,node authentication that can resist malicious attacks is very important to network security.Recently,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for WSN.In our scheme,all nodes are managed by utilizing the identity information stored on the blockchain.Besides,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection rate.The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

AI-Driven Energy Optimization in UAV-Assisted Routing for Enhanced Wireless Sensor Networks Performance

In recent advancements within wireless sensor networks(WSN),the deployment of unmanned aerial vehicles(UAVs)has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality.This research introduces a sophisticated framework,driven by computational intelligence,that merges clustering techniques with UAV mobility to refine routing strategies in WSNs.The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads(CHs).This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination.Employing a greedy algorithm for inter-cluster dialogue,our framework orchestrates CHs into an efficient communication chain.Through comparative analysis,the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing(CCMAR)and the energy-efficient cluster-based dynamic algorithms(ECCRA).Specifically,it showcases an impressive 15%increase in energy conservation and a 20%reduction in data transmission time,highlighting its advanced performance.Furthermore,this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN,emphasizing the vital role of sophisticated computational strategies in optimizing network operations.

Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.

Efficient Digital Twin Placement for Blockchain-Empowered Wireless Computing Power Network

As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.

Unintentional forking analysis in wireless blockchain networks

The forking problem plays a key role in the security issue,which is a major concern in the blockchain system.Although many works studied the attack strategy,consensus mechanism,privacy-protecting and security performance analysis,most of them only address the intentional forking caused by a malicious attacker.In fact,without any attacker,unintentional forking still remains due to transmission delay and failure,especially in wireless network scenarios.To this end,this paper investigates the reason for generating unintentional forking and derives the forking probability expression in Wireless Blockchain Networks(WBN).Furthermore,in order to illustrate the unintentional forking on the blockchain system,the performances in terms of resource utilization rate,block generation time,and Transaction Per Second(TPS)are investigated.The numerical results show that the target difficulty of hash algorithm in generating a new block,the delay time of broadcasting,the network scale,and the transmission failure probability would affect the unintentional forking probability significantly,which can provide a reliable basis for avoiding forking to save resource consumption and improving system performance.

Wireless Sensor Networks Routing Attacks Prevention with Blockchain and Deep Neural Network

Routing is a key function inWireless Sensor Networks(WSNs)since it facilitates data transfer to base stations.Routing attacks have the potential to destroy and degrade the functionality ofWSNs.A trustworthy routing system is essential for routing security andWSN efficiency.Numerous methods have been implemented to build trust between routing nodes,including the use of cryptographic methods and centralized routing.Nonetheless,the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities.At the moment,there is no effective way to avoid malicious node attacks.As a consequence of these concerns,this paper proposes a trusted routing technique that combines blockchain infrastructure,deep neural networks,and Markov Decision Processes(MDPs)to improve the security and efficiency of WSN routing.To authenticate the transmission process,the suggested methodology makes use of a Proof of Authority(PoA)mechanism inside the blockchain network.The validation group required for proofing is chosen using a deep learning approach that prioritizes each node’s characteristics.MDPs are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting messages.According to testing data,our routing system outperforms current routing algorithms in a 50%malicious node routing scenario.

Blockchain Based Secure Relay Scheme for Space-Terrestrial Integrated Networks

The future 6G networks will integrates space and terrestrial networks to realize a fully connected world with extensive collaboration.However,how to build trust between multiple parties is a difficult problem for secure cooperation without a reliable third-party.Blockchain is a promising technology to solve this problem by converting the trust between multi-parties to the trust to the common shared data.Several works have proposed to apply the incentive mechanism in blockchain to encourage effective cooperation,but how to evaluate the cooperation performance and avoid breach of contract is not discussed.In this paper,a secure relay scheme is proposed based on the consortium blockchain system composed by different operators.In particular,smart contract checks the integrity of the message based on RSA accumulator,and executes transactions automatically when the message is delivered successfully.Detailed procedures are introduced for both uplink and downlink relay.Implementation based on Hyperledger Fabric proves the effectiveness of the proposed scheme and shows that the complexity of the scheme is low enough for practical deployment.

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical system.In order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire systemorchestration.Hence,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised earlier.By performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy.Additionally,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions.Our model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model uploading.Second,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks.The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN environment.In addition,an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS algorithm.Moreover,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks

With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking applications.Wireless Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security issues.Blockchain is designed as a game changer for highly secure and effective digital society.So,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as MCRP-BWSN.The proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered WSN.In MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected clusters.In addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onfitness function.Besides,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the network.Sensor nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different measures.Based on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques.

Trust Access Authentication in Vehicular Network Based on Blockchain

Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles.Blockchain,which can provide secure authentication and protected privacy,is a crucial technology.However,traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles.In this situation,edge computing enabled Blockchain network appeals to be a promising method,where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly.In this paper,a hierarchical architecture is proposed consist of vehicular network layer,Blockchain edge layer and Blockchain network layer.Through a authentication mechanism adopting digital signature algorithm,it achieves trusted authentication and ensures valid verification.Moreover,a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles.Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on central-ized model or edge caching strategy in terms of hit ratio and average delay.

Blockchain-Based Secure Distributed Control for Software Defined Optical Networking

Software defined optical networking(SDON)is a critical technology for the next generation network with the advantages of programmable control and etc.As one of the key issues of SDON,the security of control plane has also received extensive attention,especially in certain network scenarios with high security requirement.Due to the existence of vulnerabilities and heavy overhead,the existing firewalls and distributed control technologies cannot solve the control plane security problem well.In this paper,we propose a distributed control architecture for SDON using the blockchain technique(BlockCtrl).The proposed BlockCtrl model introduces the advantages of blockchain into SDON to achieve a high-efficiency fault tolerant control.We have evaluated the performance of our proposed architecture and compared it to the existing models with respect to various metrics including processing rate,recovery latency and etc.The numerical results show that the BlockCtrl is capable of attacks detection and fault tolerant control in SDON with high performance on resource utilization and service correlation.

AABN: Anonymity Assessment Model Based on Bayesian Network With Application to Blockchain

Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like bitcoin.The anonymity of blockchain has caused widespread concern.In this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian Network.Firstly,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment schemes.Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail.Finally,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.

An Erebus Attack Detection Method Oriented to Blockchain Network Layer

Recently,the Erebus attack has proved to be a security threat to the blockchain network layer,and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer.The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks.This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer.The goal is to improve the performance of Erebus attack detection methods,by combining the traffic behavior with the routing status based on multimodal deep feature learning.The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring,hidden traffic overlay,and transaction identity forgery.The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer.Consequently,detecting objects is expected to become more relevant and sensitive.A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy(ReliefF_WMRmR)to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior.The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data.According to WMRmR,a feature selection framework was defined to eliminate weakly correlated features,eliminate redundant information,and reduce the detection overhead of the model.A multimodal deep learning model was constructed based on the multilayer perceptron(MLP)to settle the high false alarm rates incurred by multisource data.Using this model,isolated inputs and deep learning were conducted on the selected routing status and traffic behavior.Redundant intermodal information was removed because of the complementarity of the multimodal network,which was followed by feature fusion and output feature representation to boost classification detection precision.The experimental results demonstrate that the proposed method can detect features,such as traffic data,at key link nodes and route messages in a real blockchain network environment.Additionally,the model can detect Erebus attacks effectively.This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%,the recall rate by 2.01%,and the F1-score by 2.43%.