Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies...Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.展开更多
Aerial access networks are considered by one of the promising solutions for 6G technology with Unmanned Aerial Vehicles(UAVs)acting as aerial relays to fill coverage gaps,enhance line-of-sight communication,and improv...Aerial access networks are considered by one of the promising solutions for 6G technology with Unmanned Aerial Vehicles(UAVs)acting as aerial relays to fill coverage gaps,enhance line-of-sight communication,and improve network performance and user experience.To improve spectrum utilization,this paper studies a UAV-assisted wireless communication network based on Cognitive Radio(CR)and Non-Orthogonal Multiple Access(NOMA)technologies.Considering the presence of potential malicious eavesdroppers in complex practical communication scenarios,multiple transmitting nodes cooperate through UAVs.To enhance physical layer security performance,an efficient low-complexity transmitter-relay selection scheme is proposed,which selects the optimal transmitting node based on link channel quality,referred to as the Suboptimal Node Transmission Strategy(SNTS).To further enhance system security performance,another scheme is proposed,which selects the optimal transmitting node by maximizing the transmission capacity of the far user,known as the Optimal Node Transmission Strategy(ONTS).Under the decode-and-forward relay strategy,the security performance of the network under both schemes is studied.Under the joint constraints imposed by the primary interference power and secondary maximum transmission power,the secrecy outage probability for users is derived and validated through simulations.The results indicate that under identical conditions,as the number of transmitting nodes increases,the probability of establishing a line-of-sight link in ground-air communications rises,leading to a decrease in the system’s secrecy outage probability.Furthermore,in terms of multi-user diversity gain,ONTS significantly outperforms SNTS.However,the computational overhead of ONTS is relatively high.Therefore,when meeting the actual communication needs of users,a trade-off between complexity and performance must be considered.展开更多
Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resou...Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.展开更多
This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction m...This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.展开更多
Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunatel...Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunately,this crucial process often occurs through unsecured wireless connections,exposing it to numerous cyber-physical attacks.Furthermore,UAVs’limited onboard computing resources make it challenging to perform complex cryptographic operations.The main aim of constructing a cryptographic scheme is to provide substantial security while reducing the computation and communication costs.This article introduces an efficient and secure cross-domain Authenticated Key Agreement(AKA)scheme that uses Hyperelliptic Curve Cryptography(HECC).The HECC,a modified version of ECC with a smaller key size of 80 bits,is well-suited for use in UAVs.In addition,the proposed scheme is employed in a cross-domain environment that integrates a Public Key Infrastructure(PKI)at the receiving end and a Certificateless Cryptosystem(CLC)at the sending end.Integrating CLC with PKI improves network security by restricting the exposure of encryption keys only to the message’s sender and subsequent receiver.A security study employing ROM and ROR models,together with a comparative performance analysis,shows that the proposed scheme outperforms comparable existing schemes in terms of both efficiency and security.展开更多
The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)...The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.展开更多
In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deploym...In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.展开更多
ObjectiveTo present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran.MethodsWe used the registered malaria data to compu...ObjectiveTo present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran.MethodsWe used the registered malaria data to compute the standard incidence rates (SIRs) of malaria in different areas of Sistan and Baluchistan province for a nine-year period (from 2001 to 2009). Statistical analyses consisted of two different parts: geographical mapping of malaria incidence rates, and modeling the environmental factors. The empirical Bayesian estimates of malaria SIRs were utilized for geographical mapping of malaria and a Poisson random effects model was used for assessing the effect of environmental factors on malaria SIRs.ResultsIn general, 64 926 new cases of malaria were registered in Sistan and Baluchistan Province from 2001 to 2009. Among them, 42 695 patients (65.8%) were male and 22 231 patients (34.2%) were female. Modeling the environmental factors showed that malaria incidence rates had positive relationship with humidity, elevation, average minimum temperature and average maximum temperature, while rainfall had negative effect on malaria SIRs in this province.ConclusionsThe results of the present study reveals that malaria is still a serious health problem in Sistan and Baluchistan province, Iran. Geographical map and related environmental factors of malaria can help the health policy makers to intervene in high risk areas more efficiently and allocate the resources in a proper manner.展开更多
Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC...Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.展开更多
This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes...This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes)harvest energy from the environment and use the energy exclusively for transmitting data.The SU nodes(i.e.,relay nodes)on the path,store and forward the received data to the destination node.We consider a real world scenario where the EH-SU node has only local causal knowledge,i.e.,at any time,each EH-SU node only has knowledge of its own EH process,channel state and currently received data.In order to study the power and routing issues,an optimization problem that maximizes path throughput considering quality of service(QoS)and available energy constraints is proposed.To solve this optimization problem,we propose a hybrid game theory routing and power control algorithm(HGRPC).The EH-SU nodes on the same path cooperate with each other,but EH-SU nodes on the different paths compete with each other.By selecting the best next hop node,we find the best strategy that can maximize throughput.In addition,we have established four steps to achieve routing,i.e.,route discovery,route selection,route reply,and route maintenance.Compared with the direct transmission,HGRPC has advantages in longer distances and higher hop counts.The algorithm generates more energy,reduces energy consumption and increases predictable residual energy.In particular,the time complexity of HGRPC is analyzed and its convergence is proved.In simulation experiments,the performance(i.e.,throughput and bit error rate(BER))of HGRPC is evaluated.Finally,experimental results show that HGRPC has higher throughput,longer network life,less latency,and lower energy consumption.展开更多
In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after t...In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.展开更多
Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, cluster...Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.展开更多
One of the most popular standards for protecting confidential information is the Data Encryption Standard (DES). Although it has been replaced by the Advanced Encryption Standard (AES), it is still widely used in Auto...One of the most popular standards for protecting confidential information is the Data Encryption Standard (DES). Although it has been replaced by the Advanced Encryption Standard (AES), it is still widely used in Automatic Teller Machines (ATM’s), smartcards, and mobile phone SIM cards. In this paper, we present area-efficient and high-throughput FPGA implementations of the DES which are developed using the Xilinx FPGA ISE design suite. In fact, we propose modifications on the fastest DES design reported in the literature and achieve 1.1 times higher speed. Also, we introduce an 8-stage pipelined design that needs only 0.75 times the registers and consumes 0.65 times the power of a similar 16-stages pipelined design. High-speed design and synthesis optimization techniques including pipelining, register retiming, and logic replication are used. Post- layout synthesis results show that the proposed implementations achieve high throughput-to-area ratio. To make a fair comparison, the proposed designs were synthesized using matching FPGA devices as being used by other implementations reported in the literature.展开更多
Wireless sensor networks had become a hot research topic in Information science because of their ability to collect and process target information periodically in a harsh or remote environment. However, wireless senso...Wireless sensor networks had become a hot research topic in Information science because of their ability to collect and process target information periodically in a harsh or remote environment. However, wireless sensor networks were inherently limited in various software and hardware resources, especially the lack of energy resources, which is the biggest bottleneck restricting their further development. A large amount of research had been conducted to implement various optimization techniques for the problem of data transmission path selection in homogeneous wireless sensor networks. However, there is still great room for improvement in the optimization of data transmission path selection in heterogeneous wireless sensor networks (HWSNs). This paper proposes a data transmission path selection (HDQNs) protocol based on Deep reinforcement learning. In order to solve the energy consumption balance problem of heterogeneous nodes in the data transmission path selection process of HWSNs and shorten the communication distance from nodes to convergence, the protocol proposes a data collection algorithm based on Deep reinforcement learning DQN. The algorithm uses energy heterogeneous super nodes as AGent to take a series of actions against different states of HWSNs and obtain corresponding rewards to find the best data collection route. Simulation analysis shows that the HDQN protocol outperforms mainstream HWSN data transmission path selection protocols such as DEEC and SEP in key performance indicators such as overall energy efficiency, network lifetime, and system robustness.展开更多
Based on the structures of the short preamble and long preamble, which are defined for synchronization in IEEES02.16e specification, the robust synchronization algorithm for IEEES02.16e OFDM system is proposed. The co...Based on the structures of the short preamble and long preamble, which are defined for synchronization in IEEES02.16e specification, the robust synchronization algorithm for IEEES02.16e OFDM system is proposed. The correlations among the sample sequences in the preamble are investigated, especially the correlation between the first sample sequence and the last sample sequence in the long preamble. The conventional joint timing and frequency synchronization algorithm is reviewed based on the short preamble referring to the algorithm proposed by Schmidl, then a robust joint timing and frequency synchronization algorithm is proposed based on the long preamble. The simulations in the multi-path and frequency selective fading channel show that the proposed algorithm has improved the performances of timing metric plateau, timing offset and synchronization acquisition time even when signal-to-noise ratio is less than -5 dB.展开更多
Objective: To identify the relation between nasopharyngeal carcinoma and the human novel gene UBAP1, which is located in the region of minimal heterozygosity deletion at 9pl3.2 and down-expressed in NPC. Methods: Fiv...Objective: To identify the relation between nasopharyngeal carcinoma and the human novel gene UBAP1, which is located in the region of minimal heterozygosity deletion at 9pl3.2 and down-expressed in NPC. Methods: Five single nucleotide polymorphisms (SNPs) within UBAP1 gene were analysed by sequencing in 105 NPC patients and 183 control subjects which matched to the NPC cases on age, sex and residence. Results: Significant association was found between NPC with one SNP mark (rs1049557), which is located at 3' non-region of UBAP1 gene; the relative risk of this SNP mark is 1.64 (genotype GG) and 1.31 (genotype CG). Conclusion: The result has proved again that UBAP1 gene may play a certain role in the occurrence and development of nasopharyngeal carcinoma. The SNP mark rs1049557, considering its location, may influence the expression of UBAP1 gene.展开更多
The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency a...The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency and power consumption over the conventional wireless NoC. Therefore, this paper studies how to optimize the network performance and power consumption by giving the packet-switching fabric and traffic pattern of each application. Compared with the conventional method of wire-linked, which adds wireless transceivers by using the genetic algorithm （GA）, the proposed maximal declining sorting algorithm （MDSA） can effectively reduce time consumption by as much as 20.4% to 35.6%. We also evaluate the power consumption and configuration time to prove the effective of the proposed algorithm.展开更多
Spectrum resources are the precious and limited natural resources.In order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlin...Spectrum resources are the precious and limited natural resources.In order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G networks.The optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the CRN-NOMA.Under the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission power.First,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization solution.Secondly,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to solve.At the same time,the convergence and time complexity of the algorithm are verified.Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput.In terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.展开更多
In this article, a routing protocol EARP (Energy Aware Routing Protocol) with the terminal node is proposed, to deal with the impact of the limited energy resources of Cognitive Radio Networks on the whole network rou...In this article, a routing protocol EARP (Energy Aware Routing Protocol) with the terminal node is proposed, to deal with the impact of the limited energy resources of Cognitive Radio Networks on the whole network routing. The protocol allows choosing the route from the neighbor nodes in different transmission paths, according to energy consumption of a single node and the full path. If the path breaks, the protocol will increase local routing maintenance strategy. It effectively reduces the retransmission caused by the situation, and improves the routing efficiency. It also can prevent the link transmission process selecting the fault route due to the energy depletion. Through simulation experiments compared with the LEACH (Low Energy Adaptive Clustering Hierarchy) routing protocol, the results showed that in the same experimental environment, the proposed EARP could obviously balance the load, protect low energy nodes, prolong the network survival time and reduce packet loss rate and packet delay of data delivery. So it can improve the energy consumption of sensing node and provide routing capabilities.展开更多
文摘Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies.
基金supported by the National Natural Science Foundation of China(Nos.62461032 and 62001359)the Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University,Chinathe Key Research and Development Project of Lanzhou Jiaotong University,China(No.ZDYF2304).
文摘Aerial access networks are considered by one of the promising solutions for 6G technology with Unmanned Aerial Vehicles(UAVs)acting as aerial relays to fill coverage gaps,enhance line-of-sight communication,and improve network performance and user experience.To improve spectrum utilization,this paper studies a UAV-assisted wireless communication network based on Cognitive Radio(CR)and Non-Orthogonal Multiple Access(NOMA)technologies.Considering the presence of potential malicious eavesdroppers in complex practical communication scenarios,multiple transmitting nodes cooperate through UAVs.To enhance physical layer security performance,an efficient low-complexity transmitter-relay selection scheme is proposed,which selects the optimal transmitting node based on link channel quality,referred to as the Suboptimal Node Transmission Strategy(SNTS).To further enhance system security performance,another scheme is proposed,which selects the optimal transmitting node by maximizing the transmission capacity of the far user,known as the Optimal Node Transmission Strategy(ONTS).Under the decode-and-forward relay strategy,the security performance of the network under both schemes is studied.Under the joint constraints imposed by the primary interference power and secondary maximum transmission power,the secrecy outage probability for users is derived and validated through simulations.The results indicate that under identical conditions,as the number of transmitting nodes increases,the probability of establishing a line-of-sight link in ground-air communications rises,leading to a decrease in the system’s secrecy outage probability.Furthermore,in terms of multi-user diversity gain,ONTS significantly outperforms SNTS.However,the computational overhead of ONTS is relatively high.Therefore,when meeting the actual communication needs of users,a trade-off between complexity and performance must be considered.
文摘Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.
基金funded by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU241683].
文摘This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.
文摘Unmanned Aerial Vehicles(UAVs)are increasingly recognized for their pivotal role in military and civilian applications,serving as essential technology for transmitting,evaluating,and gathering information.Unfortunately,this crucial process often occurs through unsecured wireless connections,exposing it to numerous cyber-physical attacks.Furthermore,UAVs’limited onboard computing resources make it challenging to perform complex cryptographic operations.The main aim of constructing a cryptographic scheme is to provide substantial security while reducing the computation and communication costs.This article introduces an efficient and secure cross-domain Authenticated Key Agreement(AKA)scheme that uses Hyperelliptic Curve Cryptography(HECC).The HECC,a modified version of ECC with a smaller key size of 80 bits,is well-suited for use in UAVs.In addition,the proposed scheme is employed in a cross-domain environment that integrates a Public Key Infrastructure(PKI)at the receiving end and a Certificateless Cryptosystem(CLC)at the sending end.Integrating CLC with PKI improves network security by restricting the exposure of encryption keys only to the message’s sender and subsequent receiver.A security study employing ROM and ROR models,together with a comparative performance analysis,shows that the proposed scheme outperforms comparable existing schemes in terms of both efficiency and security.
文摘The next-generation RAN,known as Open Radio Access Network(ORAN),allows for several advantages,including cost-effectiveness,network flexibility,and interoperability.Now ORAN applications,utilising machine learning(ML)and artificial intelligence(AI)techniques,have become standard practice.The need for Federated Learning(FL)for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques.However,the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference.Therefore,this research presents a novel blockchain-assisted improved cryptographic privacy-preserving federated learning(BICPPFL)model,with the help of ORAN,to safely carry out federated learning and protect privacy.This model improves on the conventional masking technique for sharing model parameters by adding new characteristics.These features include the choice of distributed aggregators,validation for final model aggregation,and individual validation for BSs.To manage the security and privacy of FL processes,a combined homomorphic proxy-reencryption(HPReE)and lattice-cryptographic method(HPReEL)has been used.The upgraded delegated proof of stake(Up-DPoS)consensus protocol,which will provide quick validation of model exchanges and protect against malicious attacks,is employed for effective consensus across blockchain nodes.Without sacrificing performance metrics,the BICPPFL model strengthens privacy and adds security layers while facilitating the transfer of sensitive data across several BSs.The framework is deployed on top of a Hyperledger Fabric blockchain to evaluate its effectiveness.The experimental findings prove the reliability and privacy-preserving capability of the BICPPFL model.
文摘In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of personnel.Most studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind spots.However,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic environments.Nevertheless,research in this area still needs to be conducted.This paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this problem.This algorithm considers the dynamic alterations in obstacle locations within the designated area.It determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage rates.The experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle positions.Experimental results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles change.With 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.
文摘ObjectiveTo present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran.MethodsWe used the registered malaria data to compute the standard incidence rates (SIRs) of malaria in different areas of Sistan and Baluchistan province for a nine-year period (from 2001 to 2009). Statistical analyses consisted of two different parts: geographical mapping of malaria incidence rates, and modeling the environmental factors. The empirical Bayesian estimates of malaria SIRs were utilized for geographical mapping of malaria and a Poisson random effects model was used for assessing the effect of environmental factors on malaria SIRs.ResultsIn general, 64 926 new cases of malaria were registered in Sistan and Baluchistan Province from 2001 to 2009. Among them, 42 695 patients (65.8%) were male and 22 231 patients (34.2%) were female. Modeling the environmental factors showed that malaria incidence rates had positive relationship with humidity, elevation, average minimum temperature and average maximum temperature, while rainfall had negative effect on malaria SIRs in this province.ConclusionsThe results of the present study reveals that malaria is still a serious health problem in Sistan and Baluchistan province, Iran. Geographical map and related environmental factors of malaria can help the health policy makers to intervene in high risk areas more efficiently and allocate the resources in a proper manner.
基金supported by the National Natural Science Foundation of China(6104000561001126+5 种基金61271262)the China Postdoctoral Science Foundation Funded Project(201104916382012T50789)the Natural Science Foundation of Shannxi Province of China(2011JQ8036)the Special Fund for Basic Scientific Research of Central Colleges (CHD2012ZD005)the Research Fund of Zhejiang University of Technology(20100244)
文摘Cooperative communication can achieve spatial diversity gains,and consequently combats signal fading due to multipath propagation in wireless networks powerfully.A novel complex field network-coded cooperation（CFNCC） scheme based on multi-user detection for the multiple unicast transmission is proposed.Theoretic analysis and simulation results demonstrate that,compared with the conventional cooperation（CC） scheme and network-coded cooperation（NCC） scheme,CFNCC would obtain higher network throughput and consumes less time slots.Moreover,a further investigation is made for the symbol error probability（SEP） performance of CFNCC scheme,and SEPs of CFNCC scheme are compared with those of NCC scheme in various scenarios for different signal to noise ratio（SNR） values.
基金This work was partially supported by the National Natural Science Foundation of China(No.61771410,No.61876089)by the Postgraduate Innovation Fund Project by Southwest University of Science and Technology(No.19ycx0106)+2 种基金by the Artificial Intelligence Key Laboratory of Sichuan Province(No.2017RYY05,No.2018RYJ03)by the Zigong City Key Science and Technology Plan Project(2019YYJC16)by and by the Horizontal Project(No.HX2017134,No.HX2018264,Nos.E10203788,HX2019250).
文摘This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes)harvest energy from the environment and use the energy exclusively for transmitting data.The SU nodes(i.e.,relay nodes)on the path,store and forward the received data to the destination node.We consider a real world scenario where the EH-SU node has only local causal knowledge,i.e.,at any time,each EH-SU node only has knowledge of its own EH process,channel state and currently received data.In order to study the power and routing issues,an optimization problem that maximizes path throughput considering quality of service(QoS)and available energy constraints is proposed.To solve this optimization problem,we propose a hybrid game theory routing and power control algorithm(HGRPC).The EH-SU nodes on the same path cooperate with each other,but EH-SU nodes on the different paths compete with each other.By selecting the best next hop node,we find the best strategy that can maximize throughput.In addition,we have established four steps to achieve routing,i.e.,route discovery,route selection,route reply,and route maintenance.Compared with the direct transmission,HGRPC has advantages in longer distances and higher hop counts.The algorithm generates more energy,reduces energy consumption and increases predictable residual energy.In particular,the time complexity of HGRPC is analyzed and its convergence is proved.In simulation experiments,the performance(i.e.,throughput and bit error rate(BER))of HGRPC is evaluated.Finally,experimental results show that HGRPC has higher throughput,longer network life,less latency,and lower energy consumption.
文摘In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.
文摘Energy efficiency and sensing coverage are essential metrics for enhancing the lifetime and the utilization of wireless sensor networks. Many protocols have been developed to address these issues, among which, clustering is considered a key technique in minimizing the consumed energy. However, few clustering protocols address the sensing coverage metric. This paper proposes a general framework that addresses both metrics for clustering algorithms in wireless sensor networks. The proposed framework is based on applying the principles of Virtual Field Force on each cluster within the network in order to move the sensor nodes towards proper locations that maximize the sensing coverage and minimize the transmitted energy. Two types of virtual forces are used: an attractive force that moves the nodes towards the cluster head in order to reduce the energy used for communication and a repulsive force that moves the overlapping nodes away from each other such that their sensing coverage is maximized. The performance of the proposed mechanism was evaluated by applying it to the well-known LEACH clustering algorithm. The simulation results demonstrate that the proposed mechanism improves the performance of the LEACH protocol considerably in terms of the achieved sensing coverage, and the network lifetime.
文摘One of the most popular standards for protecting confidential information is the Data Encryption Standard (DES). Although it has been replaced by the Advanced Encryption Standard (AES), it is still widely used in Automatic Teller Machines (ATM’s), smartcards, and mobile phone SIM cards. In this paper, we present area-efficient and high-throughput FPGA implementations of the DES which are developed using the Xilinx FPGA ISE design suite. In fact, we propose modifications on the fastest DES design reported in the literature and achieve 1.1 times higher speed. Also, we introduce an 8-stage pipelined design that needs only 0.75 times the registers and consumes 0.65 times the power of a similar 16-stages pipelined design. High-speed design and synthesis optimization techniques including pipelining, register retiming, and logic replication are used. Post- layout synthesis results show that the proposed implementations achieve high throughput-to-area ratio. To make a fair comparison, the proposed designs were synthesized using matching FPGA devices as being used by other implementations reported in the literature.
文摘Wireless sensor networks had become a hot research topic in Information science because of their ability to collect and process target information periodically in a harsh or remote environment. However, wireless sensor networks were inherently limited in various software and hardware resources, especially the lack of energy resources, which is the biggest bottleneck restricting their further development. A large amount of research had been conducted to implement various optimization techniques for the problem of data transmission path selection in homogeneous wireless sensor networks. However, there is still great room for improvement in the optimization of data transmission path selection in heterogeneous wireless sensor networks (HWSNs). This paper proposes a data transmission path selection (HDQNs) protocol based on Deep reinforcement learning. In order to solve the energy consumption balance problem of heterogeneous nodes in the data transmission path selection process of HWSNs and shorten the communication distance from nodes to convergence, the protocol proposes a data collection algorithm based on Deep reinforcement learning DQN. The algorithm uses energy heterogeneous super nodes as AGent to take a series of actions against different states of HWSNs and obtain corresponding rewards to find the best data collection route. Simulation analysis shows that the HDQN protocol outperforms mainstream HWSN data transmission path selection protocols such as DEEC and SEP in key performance indicators such as overall energy efficiency, network lifetime, and system robustness.
文摘Based on the structures of the short preamble and long preamble, which are defined for synchronization in IEEES02.16e specification, the robust synchronization algorithm for IEEES02.16e OFDM system is proposed. The correlations among the sample sequences in the preamble are investigated, especially the correlation between the first sample sequence and the last sample sequence in the long preamble. The conventional joint timing and frequency synchronization algorithm is reviewed based on the short preamble referring to the algorithm proposed by Schmidl, then a robust joint timing and frequency synchronization algorithm is proposed based on the long preamble. The simulations in the multi-path and frequency selective fading channel show that the proposed algorithm has improved the performances of timing metric plateau, timing offset and synchronization acquisition time even when signal-to-noise ratio is less than -5 dB.
基金This work was supported by a grant from the National 63High-Tech Program of China (No. 2001AA221031) and a grant from the National Natural Science Foundation of China (No.30100027). *Author to whom correspondence should be addressed.
文摘Objective: To identify the relation between nasopharyngeal carcinoma and the human novel gene UBAP1, which is located in the region of minimal heterozygosity deletion at 9pl3.2 and down-expressed in NPC. Methods: Five single nucleotide polymorphisms (SNPs) within UBAP1 gene were analysed by sequencing in 105 NPC patients and 183 control subjects which matched to the NPC cases on age, sex and residence. Results: Significant association was found between NPC with one SNP mark (rs1049557), which is located at 3' non-region of UBAP1 gene; the relative risk of this SNP mark is 1.64 (genotype GG) and 1.31 (genotype CG). Conclusion: The result has proved again that UBAP1 gene may play a certain role in the occurrence and development of nasopharyngeal carcinoma. The SNP mark rs1049557, considering its location, may influence the expression of UBAP1 gene.
基金supported by the National Natural Science Foundation of China under Grant No.61376024 and No.61306024the Natural Science Foundation of Guangdong Province under Grant No.S2013040014366Basic Research Program of Shenzhen No.JCYJ20140417113430642 and JCYJ20140901003939020
文摘The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency and power consumption over the conventional wireless NoC. Therefore, this paper studies how to optimize the network performance and power consumption by giving the packet-switching fabric and traffic pattern of each application. Compared with the conventional method of wire-linked, which adds wireless transceivers by using the genetic algorithm （GA）, the proposed maximal declining sorting algorithm （MDSA） can effectively reduce time consumption by as much as 20.4% to 35.6%. We also evaluate the power consumption and configuration time to prove the effective of the proposed algorithm.
基金This work was partially supported by the National Natural Science Foundation of China(Nos.61876089,61771410)by the Talent Introduction Project of Sichuan University of Science&Engineering(No.2020RC22)+2 种基金by the Zigong City Key Science and Technology Program(No.2019YYJC16)by the Enterprise Informatization and Internet of Things Measurement and Control Technology Sichuan Provincial Key Laboratory of universities(Nos.2020WZJ02,2014WYJ08)by Artificial Intelligence Key Laboratory of Sichuan Province(No.2015RYJ04).
文摘Spectrum resources are the precious and limited natural resources.In order to improve the utilization of spectrum resources and maximize the network throughput,this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonalmultiple access(CRN-NOMA).NOMA,as the key technology of the fifth-generation communication(5G),can effectively increase the capacity of 5G networks.The optimization problem proposed in this paper aims to maximize the number of secondary users(SUs)accessing the system and the total throughput in the CRN-NOMA.Under the constraints of total power,minimum rate,interference and SINR,CRN-NOMA throughput is maximized by allocating optimal transmission power.First,for the situation of multiple sub-users,an adaptive optimization method is proposed to reduce the complexity of the optimization solution.Secondly,for the optimization problem of nonlinear programming,a maximization throughput optimization algorithm based on Chebyshev and convex(MTCC)for CRN-NOMA is proposed,which converts multi-objective optimization problem into single-objective optimization problem to solve.At the same time,the convergence and time complexity of the algorithm are verified.Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput.In terms of interference and throughput,the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access(OFDMA).This paper provides important insights for the research and application of NOMA in future communications.
文摘In this article, a routing protocol EARP (Energy Aware Routing Protocol) with the terminal node is proposed, to deal with the impact of the limited energy resources of Cognitive Radio Networks on the whole network routing. The protocol allows choosing the route from the neighbor nodes in different transmission paths, according to energy consumption of a single node and the full path. If the path breaks, the protocol will increase local routing maintenance strategy. It effectively reduces the retransmission caused by the situation, and improves the routing efficiency. It also can prevent the link transmission process selecting the fault route due to the energy depletion. Through simulation experiments compared with the LEACH (Low Energy Adaptive Clustering Hierarchy) routing protocol, the results showed that in the same experimental environment, the proposed EARP could obviously balance the load, protect low energy nodes, prolong the network survival time and reduce packet loss rate and packet delay of data delivery. So it can improve the energy consumption of sensing node and provide routing capabilities.
