Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Covert LEO Satellite Communication Aided by Generative Adversarial Network Based Cooperative UAV Jamming

In this paper,we study the covert performance of the downlink low earth orbit(LEO)satellite communication,where the unmanned aerial vehicle(UAV)is employed as a cooperative jammer.To maximize the covert rate of the LEO satellite transmission,a multi-objective problem is formulated to jointly optimize the UAV’s jamming power and trajectory.For practical consideration,we assume that the UAV can only have partial environmental information,and can’t know the detection threshold and exact location of the eavesdropper on the ground.To solve the multiobjective problem,we propose the data-driven generative adversarial network(DD-GAN)based method to optimize the power and trajectory of the UAV,in which the sample data is collected by using genetic algorithm(GA).Simulation results show that the jamming solution of UAV generated by DD-GAN can achieve an effective trade-off between covert rate and probability of detection errors when only limited prior information is obtained.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Trust-based co-operative routing for secure communication in mobile ad hoc networks

The working of a Mobile Ad hoc NETwork(MANET)relies on the supportive cooperation among the network nodes.But due to its intrinsic features,a misbehaving node can easily lead to a routing disorder.This paper presents two trust-based routing schemes,namely Trust-based Self-Detection Routing(TSDR)and Trust-based Cooperative Routing(TCOR)designed with an Ad hoc On-demand Distance Vector(AODV)protocol.The proposed work covers a wide range of security challenges,including malicious node identification and prevention,accurate trust quantification,secure trust data sharing,and trusted route maintenance.This brings a prominent solution for mitigating misbehaving nodes and establishing efficient communication in MANET.It is empirically validated based on a performance comparison with the current Evolutionary Self-Cooperative Trust(ESCT)scheme,Generalized Trust Model(GTM),and the conventional AODV protocol.The extensive simulations are conducted against three different varying network scenarios.The results affirm the improved values of eight popular performance metrics overcoming the existing routing schemes.Among the two proposed works,TCOR is more suitable for highly scalable networks;TSDR suits,however,the MANET application better with its small size.This work thus makes a significant contribution to the research community,in contrast to many previous works focusing solely on specific security aspects,and results in a trade-off in the expected values of evaluation parameters and asserts their efficiency.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

Cutting-Edge Challenges in Communication Technology and Computer Network Security

The rapid development of communication technology and computer networks has brought a lot of convenience to production and life,but it also increases the security problem.Information security has become one of the severe challenges faced by people in the digital age.Currently,the security problems facing the field of communication technology and computer networks in China mainly include the evolution of offensive technology,the risk of large-scale data transmission,the potential vulnerabilities introduced by emerging technology,and the dilemma of user identity verification.This paper analyzes the frontier challenges of communication technology and computer network security,and puts forward corresponding solutions,hoping to provide ideas for coping with the security challenges of communication technology and computer networks.

Distribution algorithm of entangled particles for wireless quantum communication mesh networks

With ensured network connectivity in quantum channels, the issue of distributing entangled particles in wireless quantum communication mesh networks can be equivalently regarded as a problem of quantum backbone nodes selection in order to save cost and reduce complexity. A minimum spanning tree（ MST）-based quantum distribution algorithm（ QDMST） is presented to construct the mesh backbone network. First, the articulation points are found,and for each connected block uncovered by the articulation points, the general centers are solved. Then, both articulation points and general centers are classified as backbone nodes and an M ST is formed. The quantum path between every two neighbor nodes on the MST is calculated. The nodes on these paths are also classified as backbone nodes. Simulation results validate the advantages of QDMST in the average backbone nodes number and average quantum channel distance compared to the existing random selection algorithm under multiple network scenarios.

Reform of Urban Spatial Pattern in the Age of Network Communication

Through reviewing different development phases of transportation and communication facilities as well as their profound influence on the evolution of urban spatial pattern,it was disclosed that the development and improvement of information network communication would bring fundamental transformation of urban basic functions which would take effect through the usage of urban land,and finally lead to the spatial pattern reform of the whole city.

Economical quantum secure direct communication network with single photons

In this paper a scheme for quantum secure direct communication （QSDC） network is proposed with a sequence of polarized single photons. The single photons are prepared originally in the same state （0） by the servers on the network, which will reduce the difficulty for the legitimate users to check eavesdropping largely. The users code the information on the single photons with two unitary operations which do not change their measuring bases. Some decoy photons, which are produced by operating the sample photons with a Hadamard, are used for preventing a potentially dishonest server from eavesdropping the quantum lines freely. This scheme is an economical one as it is the easiest way for QSDC network communication securely.

Load Balancing-Based Routing Optimization Mechanism for Power Communication Networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

Joint Computing and Communication Resource Allocation for Satellite Communication Networks with Edge Computing

Benefit from the enhanced onboard processing capacities and high-speed satellite-terrestrial links,satellite edge computing has been regarded as a promising technique to facilitate the execution of the computation-intensive applications for satellite communication networks(SCNs).By deploying edge computing servers in satellite and gateway stations,SCNs can achieve significant performance gains of the computing capacities at the expense of extending the dimensions and complexity of resource management.Therefore,in this paper,we investigate the joint computing and communication resource management problem for SCNs to minimize the execution latency of the computation-intensive applications,while two different satellite edge computing scenarios and local execution are considered.Furthermore,the joint computing and communication resource allocation problem for the computation-intensive services is formulated as a mixed-integer programming problem.A game-theoretic and many-to-one matching theorybased scheme(JCCRA-GM)is proposed to achieve an approximate optimal solution.Numerical results show that the proposed method with low complexity can achieve almost the same weight-sum latency as the Brute-force method.

A Stackelberg Differential Game Based Bandwidth Allocation in Satellite Communication Network

In this paper, a Stackelberg differential game based approach is proposed to solve the bandwidth allocation problems in satellite communication network. All the satellites are divided into two groups, one has high download requirements, and the other one has low download requirements. Each satellites group has its own controller for bandwidth allocation, and can get payments from the satellites for the allocated resources. The relationships between the controllers and satellites are formed as a Stackelberg game. In our model, differential equation is introduced to describe the bandwidth dynamics for the whole satellite communication network. Combine the differential equation and Stackelberg game together, we can formulate the bandwidth allocation problems in satellite communication network as a Stackelber differential game. The solutions to the proposed game is solved based the Bellman dynamic equations. Numerical simulations are given to prove the effeteness and correctness of the proposed approach.

Frequency-Hopping Frequency Reconnaissance and Prediction for Non-cooperative Communication Network

The continuous change of communica-tion frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication net-works.Since the frequency-hopping(FH)sequence is usually generated by a certain model with certain regularity,the FH frequency is thus predictable.In this paper,we investigate the FH frequency reconnais-sance and prediction of a non-cooperative communi-cation network by effective FH signal detection,time-frequency(TF)analysis,wavelet detection and fre-quency estimation.With the intercepted massive FH signal data,long short-term memory(LSTM)neural network model is constructed for FH frequency pre-diction.Simulation results show that our parameter es-timation methods could estimate frequency accurately in the presence of certain noise.Moreover,the LSTM-based scheme can effectively predict FH frequency and frequency interval.

Distributed wireless quantum communication networks with partially entangled pairs

Wireless quantum communication networks transfer quantum state by teleportation. Existing research focuses on maximal entangled pairs. In this paper, we analyse the distributed wireless quantum communication networks with partially entangled pairs. A quantum routing scheme with multi-hop teleportation is proposed. With the proposed scheme, is not necessary for the quantum path to be consistent with the classical path. The quantum path and its associated classical path are established in a distributed way. Direct multi-hop teleportation is conducted on the selected path to transfer a quantum state from the source to the destination. Based on the feature of multi-hop teleportation using partially entangled pairs, if the node number of the quantum path is even, the destination node will add another teleportation at itself. We simulated the performance of distributed wireless quantum communication networks with a partially entangled state. The probability of transferring the quantum state successfully is statistically analyzed. Our work shows that multi-hop teleportation on distributed wireless quantum networks with partially entangled pairs is feasible.

Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years.As in the classical networks,trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues,therefore the basic and importance of trust management in quantum communication networks should be taken more seriously.Compared with other theories and techniques in quantum communication,the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage.In this paper,the core technologies of establishing secure and reliable quantum communication networks are categorized and summarized,and the trends of each direction in trust management of quantum communication network are discussed in depth.

A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks

As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.

Deep Spectrum Prediction in High Frequency Communication Based on Temporal-Spectral Residual Network

High frequency(HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on longterm and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.