Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be dep...Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.展开更多
A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation...A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.展开更多
Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal ...Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.展开更多
Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Ne...Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.展开更多
Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is sti...Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.展开更多
The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell cove...The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell coverage very frequently,for example,may lead to service disruptions if a predictive approach is not deployed in the system.Although several works examined mobility prediction in the new-generation mobile networks,all of these studies focused on studying the time features of mobility traces,and the spectral content of historical mobility patterns was not considered for prediction purposes as yet.In the present study,we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency.The proposed approach lies in the mobility analysis in the frequency domain,to extract hidden features of the mobility process.Thus,we proposed a new methodology to determine the spectral content of mobility traces(considered as signals)and,thus,the appropriate sampling frequency,which can provide numerous advantages.We considered several types of mobility models(e.g.pedestrian,urban,and vehicular),containing important details in the time and frequency domains.Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.展开更多
Nowadays,high mobility scenarios have become increasingly common.The widespread adoption of High-speed Rail(HSR)in China exemplifies this trend,while more promising use cases,such as vehicle-to-everything,continue to ...Nowadays,high mobility scenarios have become increasingly common.The widespread adoption of High-speed Rail(HSR)in China exemplifies this trend,while more promising use cases,such as vehicle-to-everything,continue to emerge.However,the Internet access provided in high mobility environments stllstruggles to achieve seamless connectivity.The next generation of wireless cellular technology 5 G further poses more requirements on the endto-end evolution to fully utilize its ultra-high band-width,while existing network diagnostic tools focus on above-IP layers or below-IP layers only.We then propose HiMoDiag,which enables flexible online analysis of the network performance in a cross-layer manner,i.e.,from the top(application layer)to the bottom(physical layer).We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR,lead to more timely optimization and ultimately help to improve the network performance.展开更多
Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a st...Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.展开更多
A QoS routing protocol based on mobility prediction is proposed. The protocol selects the steadiest path based on mobility prediction and QoS requirements on bandwidth, delay, and so forth. The main properties of the ...A QoS routing protocol based on mobility prediction is proposed. The protocol selects the steadiest path based on mobility prediction and QoS requirements on bandwidth, delay, and so forth. The main properties of the protocol as follows. (1) Each node just broadcasts its own information, so the packet is very small and can get to the destination in a very short time. (2) When another path is built for the same QoS requirements, the original path has higher priority. (3) The update messages are reduced by using mobility prediction. (4) Data packets carry the information of link change using piggyback, which is helpful for forecasting the link status more accurately. (5) When source node gets Resource Reserve and reconnect packets at the same time, it selects reconnect packet over Resource Reserve packet. The results of simulation show that the protocol has good network performance with low control overload, and efficiently supports transmitting multimedia with QoS requirements in mobile ad hoc networks.展开更多
In this paper, we study the dynamical behaviour of an epidemic on complex networks with population mobility. In our model, the number of people on each node is unrestricted as the nodes of the network are considered a...In this paper, we study the dynamical behaviour of an epidemic on complex networks with population mobility. In our model, the number of people on each node is unrestricted as the nodes of the network are considered as cities, communities, and so on. Because people can travel between different cities, we study the effect of a population's mobility on the epidemic spreading. In view of the population's mobility, we suppose that the susceptible individual can be infected by an infected individual in the same city or other connected cities. Simulations are presented to verify our analysis.展开更多
In coded caching,users cache pieces of files under a specific arrangement so that the server can satisfy their requests simultaneously in the broadcast scenario via e Xclusive OR(XOR)operation and therefore reduce the...In coded caching,users cache pieces of files under a specific arrangement so that the server can satisfy their requests simultaneously in the broadcast scenario via e Xclusive OR(XOR)operation and therefore reduce the amount of transmission data.However,when users’locations are changing,the uploading of caching information is frequent and extensive that the traffic increase outweighed the traffic reduction that the traditional coded caching achieved.In this paper,we propose mobile coded caching schemes to reduce network traffic in mobility scenarios,which achieve a lower cost on caching information uploading.In the cache placement phase,the proposed scheme first constructs caching patterns,and then assigns the caching patterns to users according to the graph coloring method and four color theorem in our centralized cache placement algorithm or randomly in our decentralized cache placement algorithm.Then users are divided into groups based on their caching patterns.As a benefit,when user movements occur,the types of caching pattern,rather than the whole caching information of which file pieces are cached,are uploaded.In the content delivery phase,XOR coded caching messages are reconstructed.Transmission data volume is derived to measure the performance of the proposed schemes.Numerical results show that the proposed schemes achieve great improvement in traffic offloading.展开更多
The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range comm...The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.展开更多
In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are...In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.展开更多
A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combin...A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.展开更多
In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In...In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and self-administration.Clustering in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and analytics.We present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations.Based on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each hub.Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.展开更多
For wireless ad hoc networks simulation, node's mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle en...For wireless ad hoc networks simulation, node's mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node's moving path. Obstacles also affect node's signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. Simulation results show that the model has a significant impact on the performance of protocols.展开更多
Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient co...Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.展开更多
Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges ...Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.展开更多
Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interc...Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.展开更多
Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in thefield of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles th...Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in thefield of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate precision.For most applications,a frame-work must ensure that the retrieved data reflects the transmitted data.Before driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be repaired.Most link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it down.In other words,because of asset information must be secured from threats,information is a valu-able resource.In MANETs,some applications necessitate the use of a network method for detecting and blocking these assaults.Building a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difficulty in MANET.Attacks on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention engine.By bringing the Secure Intrusion Detection System(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be developed.The Secure Energy Routing(SER)protocol for MANETs is introduced in this study.The protocol addresses the issue of network security by detecting and preventing attacks in the network.The data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of security.Network Simulator–2 is used to simulate the network and experiments are compared with existing methods.展开更多
基金supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065)the Domestic Visiting Program of Jiangxi Normal University.
文摘Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62073172)the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20221329)。
文摘A dynamical model is constructed to depict the spatial-temporal evolution of malware in mobile wireless sensor networks(MWSNs). Based on such a model, we design a hybrid control scheme combining parameter perturbation and state feedback to effectively manipulate the spatiotemporal dynamics of malware propagation. The hybrid control can not only suppress the Turing instability caused by diffusion factor but can also adjust the occurrence of Hopf bifurcation induced by time delay. Numerical simulation results show that the hybrid control strategy can efficiently manipulate the transmission dynamics to achieve our expected desired properties, thus reducing the harm of malware propagation to MWSNs.
基金supported by National Key Research and Development Program of China(2018YFC1504502).
文摘Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.
基金the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2024-1008.
文摘Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.
文摘Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.
基金supported by the Czech Ministry of Education,Youth and Sports under project Reg.No.SP2021/25partially from the project“e-Infrastructure CZ”Reg.No.LM2018140.
文摘The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell coverage very frequently,for example,may lead to service disruptions if a predictive approach is not deployed in the system.Although several works examined mobility prediction in the new-generation mobile networks,all of these studies focused on studying the time features of mobility traces,and the spectral content of historical mobility patterns was not considered for prediction purposes as yet.In the present study,we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency.The proposed approach lies in the mobility analysis in the frequency domain,to extract hidden features of the mobility process.Thus,we proposed a new methodology to determine the spectral content of mobility traces(considered as signals)and,thus,the appropriate sampling frequency,which can provide numerous advantages.We considered several types of mobility models(e.g.pedestrian,urban,and vehicular),containing important details in the time and frequency domains.Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.
基金supported by National Key Research and Development Plan,China(Grant No.2020YFB1710900)National Natural Science Foundation of China(Grant No.62022005 and 62172008).
文摘Nowadays,high mobility scenarios have become increasingly common.The widespread adoption of High-speed Rail(HSR)in China exemplifies this trend,while more promising use cases,such as vehicle-to-everything,continue to emerge.However,the Internet access provided in high mobility environments stllstruggles to achieve seamless connectivity.The next generation of wireless cellular technology 5 G further poses more requirements on the endto-end evolution to fully utilize its ultra-high band-width,while existing network diagnostic tools focus on above-IP layers or below-IP layers only.We then propose HiMoDiag,which enables flexible online analysis of the network performance in a cross-layer manner,i.e.,from the top(application layer)to the bottom(physical layer).We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR,lead to more timely optimization and ultimately help to improve the network performance.
文摘Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.
基金TheNationalHighTechnologyDevelopment"863"Program(No.2 0 0 1AA112051),TheNationalScienceFundforOverseasDistinguishedYoungScholars (No .6992 82 0 1)
文摘A QoS routing protocol based on mobility prediction is proposed. The protocol selects the steadiest path based on mobility prediction and QoS requirements on bandwidth, delay, and so forth. The main properties of the protocol as follows. (1) Each node just broadcasts its own information, so the packet is very small and can get to the destination in a very short time. (2) When another path is built for the same QoS requirements, the original path has higher priority. (3) The update messages are reduced by using mobility prediction. (4) Data packets carry the information of link change using piggyback, which is helpful for forecasting the link status more accurately. (5) When source node gets Resource Reserve and reconnect packets at the same time, it selects reconnect packet over Resource Reserve packet. The results of simulation show that the protocol has good network performance with low control overload, and efficiently supports transmitting multimedia with QoS requirements in mobile ad hoc networks.
基金supported by National Natural Science Foundation of China (Grant Nos 60744003,10635040,10532060 and 10672146)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No 20060358065)+2 种基金National Science Fund for Fostering Talents in Basic Science (Grant No J0630319)A grant from the Health,Welfare and Food Bureau of the Hong Kong SAR GovernmentShanghai Leading Academic Discipline Project (Project Number:J50101)
文摘In this paper, we study the dynamical behaviour of an epidemic on complex networks with population mobility. In our model, the number of people on each node is unrestricted as the nodes of the network are considered as cities, communities, and so on. Because people can travel between different cities, we study the effect of a population's mobility on the epidemic spreading. In view of the population's mobility, we suppose that the susceptible individual can be infected by an infected individual in the same city or other connected cities. Simulations are presented to verify our analysis.
基金supported by National Natural Science Foundation of China(No.61971060)。
文摘In coded caching,users cache pieces of files under a specific arrangement so that the server can satisfy their requests simultaneously in the broadcast scenario via e Xclusive OR(XOR)operation and therefore reduce the amount of transmission data.However,when users’locations are changing,the uploading of caching information is frequent and extensive that the traffic increase outweighed the traffic reduction that the traditional coded caching achieved.In this paper,we propose mobile coded caching schemes to reduce network traffic in mobility scenarios,which achieve a lower cost on caching information uploading.In the cache placement phase,the proposed scheme first constructs caching patterns,and then assigns the caching patterns to users according to the graph coloring method and four color theorem in our centralized cache placement algorithm or randomly in our decentralized cache placement algorithm.Then users are divided into groups based on their caching patterns.As a benefit,when user movements occur,the types of caching pattern,rather than the whole caching information of which file pieces are cached,are uploaded.In the content delivery phase,XOR coded caching messages are reconstructed.Transmission data volume is derived to measure the performance of the proposed schemes.Numerical results show that the proposed schemes achieve great improvement in traffic offloading.
文摘The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.
基金supported by the National Natural Science Foundation of China (Grant No. 60776052)
文摘In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AIGaN/CaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of A1GaN/GaN HEMT are more accurate than those obtained from the EEHEMT model.
基金supported by the National Key R&D Program of China (No.2021YFF0901002)the National Natural Science Foundation of China (No.61802291)+1 种基金Fundamental Research Funds for the Provincial Universities of Zhejiang (GK199900299012-025)Fundamental Research Funds for the Central Universities (No.JB210311).
文摘A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.
文摘In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and self-administration.Clustering in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and analytics.We present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations.Based on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each hub.Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.
文摘For wireless ad hoc networks simulation, node's mobility pattern and traffic pattern are two key elements. A new simulation model is presented based on the virtual reality collision detection algorithm in obstacle environment, and the model uses the path planning method to avoid obstacles and to compute the node's moving path. Obstacles also affect node's signal propagation. Considering these factors, this study implements the mobility model for wireless ad hoc networks. Simulation results show that the model has a significant impact on the performance of protocols.
文摘Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.
基金supported by the National Natural Science Foundation of China(61975020,62171053)。
文摘Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.
基金supported by the National Natural Science Foundation of China(No.62201313)the Opening Foundation of Fujian Key Laboratory of Sensing and Computing for Smart Cities(Xiamen University)(No.SCSCKF202101)the Open Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University)(No.MJUKF-IPIC202206).
文摘Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent years.The mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all things.The variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication environments.Ensuring data secure transmission is critical for mobile IIoT networks.This paper investigates the data secure transmission performance prediction of mobile IIoT networks.To cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first derived.Then,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction algorithm.For mobile signals,the important features may be removed by the pooling layers.This will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is designed.Out of the input and output layers,it removes the pooling layer and contains six convolution layers.Elman,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed algorithm.Through simulation analysis,good prediction accuracy is achieved by the CNN algorithm.The prediction accuracy obtains a 59%increase.
文摘Mobile computing is the most powerful application for network com-munication and connectivity,given recent breakthroughs in thefield of wireless networks or Mobile Ad-hoc networks(MANETs).There are several obstacles that effective networks confront and the networks must be able to transport data from one system to another with adequate precision.For most applications,a frame-work must ensure that the retrieved data reflects the transmitted data.Before driv-ing to other nodes,if the frame between the two nodes is deformed in the data-link layer,it must be repaired.Most link-layer protocols immediately disregard the frame and enable the high-layer protocols to transmit it down.In other words,because of asset information must be secured from threats,information is a valu-able resource.In MANETs,some applications necessitate the use of a network method for detecting and blocking these assaults.Building a secure intrusion detection system in the network,which provides security to the nodes and route paths in the network,is a major difficulty in MANET.Attacks on the network can jeopardize security issues discovered by the intrusion detection system engine,which are then blocked by the network’s intrusion prevention engine.By bringing the Secure Intrusion Detection System(S-IDS)into the network,a new technique for implementing security goals and preventing attacks will be developed.The Secure Energy Routing(SER)protocol for MANETs is introduced in this study.The protocol addresses the issue of network security by detecting and preventing attacks in the network.The data transmission in the MANET is forwarded using Elliptical Curve Cryptography(ECC)with an objective to improve the level of security.Network Simulator–2 is used to simulate the network and experiments are compared with existing methods.