We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that prov...We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.展开更多
In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield w...In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.展开更多
With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protecti...With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.展开更多
Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms i...Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms in Zhejiang Province,China,were investigated.A total of 215 isolates were identified as Escherichia coli(64.65%),Klebsiella pneumoniae(12.09%),Proteus mirabilis(10.23%),Salmonella(8.84%),and Enterobacter cloacae(4.19%).Meanwhile,all isolates were resistant to at least two antibiotics.Most isolates carried tet(A)(85.12%),blaTEM(78.60%)and sul1(67.44%)resistance genes.Gene co-occurrence analysis showed that the resistance genes were associated with IS26 and integrons.A conjugative IncFII plasmid pSDM004 containing all the above MGEs was detected in Proteus mirabilis isolate SDM004.This isolate was resistant to 18 antibiotics and carried the blaNDM-5 gene.MGEs,especially plasmids,are the primary antibiotic resistance gene transmission route in duck farms.These findings provide a theoretical basis for the rational use of antibiotics in farms which are substantial for evaluating public health and food safety.展开更多
Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy....Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.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
Thousands of long-distance mobile mRNAs were identified from different grafting systems,based on high-throughput sequencing technology.Moreover,the long-distance delivery of RNAs was proved to involve multiple mechani...Thousands of long-distance mobile mRNAs were identified from different grafting systems,based on high-throughput sequencing technology.Moreover,the long-distance delivery of RNAs was proved to involve multiple mechanisms.Here,we analyzed the homology,motif,and tRNA-like structure(TLS)of long-distance mobile mRNAs identified by RNA-seq as well as the RNA-binding protein(RBP)in nine grafting combinations including Arabidopsis thaliana,Vitis vinifera,Cucumis sativus,Citrullus lanatus,Nicotiana benthamiana,Malus domestica,Pyrus spp.,Glycine max and Phaseolus vulgaris.Although several mRNAs were found to be shared in herbaceous,woody,and related species,the vast majority of long-distance mobile mRNAs were species-specific.Four non-specific movement-related motifs were identified,while the TLS was not necessary for mRNA long distance mobility.In addition,we found that RBPs were conserved among herbaceous and woody plants as well as related species.This paper reports a further in-depth analysis of the endogenous mechanisms by which the species-specific transportable m RNAs were selected by bioinformatics,in order to provide insights for future research on long-distance mobile mRNAs.展开更多
The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-base...The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.展开更多
Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC a...Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.展开更多
In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the ...In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.展开更多
In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of ...In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.展开更多
Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffe...Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffering from“double disadvantage”.So,based on an ecological model of the stress process,this paper tries to use the data of the questionnaire on the mental health of mobile young white-collar workers in Zhejiang Province to explore the influence of some factors in the middle workplace and residence place on the mental health of micro individuals.The results show that:(1)The working environment with high control and low freedom and the workplace discrimination against the mobile status will have a negative impact on the mental health of mobile young white-collar workers;(2)Financial anxiety in daily life will lead to a decline in the mental health level of mobile young white-collar workers;(3)Good organizational support and neighborhood social relations can significantly relieve life pressure,so as to effectively improve the mental health of mobile young white-collar workers.It can be seen that we also need to pay more attention to the mental health of mobile young white-collar workers in order to improve their situation.展开更多
In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is metic...In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.展开更多
Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has beco...Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has become mainstream to utilize mobile devices for English learning among university students’English learning.The current study aims to examine the current situation and influencing factors of university students’English mobile learning.98 university students in one university of Shanghai participated the study and the questionnaire was used to collect the data.The results indicated that most university students already have electronic devices to support mobile learning.Personal factors,environmental factors,digital literacy,and technological capabilities are the main factors affecting university students’English mobile learning.The current study has implications for learners,teachers,and software developers.Learners should adjust their learning motivation,play an active role,and fully utilize the mobile platform to obtain resources and improve learning efficiency.Teachers should incorporate the advantages of mobile teaching and promote categorized and tiered teaching.Software developers should add new functions on the basis of meeting the basic needs of learners and continuously innovate the mobile learning platform.展开更多
The primary objective of emergency pre-examination and triage is to provide the most appropriate clinical service to patients with acute and critical illnesses while ensuring the optimal utilization of limited medical...The primary objective of emergency pre-examination and triage is to provide the most appropriate clinical service to patients with acute and critical illnesses while ensuring the optimal utilization of limited medical resources.With the advancement of medical information technology,mobile technology,and information management,these features have been gradually incorporated into emergency pre-examination and triage and have shown promising outcomes.In this article,the benefits of implementing mobile technology and the current status of integrated information management to provide a reference for the future development and enhancement of emergency pre-examination and triage are reviewed.展开更多
The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks r...The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.展开更多
Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close proximity.The edge servers are not allocated efficiently according to the dynamic nature of the network.It leads to process...Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close proximity.The edge servers are not allocated efficiently according to the dynamic nature of the network.It leads to processing delay,and the tasks are dropped due to time limitations.The researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes.The challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized manner.This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge servers.Initially,a task-offloading problem needs to be formulated based on the communication and pro-cessing.Then offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge services.The significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed idea.The processing time of the anticipated model is 6.6 s.The following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning approaches.The proposed model shows a better trade-off compared to existing approaches.展开更多
Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication n...Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.展开更多
Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applic...Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.展开更多
Recently,a specific interest is being taken in the development of mobile application(app)via Model-Based User Interface Development(MBUID)approach.MBUID allows the generation of mobile apps in the target platform(s)fr...Recently,a specific interest is being taken in the development of mobile application(app)via Model-Based User Interface Development(MBUID)approach.MBUID allows the generation of mobile apps in the target platform(s)from conceptual models.As such it simplified the development process of mobile app.However,the interest is only focused on the functional aspects of the mobile app while neglecting the non-functional aspects,such as usability.The latter is largely considered as the main factor leading to the success or failure of any software system.This paper aims at addressing non-functional aspects of mobile apps generated using MBUID approach.As such,we propose a usability-driven approach for the development of mobile apps.The main stages of the proposed approach are defined in a generic way so that they can be integrated with any MBUID method.A case study is presented,in the paper,with the aim of illustrating the feasibility of this approach.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.62073045)。
文摘We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.
文摘In today’s era, where mobile devices have become an integral part of our daily lives, ensuring the security of mobile applications has become increasingly crucial. Mobile penetration testing, a specialized subfield within the realm of cybersecurity, plays a vital role in safeguarding mobile ecosystems against the ever-evolving landscape of threats. The ubiquity of mobile devices has made them a prime target for cybercriminals, and the data and functionality accessed through mobile applications make them valuable assets to protect. Mobile penetration testing is designed to identify vulnerabilities, weaknesses, and potential exploits within mobile applications and the devices themselves. Unlike traditional penetration testing, which often focuses on network and server security, mobile penetration testing zeroes in on the unique challenges posed by mobile platforms. Mobile penetration testing, a specialized field within cybersecurity, is an essential tool in the Cybersecurity specialists’ toolkit to protect mobile ecosystems from emerging threats. This article introduces mobile penetration testing, emphasizing its significance, including comprehensive learning labs for Android and iOS platforms, and highlighting how it distinctly differs from traditional penetration testing methodologies.
基金Wenzhou Key Scientific and Technological Projects(No.ZG2020031)Wenzhou Polytechnic Research Projects(No.WZY2021002)+3 种基金Key R&D Projects in Zhejiang Province(No.2021C01117)Major Program of Natural Science Foundation of Zhejiang Province(LD22F020002)the Cloud Security Key Technology Research Laboratorythe Researchers Supporting Project Number(RSP2023R509),King Saud University,Riyadh,Saudi Arabia.
文摘With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy information.At present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of practicability.To this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model certification.The experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.
基金supported by the National Natural Science Foundation of China(32172188)Science and Technology Cooperation Project of ZheJiang Province(2023SNJF058-3)。
文摘Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms in Zhejiang Province,China,were investigated.A total of 215 isolates were identified as Escherichia coli(64.65%),Klebsiella pneumoniae(12.09%),Proteus mirabilis(10.23%),Salmonella(8.84%),and Enterobacter cloacae(4.19%).Meanwhile,all isolates were resistant to at least two antibiotics.Most isolates carried tet(A)(85.12%),blaTEM(78.60%)and sul1(67.44%)resistance genes.Gene co-occurrence analysis showed that the resistance genes were associated with IS26 and integrons.A conjugative IncFII plasmid pSDM004 containing all the above MGEs was detected in Proteus mirabilis isolate SDM004.This isolate was resistant to 18 antibiotics and carried the blaNDM-5 gene.MGEs,especially plasmids,are the primary antibiotic resistance gene transmission route in duck farms.These findings provide a theoretical basis for the rational use of antibiotics in farms which are substantial for evaluating public health and food safety.
基金supported by Jilin Provincial Science and Technology Department Natural Science Foundation of China(20210101415JC)Jilin Provincial Science and Technology Department Free exploration research project of China(YDZJ202201ZYTS642).
文摘Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.
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supported by the 111 Project(Grant No.B17043)the 2115 Talent Development Program of China Agricultural University。
文摘Thousands of long-distance mobile mRNAs were identified from different grafting systems,based on high-throughput sequencing technology.Moreover,the long-distance delivery of RNAs was proved to involve multiple mechanisms.Here,we analyzed the homology,motif,and tRNA-like structure(TLS)of long-distance mobile mRNAs identified by RNA-seq as well as the RNA-binding protein(RBP)in nine grafting combinations including Arabidopsis thaliana,Vitis vinifera,Cucumis sativus,Citrullus lanatus,Nicotiana benthamiana,Malus domestica,Pyrus spp.,Glycine max and Phaseolus vulgaris.Although several mRNAs were found to be shared in herbaceous,woody,and related species,the vast majority of long-distance mobile mRNAs were species-specific.Four non-specific movement-related motifs were identified,while the TLS was not necessary for mRNA long distance mobility.In addition,we found that RBPs were conserved among herbaceous and woody plants as well as related species.This paper reports a further in-depth analysis of the endogenous mechanisms by which the species-specific transportable m RNAs were selected by bioinformatics,in order to provide insights for future research on long-distance mobile mRNAs.
基金the China Scholarship Council(202106690037)the Natural Science Foundation of Anhui Province(19080885QE194)。
文摘The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.
基金supported by the Key Research and Development Project in Anhui Province of China(Grant No.202304a05020059)the Fundamental Research Funds for the Central Universities of China(Grant No.PA2023GDSK0055)the Project of Anhui Province Economic and Information Bureau(Grant No.JB20099).
文摘Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.
基金the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)+1 种基金Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2110)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.
基金This work was supported by the Key Scientific and Technological Project of Henan Province(Grant Number 222102210212)Doctoral Research Start Project of Henan Institute of Technology(Grant Number KQ2005)Key Research Projects of Colleges and Universities in Henan Province(Grant Number 23B510006).
文摘In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.
基金the National Social Science Fund of China(Grant No.20BTJ005).
文摘Mobile young white-collar workers not only have the characteristics of mobile young people,but also have the characteristics of general white-collar workers.Under the influence of both,their mental health may be suffering from“double disadvantage”.So,based on an ecological model of the stress process,this paper tries to use the data of the questionnaire on the mental health of mobile young white-collar workers in Zhejiang Province to explore the influence of some factors in the middle workplace and residence place on the mental health of micro individuals.The results show that:(1)The working environment with high control and low freedom and the workplace discrimination against the mobile status will have a negative impact on the mental health of mobile young white-collar workers;(2)Financial anxiety in daily life will lead to a decline in the mental health level of mobile young white-collar workers;(3)Good organizational support and neighborhood social relations can significantly relieve life pressure,so as to effectively improve the mental health of mobile young white-collar workers.It can be seen that we also need to pay more attention to the mental health of mobile young white-collar workers in order to improve their situation.
文摘In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.
文摘Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has become mainstream to utilize mobile devices for English learning among university students’English learning.The current study aims to examine the current situation and influencing factors of university students’English mobile learning.98 university students in one university of Shanghai participated the study and the questionnaire was used to collect the data.The results indicated that most university students already have electronic devices to support mobile learning.Personal factors,environmental factors,digital literacy,and technological capabilities are the main factors affecting university students’English mobile learning.The current study has implications for learners,teachers,and software developers.Learners should adjust their learning motivation,play an active role,and fully utilize the mobile platform to obtain resources and improve learning efficiency.Teachers should incorporate the advantages of mobile teaching and promote categorized and tiered teaching.Software developers should add new functions on the basis of meeting the basic needs of learners and continuously innovate the mobile learning platform.
文摘The primary objective of emergency pre-examination and triage is to provide the most appropriate clinical service to patients with acute and critical illnesses while ensuring the optimal utilization of limited medical resources.With the advancement of medical information technology,mobile technology,and information management,these features have been gradually incorporated into emergency pre-examination and triage and have shown promising outcomes.In this article,the benefits of implementing mobile technology and the current status of integrated information management to provide a reference for the future development and enhancement of emergency pre-examination and triage are reviewed.
文摘The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.
文摘Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close proximity.The edge servers are not allocated efficiently according to the dynamic nature of the network.It leads to processing delay,and the tasks are dropped due to time limitations.The researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge nodes.The challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized manner.This study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge servers.Initially,a task-offloading problem needs to be formulated based on the communication and pro-cessing.Then offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge services.The significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed idea.The processing time of the anticipated model is 6.6 s.The following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning approaches.The proposed model shows a better trade-off compared to existing approaches.
文摘Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks,mobile edge computing,artificial intelligence,and mobile devices.However,due to the constrained data processing capacity of mobile devices,efficient and effective interactive mobile learning is a challenge.Therefore,for mobile learning,we propose a"Cloud,Edge and End"fusion system architecture.Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment.Then,we present the proposed solutions that uses the minimum cost maximum flow(MCMF)algorithm to deal with the offloading problem and the deep Q network(DQN)algorithm to deal with the resource allocation problem respectively.Finally,the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance,save energy,and satisfy the needs of learning users.
基金supported by the Bio and Medical Technology Development Program of the National Research Foundation(NRF)funded by the Korean government(MSIT)(No.NRF-2019M3E5D1A02069073)supported by the Soonchunhyang University Research Fund.
文摘Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under the research project\#2021/01/17815.
文摘Recently,a specific interest is being taken in the development of mobile application(app)via Model-Based User Interface Development(MBUID)approach.MBUID allows the generation of mobile apps in the target platform(s)from conceptual models.As such it simplified the development process of mobile app.However,the interest is only focused on the functional aspects of the mobile app while neglecting the non-functional aspects,such as usability.The latter is largely considered as the main factor leading to the success or failure of any software system.This paper aims at addressing non-functional aspects of mobile apps generated using MBUID approach.As such,we propose a usability-driven approach for the development of mobile apps.The main stages of the proposed approach are defined in a generic way so that they can be integrated with any MBUID method.A case study is presented,in the paper,with the aim of illustrating the feasibility of this approach.
