With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these t...With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.展开更多
Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/f...Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.展开更多
The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC n...The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.展开更多
Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this r...Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.展开更多
Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating ...Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating Software Defined Networks(SDN)for enhancing resource orchestration,task scheduling,and traffic management remains a relatively underexplored area with significant innovation potential.This paper provides a comprehensive review of existing mechanisms,categorizing resource provisioning approaches into static,dynamic,and user-centric models,while examining applications across domains such as IoT,healthcare,and autonomous systems.The survey highlights challenges such as scalability,interoperability,and security in managing dynamic and heterogeneous infrastructures.This exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration processes.Furthermore,proposes future directions,including AI-driven optimization techniques and hybrid orchestrationmodels.By addressing these emerging opportunities,thiswork serves as a foundational reference for advancing resource management strategies in next-generation cloud,fog,and edge computing ecosystems.This survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities.展开更多
The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for...The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.展开更多
Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differen...Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differences in traits and functional diversity between forest edges and interiors,and how the inclusion of intraspecific trait variation affects the assessment of functional diversity in these habitats.We sampled 10 representative forest patches,and,in each patch,we established five plots on the edge and five inside the forest,collecting leaf functional traits,allometric and wood density for all species.We assessed functional diversity using functional richness(FRic),divergence(FDiv),and dispersion(FDis).To assess the impact of incorporating intraspecific variation when comparing trait values and functional diversity indices,we established two scenarios:one that excludes intraspecific variation and another that includes it.We found that the edge and interior harbor individuals with distinct functional traits that alleviate the inherent stress of each habitat.The edge was also found to be more selective in terms of the range of functional traits,resulting in lower functional diversity.Our findings demonstrated that habitats play an important role in intraspecific trait variation(ITV)and that statistically significant differences between habitats,in relation to traits and functional diversity,were better observed with the inclusion of intraspecific variation.Our study highlights the potential of using natural forest patches to understand the edge effect,regardless of habitat loss.Additionally,we emphasize the importance of incorporating ITV into functional diversity studies,especially those on a smaller scale that incorporate quantitative variables,to better understand and predict ecological patterns.展开更多
Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength...Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.展开更多
The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand...The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing’s ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.展开更多
Resource allocation and task scheduling in the Cloud environment faces many challenges,such as time delay,energy consumption,and security.Also,executing computation tasks of mobile applications on mobile devices(MDs)r...Resource allocation and task scheduling in the Cloud environment faces many challenges,such as time delay,energy consumption,and security.Also,executing computation tasks of mobile applications on mobile devices(MDs)requires a lot of resources,so they can offload to the Cloud.But Cloud is far from MDs and has challenges as high delay and power consumption.Edge computing with processing near the Internet of Things(IoT)devices have been able to reduce the delay to some extent,but the problem is distancing itself from the Cloud.The fog computing(FC),with the placement of sensors and Cloud,increase the speed and reduce the energy consumption.Thus,FC is suitable for IoT applications.In this article,we review the resource allocation and task scheduling methods in Cloud,Edge and Fog environments,such as traditional,heuristic,and meta-heuristics.We also categorize the researches related to task offloading in Mobile Cloud Computing(MCC),Mobile Edge Computing(MEC),and Mobile Fog Computing(MFC).Our categorization criteria include the issue,proposed strategy,objectives,framework,and test environment.展开更多
As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although ...As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so far.The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users.Therefore,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet placement.To solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution.After cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility.The performance of CAPABLE has been validated by extensive simulations.展开更多
By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task off...By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.展开更多
A mobile edge cloud provides a platform to accommodate the offloaded traffic workload generated by mobile devices.It can significantly reduce the access delay for mobile application users.However,the high user mobilit...A mobile edge cloud provides a platform to accommodate the offloaded traffic workload generated by mobile devices.It can significantly reduce the access delay for mobile application users.However,the high user mobility brings significant challenges to the service provisioning for mobile users,especially to delay-sensitive mobile applications.With the objective to maximize a profit,which positively associates with the overall admitted traffic served by the local edge cloud,and negatively associates with the access delay as well as virtual machine migration delay,we study a fundamental problem in this paper:how to update the service provisioning solution for a given group of mobile users.Such a profit-maximization problem is formulated as a nonlinear integer linear programming and linearized by absolute value manipulation techniques.Then,we propose a framework of heuristic algorithms to solve this Nondeterministic Polynomial(NP)-hard problem.The numerical simulation results demonstrate the efficiency of the devised algorithms.Some useful summaries are concluded via the analysis of evaluation results.展开更多
Cloud manufacturing has become a reality. It requires sensing and capturing heterogeneous manufacturing resources and extensive data analysis through the industrial internet. However,the cloud computing and serviceori...Cloud manufacturing has become a reality. It requires sensing and capturing heterogeneous manufacturing resources and extensive data analysis through the industrial internet. However,the cloud computing and serviceoriented architecture are slightly inadequate in dynamic manufacturing resource management. This paper integrates the technology of edge computing and microservice and develops an intelligent edge gateway for internet of thing(IoT)-based manufacturing. Distributed manufacturing resources can be accessed through the edge gateway,and cloud-edge collaboration can be realized. The intelligent edge gateway provides a solution for complex resource ubiquitous perception in current manufacturing scenarios. Finally,a prototype system is developed to verify the effectiveness of the intelligent edge gateway.展开更多
In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer t...In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.展开更多
Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources.In this paper,we study the task schedu...Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources.In this paper,we study the task scheduling problem in the hierarchically deployed edge cloud.We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the problem.Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based solution.JNNHSP takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling solution.In-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.展开更多
Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.How...Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.展开更多
The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.Howe...The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.展开更多
基金supported by the National Research Foundation of Korea(NRF)funded by theMinistry of Science and ICT(2022K1A3A1A61014825)。
文摘With the rapid advancement of ICT and IoT technologies,the integration of Edge and Fog Computing has become essential to meet the increasing demands for real-time data processing and network efficiency.However,these technologies face critical security challenges,exacerbated by the emergence of quantum computing,which threatens traditional encryption methods.The rise in cyber-attacks targeting IoT and Edge/Fog networks underscores the need for robust,quantum-resistant security solutions.To address these challenges,researchers are focusing on Quantum Key Distribution and Post-Quantum Cryptography,which utilize quantum-resistant algorithms and the principles of quantum mechanics to ensure data confidentiality and integrity.This paper reviews the current security practices in IoT and Edge/Fog environments,explores the latest advancements in QKD and PQC technologies,and discusses their integration into distributed computing systems.Additionally,this paper proposes an enhanced QKD protocol combining the Cascade protocol and Kyber algorithm to address existing limitations.Finally,we highlight future research directions aimed at improving the scalability,efficiency,and practicality of QKD and PQC for securing IoT and Edge/Fog networks against evolving quantum threats.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-RG23129).
文摘Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.
基金supported in part by the National Natural Science Foundation of China under Grants 62201105,62331017,and 62075024in part by the Natural Science Foundation of Chongqing under Grant cstc2021jcyj-msxmX0404+1 种基金in part by the Chongqing Municipal Education Commission under Grant KJQN202100643in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110056.
文摘The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/6G.However, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.
文摘Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.
文摘Efficient resource provisioning,allocation,and computation offloading are critical to realizing lowlatency,scalable,and energy-efficient applications in cloud,fog,and edge computing.Despite its importance,integrating Software Defined Networks(SDN)for enhancing resource orchestration,task scheduling,and traffic management remains a relatively underexplored area with significant innovation potential.This paper provides a comprehensive review of existing mechanisms,categorizing resource provisioning approaches into static,dynamic,and user-centric models,while examining applications across domains such as IoT,healthcare,and autonomous systems.The survey highlights challenges such as scalability,interoperability,and security in managing dynamic and heterogeneous infrastructures.This exclusive research evaluates how SDN enables adaptive policy-based handling of distributed resources through advanced orchestration processes.Furthermore,proposes future directions,including AI-driven optimization techniques and hybrid orchestrationmodels.By addressing these emerging opportunities,thiswork serves as a foundational reference for advancing resource management strategies in next-generation cloud,fog,and edge computing ecosystems.This survey concludes that SDN-enabled computing environments find essential guidance in addressing upcoming management opportunities.
文摘The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.
基金the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) by granting the scholarship (Finance Code 001)supported by the Brazilian National Council for Scientific and Technological Development (CNPq, project number 433828/2018-8,435598/2018-0)+1 种基金the Minas Gerais Research Funding Foundation (FAPEMIG, project number CRA APQ 00929-15)CNPq productivity fellowships
文摘Functional traits are characteristics associated with the growth,reproduction,and survival of individuals.Studying them helps us understand how species traits drive ecosystem functioning.Thus,we evaluated the differences in traits and functional diversity between forest edges and interiors,and how the inclusion of intraspecific trait variation affects the assessment of functional diversity in these habitats.We sampled 10 representative forest patches,and,in each patch,we established five plots on the edge and five inside the forest,collecting leaf functional traits,allometric and wood density for all species.We assessed functional diversity using functional richness(FRic),divergence(FDiv),and dispersion(FDis).To assess the impact of incorporating intraspecific variation when comparing trait values and functional diversity indices,we established two scenarios:one that excludes intraspecific variation and another that includes it.We found that the edge and interior harbor individuals with distinct functional traits that alleviate the inherent stress of each habitat.The edge was also found to be more selective in terms of the range of functional traits,resulting in lower functional diversity.Our findings demonstrated that habitats play an important role in intraspecific trait variation(ITV)and that statistically significant differences between habitats,in relation to traits and functional diversity,were better observed with the inclusion of intraspecific variation.Our study highlights the potential of using natural forest patches to understand the edge effect,regardless of habitat loss.Additionally,we emphasize the importance of incorporating ITV into functional diversity studies,especially those on a smaller scale that incorporate quantitative variables,to better understand and predict ecological patterns.
基金supported by James M.Cox Foundation,National Institute on Deafness and Other Communication Disorders(grant no.1R21DC021029-01A1)Cox Enterprises Inc.,National Institute of Child Health and Human Development(grant no.AWD-006196-G1)Thrasher Research Fund Early Career Award Program.
文摘Bluetooth low energy(BLE)-based indoor localization has been extensively researched due to its cost-effectiveness,low power consumption,and ubiquity.Despite these advantages,the variability of received signal strength indicator(RSSI)measurements,influenced by physical obstacles,human presence,and electronic interference,poses a significant challenge to accurate localization.In this work,we present an optimised method to enhance indoor localization accuracy by utilising multiple BLE beacons in a radio frequency(RF)-dense modern building environment.Through a proof-of-concept study,we demonstrate that using three BLE beacons reduces localization error from a worst-case distance of 9.09-2.94 m,whereas additional beacons offer minimal incremental benefit in such settings.Furthermore,our framework for BLE-based localization,implemented on an edge network of Raspberry Pies,has been released under an open-source license,enabling broader application and further research.
文摘The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing’s ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.
文摘Resource allocation and task scheduling in the Cloud environment faces many challenges,such as time delay,energy consumption,and security.Also,executing computation tasks of mobile applications on mobile devices(MDs)requires a lot of resources,so they can offload to the Cloud.But Cloud is far from MDs and has challenges as high delay and power consumption.Edge computing with processing near the Internet of Things(IoT)devices have been able to reduce the delay to some extent,but the problem is distancing itself from the Cloud.The fog computing(FC),with the placement of sensors and Cloud,increase the speed and reduce the energy consumption.Thus,FC is suitable for IoT applications.In this article,we review the resource allocation and task scheduling methods in Cloud,Edge and Fog environments,such as traditional,heuristic,and meta-heuristics.We also categorize the researches related to task offloading in Mobile Cloud Computing(MCC),Mobile Edge Computing(MEC),and Mobile Fog Computing(MFC).Our categorization criteria include the issue,proposed strategy,objectives,framework,and test environment.
基金supported in part by the National Science Foundation(CNS-1647170)
文摘As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this problem.Although some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so far.The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users.Therefore,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet placement.To solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution.After cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility.The performance of CAPABLE has been validated by extensive simulations.
基金the National Key R&D Program of China 2018YFB1800804the Nature Science Foundation of China (No. 61871254,No. 61861136003,No. 91638204)Hitachi Ltd.
文摘By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.
基金partially supported by JSPS KAKENHI under Grant Number JP16J07062
文摘A mobile edge cloud provides a platform to accommodate the offloaded traffic workload generated by mobile devices.It can significantly reduce the access delay for mobile application users.However,the high user mobility brings significant challenges to the service provisioning for mobile users,especially to delay-sensitive mobile applications.With the objective to maximize a profit,which positively associates with the overall admitted traffic served by the local edge cloud,and negatively associates with the access delay as well as virtual machine migration delay,we study a fundamental problem in this paper:how to update the service provisioning solution for a given group of mobile users.Such a profit-maximization problem is formulated as a nonlinear integer linear programming and linearized by absolute value manipulation techniques.Then,we propose a framework of heuristic algorithms to solve this Nondeterministic Polynomial(NP)-hard problem.The numerical simulation results demonstrate the efficiency of the devised algorithms.Some useful summaries are concluded via the analysis of evaluation results.
基金supported by the National Key Research and Development Program of China (No.2020YFB1710500)the Primary Research & Development Plan of Jiangsu Province(No.BE2021091)。
文摘Cloud manufacturing has become a reality. It requires sensing and capturing heterogeneous manufacturing resources and extensive data analysis through the industrial internet. However,the cloud computing and serviceoriented architecture are slightly inadequate in dynamic manufacturing resource management. This paper integrates the technology of edge computing and microservice and develops an intelligent edge gateway for internet of thing(IoT)-based manufacturing. Distributed manufacturing resources can be accessed through the edge gateway,and cloud-edge collaboration can be realized. The intelligent edge gateway provides a solution for complex resource ubiquitous perception in current manufacturing scenarios. Finally,a prototype system is developed to verify the effectiveness of the intelligent edge gateway.
基金funding from TECNALIA,Basque Research and Technology Alliance(BRTA)supported by the project aOptimization of Deep Learning algorithms for Edge IoT devices for sensorization and control in Buildings and Infrastructures(EMBED)funded by the Gipuzkoa Provincial Council and approved under the 2023 call of the Guipuzcoan Network of Science,Technology and Innovation Program with File Number 2023-CIEN-000051-01.
文摘In a network environment composed of different types of computing centers that can be divided into different layers(clod,edge layer,and others),the interconnection between them offers the possibility of peer-to-peer task offloading.For many resource-constrained devices,the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity.In this scenario,it is worth considering transferring these tasks to resource-rich platforms,such as Edge Data Centers or remote cloud servers.For different reasons,it is more exciting and appropriate to download various tasks to specific download destinations depending on the properties and state of the environment and the nature of the functions.At the same time,establishing an optimal offloading policy,which ensures that all tasks are executed within the required latency and avoids excessive workload on specific computing centers is not easy.This study presents two alternatives to solve the offloading decision paradigm by introducing two well-known algorithms,Graph Neural Networks(GNN)and Deep Q-Network(DQN).It applies the alternatives on a well-known Edge Computing simulator called PureEdgeSimand compares them with the two defaultmethods,Trade-Off and Round Robin.Experiments showed that variants offer a slight improvement in task success rate and workload distribution.In terms of energy efficiency,they provided similar results.Finally,the success rates of different computing centers are tested,and the lack of capacity of remote cloud servers to respond to applications in real-time is demonstrated.These novel ways of finding a download strategy in a local networking environment are unique as they emulate the state and structure of the environment innovatively,considering the quality of its connections and constant updates.The download score defined in this research is a crucial feature for determining the quality of a download path in the GNN training process and has not previously been proposed.Simultaneously,the suitability of Reinforcement Learning(RL)techniques is demonstrated due to the dynamism of the network environment,considering all the key factors that affect the decision to offload a given task,including the actual state of all devices.
基金Supported by Scientific and Technological Innovation Project of Chongqing(No.cstc2021jxjl20010)The Graduate Student Innovation Program of Chongqing University of Technology(No.clgycx-20203166,No.gzlcx20222061,No.gzlcx20223229)。
文摘Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of resources.In this paper,we study the task scheduling problem in the hierarchically deployed edge cloud.We first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the problem.Different from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based solution.JNNHSP takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling solution.In-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.
基金supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.
基金This research was funded by the National Natural Science Foundation of China(Grant Number 61902069)Natural Science Foundation of Fujian Province of China(Grant Number 2021J011068)+1 种基金Research Initiation Fund Program of Fujian University of Technology(GY-S24002,GY-Z21048)Fujian Provincial Department of Science and Technology Industrial Guidance Project(Grant Number 2022H0025).
文摘The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers.However,with the advancement of information and communication technology,new security and privacy challenges have emerged for AMI.To address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this paper.The proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid.The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection levels.Furthermore,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user data.Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.
