Mobility management is an essential component in enabling mobile hosts to move seamlessly from one location to another while maintaining the packet routing efficiency between the corresponding hosts. One of the concer...Mobility management is an essential component in enabling mobile hosts to move seamlessly from one location to another while maintaining the packet routing efficiency between the corresponding hosts. One of the concerns raised with the internet engineering task force(IETF)mobile IP standard is the excessive signaling generated for highly mobile computers. This paper introduces a scheme to address that issue by manipulating the inherent client-server interaction which exists in most applications to provide the correspondent host with the current mobile host binding. To evaluate the performance of the scheme, typical internet application sessions involving a mobile host is simulated and the signaling and routing costs are examined. Results show a substantial reduction in the mobility management overhead as well as the total cost of delivering packets to the mobile host.展开更多
Nodes in a mobile computing system are vulnerable to clone attacks due to their mobility.In such attacks,an adversary accesses a few network nodes,generates replication,then inserts this replication into the network,p...Nodes in a mobile computing system are vulnerable to clone attacks due to their mobility.In such attacks,an adversary accesses a few network nodes,generates replication,then inserts this replication into the network,potentially resulting in numerous internal network attacks.Most existing techniques use a central base station,which introduces several difficulties into the system due to the network’s reliance on a single point,while other ways generate more overhead while jeopardising network lifetime.In this research,an intelligent double hashing-based clone node identification scheme was used,which reduces communication and memory costs while performing the clone detection procedure.The approach works in two stages:in the first,the network is deployed using an intelligent double hashing procedure to avoid any network collisions and then in the second,the clone node identification procedure searches for any clone node in the network.This first phase verifies the node prior to network deployment,and then,whenever a node wants to interact,it executes the second level of authentication.End-to-end delay,which is bound to increase owing to the injection of clone nodes,and packet loss,which is reduced by the double hashing technique,were used to evaluate the performance of the aforementioned approach.展开更多
Contact between mobile hosts and database servers presents many problems in theMobile Database System(MDS).It is harmed by a variety of causes,including handoff,inadequate capacity,frequent transaction updates,and rep...Contact between mobile hosts and database servers presents many problems in theMobile Database System(MDS).It is harmed by a variety of causes,including handoff,inadequate capacity,frequent transaction updates,and repeated failures,both of which contribute to serious issues with the information system’s consistency.However,error tolerance technicality allows devices to continue performing their functions in the event of a failure.The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory.Several of the presented recovery protocols are chosen and evaluated in order to determine the most critical factors affecting the recovery mechanism,such as the number of processes,the time required to deliver messages,and the number of messages logged-in time.Then,using the suggested payout matrix,the game theory strategy is adapted to choose the optimum recovery technique for the specified environmental variables.The NS2 simulatorwas used to carry out the tests and apply the chosen recovery protocols.The experiments validate the proposed model’s usefulness in comparison to other methods.展开更多
The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges...The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.展开更多
With the rapid developments of wireless communication and microelectronic technology, the bandwidth of wireless communication is becoming wider than ever, up to 100Gbps and the computer can be designed as small as a m...With the rapid developments of wireless communication and microelectronic technology, the bandwidth of wireless communication is becoming wider than ever, up to 100Gbps and the computer can be designed as small as a match with powerful computing and controlling capability. These rapid developments have extended the mobile computing. There are many application forms of mobile computing, such as mobile databases, mobile data management, wearable computing etc. A great branch of mobile computing, Augmented Reality (AR), which is the combination of mobile computing and wearable computers was discussed.展开更多
Due to the mobility of mobile hosts,checkpoints and message logs of the computing process may disperseover different mobile support stations in the checkpointing and rollback recovery protocol for mobilecomputing.Thre...Due to the mobility of mobile hosts,checkpoints and message logs of the computing process may disperseover different mobile support stations in the checkpointing and rollback recovery protocol for mobilecomputing.Three existing checkpoint handoff schemes do not give well consideration to the efficiency offailure-free process execution and the recovery speed of the failure process at the same time.A dynamicadaptive handoff management of the checkpointing and rollback recovery protocol for mobile computing isproposed in this paper.According to the individual feature and current state of each mobile host,differentimplementations are selected dynamically to complete the handoff process upon the handoff event.Performance analyses show that the proposed handoff management incurs a low loss of performance duringfailure-free and achieves a quick recovery upon the process fault.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latenc...Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks.Inspired by mobile edge computing(MEC),VR users can offload rendering tasks to other devices.The main challenge of task offloading is to minimize latency and energy consumption.Yet,in non-panoramic VR scenarios,it is essential to consider the Quality of Perceptual Experience(QOPE)for users.Simultaneously,one must also take into account the diverse requirements of users in real-world scenarios.Therefore,this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem(CMOP)that minimizes latency and energy consumption while providing a satisfied QOPE.And we propose an evolutionary algorithm(EA),GNSGA-II,to solve the CMOP.Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives,satisfying the requirements of different users.展开更多
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of ...The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of autoadapting distributed object is proposed, and evaluating methods of object performance are given as well. Distributed objects can adjust their behaviors automaticallyin the framework and keep in relatively good performance to serve requests of remoteapplications. It is an efficient way to implement the performance transparency formobile clients.展开更多
In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wirele...In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wireless communications disconnect at times and clients move frequently. In this paper, we extend the general semantic cache mechanism by enabling mobile clients to share their local semantic caches in a cooperative matter, and the process way and flow chart of the algorithm are described in detail. In addition, we discuss the methods used in cache consistence maintenance, which focus on confirm receiver of the periodic cache invalidation report and the process of validate client＇s local cache. The experiment results indicate cooperative semantic cache mechanism could reduce query response time and increase cache hit ratio effectively.展开更多
When applied to mobile computing systems,checkpoint protocols for distributed computing systems would face many new challenges, such as low wireless bandwidth, frequent disconnections, and lack of stable storage at mo...When applied to mobile computing systems,checkpoint protocols for distributed computing systems would face many new challenges, such as low wireless bandwidth, frequent disconnections, and lack of stable storage at mobile hosts. This paper proposes a novel checkpoint protocol to effectively reduce the coordinating overhead. By using a communication vector, only a few processes participate in the checkpointing event. During checkpointing, the scheme can save the time used to trace the dependency tree by sending checkpoint requests to dependent processes at once. In addition, processes are non- blocking in this scheme, since the inconsistency is resolved by the piggyback technique. Hence the unnecessary and orphan messages can be avoided. Compared with the traditional coordinated checkpoint approach, the proposed non-blocking algorithm obtains a minimal number of processes to take checkpoints. It also reduces the checkpoint latency, which brings less overhead to mobile host with limited resources.展开更多
Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication.Existing software systems can be migrated(while preserving t...Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication.Existing software systems can be migrated(while preserving their data and logic)to mobile computing platforms that support portability,context-sensitivity,and enhanced usability.In recent years,some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms.To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms.We aim to analyze the progression and impacts of existing research,highlight challenges and solutions that reflect dimensions of emerging and futuristic research.We followed evidence-based software engineering(EBSE)method to conduct a systematic mapping study(SMS)of the existing research that has progressed over more than a decade(25 studies published from 1996–2017).We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress,impacts,and potential areas of futuristic research and development.The SMS has identified three types of migration namely Static,Dynamic,and State-based Migration of existing software systems to mobile computing platforms.Migration to mobile computing platforms enables existing software systems to achieve portability,context-sensitivity,and high connectivity.However,mobile systems may face some challenges such as resource poverty,data security,and privacy.The emerging and futuristic research aims to support patterns and tool support to automate the migration process.The results of this SMS can benefit researchers and practitioners-by highlighting challenges,solutions,and tools,etc.,-to conceptualize the state-of-the-art and futuristic trends that support migration of existing software to mobile computing.展开更多
Cloud computing is an emerging and popular method of accessing shared and dynamically configurable resources via the computer network on demand. Cloud computing is excessively used by mobile applications to offload da...Cloud computing is an emerging and popular method of accessing shared and dynamically configurable resources via the computer network on demand. Cloud computing is excessively used by mobile applications to offload data over the network to the cloud. There are some security and privacy concerns using both mobile devices to offload data to the facilities provided by the cloud providers. One of the critical threats facing cloud users is the unauthorized access by the insiders (cloud administrators) or the justification of location where the cloud providers operating. Although, there exist variety of security mechanisms to prevent unauthorized access by unauthorized user by the cloud administration, but there is no security provision to prevent unauthorized access by the cloud administrators to the client data on the cloud computing. In this paper, we demonstrate how steganography, which is a secrecy method to hide information, can be used to enhance the security and privacy of data (images) maintained on the cloud by mobile applications. Our proposed model works with a key, which is embedded in the image along with the data, to provide an additional layer of security, namely, confidentiality of data. The practicality of the proposed method is represented via a simple case study.展开更多
Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers ...Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.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
Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo...Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.展开更多
With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored t...With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.展开更多
The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload comput...The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。展开更多
This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay o...This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.展开更多
文摘Mobility management is an essential component in enabling mobile hosts to move seamlessly from one location to another while maintaining the packet routing efficiency between the corresponding hosts. One of the concerns raised with the internet engineering task force(IETF)mobile IP standard is the excessive signaling generated for highly mobile computers. This paper introduces a scheme to address that issue by manipulating the inherent client-server interaction which exists in most applications to provide the correspondent host with the current mobile host binding. To evaluate the performance of the scheme, typical internet application sessions involving a mobile host is simulated and the signaling and routing costs are examined. Results show a substantial reduction in the mobility management overhead as well as the total cost of delivering packets to the mobile host.
文摘Nodes in a mobile computing system are vulnerable to clone attacks due to their mobility.In such attacks,an adversary accesses a few network nodes,generates replication,then inserts this replication into the network,potentially resulting in numerous internal network attacks.Most existing techniques use a central base station,which introduces several difficulties into the system due to the network’s reliance on a single point,while other ways generate more overhead while jeopardising network lifetime.In this research,an intelligent double hashing-based clone node identification scheme was used,which reduces communication and memory costs while performing the clone detection procedure.The approach works in two stages:in the first,the network is deployed using an intelligent double hashing procedure to avoid any network collisions and then in the second,the clone node identification procedure searches for any clone node in the network.This first phase verifies the node prior to network deployment,and then,whenever a node wants to interact,it executes the second level of authentication.End-to-end delay,which is bound to increase owing to the injection of clone nodes,and packet loss,which is reduced by the double hashing technique,were used to evaluate the performance of the aforementioned approach.
文摘Contact between mobile hosts and database servers presents many problems in theMobile Database System(MDS).It is harmed by a variety of causes,including handoff,inadequate capacity,frequent transaction updates,and repeated failures,both of which contribute to serious issues with the information system’s consistency.However,error tolerance technicality allows devices to continue performing their functions in the event of a failure.The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory.Several of the presented recovery protocols are chosen and evaluated in order to determine the most critical factors affecting the recovery mechanism,such as the number of processes,the time required to deliver messages,and the number of messages logged-in time.Then,using the suggested payout matrix,the game theory strategy is adapted to choose the optimum recovery technique for the specified environmental variables.The NS2 simulatorwas used to carry out the tests and apply the chosen recovery protocols.The experiments validate the proposed model’s usefulness in comparison to other methods.
基金Sponsored by the National 863 Plan (Grant No.2002AA1Z2101)the National Tenth Five-Year Research Plan(Grant No. 41316.1.2).
文摘The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1) the blocked nodes inform their predecessors and successors simultaneously; 2) the detection messages (agents) hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n-2 steps and with (n^2-n-2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.
文摘With the rapid developments of wireless communication and microelectronic technology, the bandwidth of wireless communication is becoming wider than ever, up to 100Gbps and the computer can be designed as small as a match with powerful computing and controlling capability. These rapid developments have extended the mobile computing. There are many application forms of mobile computing, such as mobile databases, mobile data management, wearable computing etc. A great branch of mobile computing, Augmented Reality (AR), which is the combination of mobile computing and wearable computers was discussed.
基金Supported by the National Natural Science Foundation of China (No. 60873138)Postdoctoral Scientific Research Foundation of Heilongjiang (No. LBH-008124)the Fundamental Research Funds for the Central Universities (No. HEUCFT1007)
文摘Due to the mobility of mobile hosts,checkpoints and message logs of the computing process may disperseover different mobile support stations in the checkpointing and rollback recovery protocol for mobilecomputing.Three existing checkpoint handoff schemes do not give well consideration to the efficiency offailure-free process execution and the recovery speed of the failure process at the same time.A dynamicadaptive handoff management of the checkpointing and rollback recovery protocol for mobile computing isproposed in this paper.According to the individual feature and current state of each mobile host,differentimplementations are selected dynamically to complete the handoff process upon the handoff event.Performance analyses show that the proposed handoff management incurs a low loss of performance duringfailure-free and achieves a quick recovery upon the process fault.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金supported by National Natural Science Foundation of China(No.62101499)Science and National Key Research and Development Program of China(2019YFB1803200).
文摘Non-panoramic virtual reality(VR)provides users with immersive experiences involving strong interactivity,thus attracting growing research and development attention.However,the demand for high bandwidth and low latency in VR services presents greater challenges to existing networks.Inspired by mobile edge computing(MEC),VR users can offload rendering tasks to other devices.The main challenge of task offloading is to minimize latency and energy consumption.Yet,in non-panoramic VR scenarios,it is essential to consider the Quality of Perceptual Experience(QOPE)for users.Simultaneously,one must also take into account the diverse requirements of users in real-world scenarios.Therefore,this paper proposes a QOPE model to measure the visual quality of non-panoramic VR users and models the non-panoramic VR task offloading problem based on MEC as a constrained multi-objective optimization problem(CMOP)that minimizes latency and energy consumption while providing a satisfied QOPE.And we propose an evolutionary algorithm(EA),GNSGA-II,to solve the CMOP.Simulation results show that the algorithm can effectively find various trade-off solutions among the objectives,satisfying the requirements of different users.
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
文摘The low bandwidth hinders the development of mobile computing.Besides providing relatively higher bandwidth on communication layer, constructing adaptable upper application is important. In this paper, a framework of autoadapting distributed object is proposed, and evaluating methods of object performance are given as well. Distributed objects can adjust their behaviors automaticallyin the framework and keep in relatively good performance to serve requests of remoteapplications. It is an efficient way to implement the performance transparency formobile clients.
基金supported by the National Basic Research and Development Program of China (2007CB07100, 2007CB07106)the Ministry of Education in China Project of Humanities and Social Sciences (11YJCZH195)
文摘In cooperative cache research domain, most of previous work engage in peer-to-peer systems and distributed systems, but do not involve applying cooperative semantic cache in mobile computing environments, which wireless communications disconnect at times and clients move frequently. In this paper, we extend the general semantic cache mechanism by enabling mobile clients to share their local semantic caches in a cooperative matter, and the process way and flow chart of the algorithm are described in detail. In addition, we discuss the methods used in cache consistence maintenance, which focus on confirm receiver of the periodic cache invalidation report and the process of validate client＇s local cache. The experiment results indicate cooperative semantic cache mechanism could reduce query response time and increase cache hit ratio effectively.
基金the Postdoctoral Science Foundation (No. 20060390461)the Basic Research Foundation of Harbin Engineering University (Nos. HEUF040806,HEUFT05009, and HEUFP05020)
文摘When applied to mobile computing systems,checkpoint protocols for distributed computing systems would face many new challenges, such as low wireless bandwidth, frequent disconnections, and lack of stable storage at mobile hosts. This paper proposes a novel checkpoint protocol to effectively reduce the coordinating overhead. By using a communication vector, only a few processes participate in the checkpointing event. During checkpointing, the scheme can save the time used to trace the dependency tree by sending checkpoint requests to dependent processes at once. In addition, processes are non- blocking in this scheme, since the inconsistency is resolved by the piggyback technique. Hence the unnecessary and orphan messages can be avoided. Compared with the traditional coordinated checkpoint approach, the proposed non-blocking algorithm obtains a minimal number of processes to take checkpoints. It also reduces the checkpoint latency, which brings less overhead to mobile host with limited resources.
基金This research has been funded by Research Deanship in University of Ha’il Saudi Arabia through project number RG-20155.
文摘Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication.Existing software systems can be migrated(while preserving their data and logic)to mobile computing platforms that support portability,context-sensitivity,and enhanced usability.In recent years,some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms.To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms.We aim to analyze the progression and impacts of existing research,highlight challenges and solutions that reflect dimensions of emerging and futuristic research.We followed evidence-based software engineering(EBSE)method to conduct a systematic mapping study(SMS)of the existing research that has progressed over more than a decade(25 studies published from 1996–2017).We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress,impacts,and potential areas of futuristic research and development.The SMS has identified three types of migration namely Static,Dynamic,and State-based Migration of existing software systems to mobile computing platforms.Migration to mobile computing platforms enables existing software systems to achieve portability,context-sensitivity,and high connectivity.However,mobile systems may face some challenges such as resource poverty,data security,and privacy.The emerging and futuristic research aims to support patterns and tool support to automate the migration process.The results of this SMS can benefit researchers and practitioners-by highlighting challenges,solutions,and tools,etc.,-to conceptualize the state-of-the-art and futuristic trends that support migration of existing software to mobile computing.
文摘Cloud computing is an emerging and popular method of accessing shared and dynamically configurable resources via the computer network on demand. Cloud computing is excessively used by mobile applications to offload data over the network to the cloud. There are some security and privacy concerns using both mobile devices to offload data to the facilities provided by the cloud providers. One of the critical threats facing cloud users is the unauthorized access by the insiders (cloud administrators) or the justification of location where the cloud providers operating. Although, there exist variety of security mechanisms to prevent unauthorized access by unauthorized user by the cloud administration, but there is no security provision to prevent unauthorized access by the cloud administrators to the client data on the cloud computing. In this paper, we demonstrate how steganography, which is a secrecy method to hide information, can be used to enhance the security and privacy of data (images) maintained on the cloud by mobile applications. Our proposed model works with a key, which is embedded in the image along with the data, to provide an additional layer of security, namely, confidentiality of data. The practicality of the proposed method is represented via a simple case study.
文摘Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.
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supported by NSFC(No. 61571055)fund of SKL of MMW (No. K201815)Important National Science & Technology Specific Projects(2017ZX03001028)
文摘Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.
基金the National S&T Major Project (No. 2018ZX03001011)the National Key R&D Program(No.2018YFB1801102)+1 种基金the National Natural Science Foundation of China (No. 61671072)the Beijing Natural Science Foundation (No. L192025)
文摘With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.
基金the National Natural Science Foundation of China under Grants No.61572440 and No.61502428the Zhejiang Provincial Natural Science Foundation of China under Grants No.LR16F010003 and No.LY19F020033.
文摘The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。
基金supported in part by National Natural Science Foundation of China (Grant No. 62101277)in part by the Natural Science Foundation of Jiangsu Province (Grant No. BK20200822)+1 种基金in part by the Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant No. 20KJB510036)in part by the Guangxi Key Laboratory of Multimedia Communications and Network Technology (Grant No. KLF-2020-03)。
文摘This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.
