Database Recovery Technique for Mobile Computing:A Game Theory Approach

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.

An Extended Application form of Mobile Computing ── Augmented Reality

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.

Intelligent Multilevel Node Authentication in Mobile Computing Using Clone Node

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.

An architecture for mobile database management system

In order to design a new kind of mobile database management system (DBMS)more suitable for mobile computing than the existent DBMS, the essence of database systems in mobilecomputing is analyzed. An opinion is introduced that the mobile database is a kind of dynamicdistributed database, and the concept of virtual servers to translate the clients' mobility to theservers' mobility is proposed. Based on these opinions, a kind of architecture of mobile DBMS, whichis of versatility, is presented. The architecture is composed of a virtual server and a local DBMS,the virtual server is the kernel of the architecture and its functions are described. Eventually,the server kernel of a mobile DBMS prototype is illustrated.

An adaptive handoff management for fault tolerant mobile computing

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.

A distributed deadlock detection algorithm for mobile computing system

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.

IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this article.The proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for UEs.Firstly,we introduce the AGC-MEC architecture and elaborate three typical use cases.Then,we discuss four main challenges in the AGC-MEC as well as their potential solutions.Next,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy.Finally,we highlight several potential research directions of the AGC-MEC.

Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing services.Sometimes,the network state information is uncertain or unknown.To deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this paper.The problem of minimizing the average sum task completion delay of all IoT devices over all time periods is formulated.We decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method.Simulation results validate that the proposed scheme performs better than other baseline schemes.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure.Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing(MEC)to the Internet of Things(loT).However,problems such as multi-user and huge data flow in large areas,which contradict the reality that a single UAV is constrained by limited computing power,still exist.Due to allowing UAV collaboration to accomplish complex tasks,cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing,which reduces the computing power consumption and endurance pressure of terminals.Considering the computing requirements of the user terminal,delay constraint of a computing task,energy constraint,and safe distance of UAV,we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption.However,the resulting optimization problem is originally nonconvex and thus,difficult to solve optimally.To tackle this problem,we developed an energy efficiency optimization algorithm using Block Coordinate Descent(BCD)that decomposes the problem into three convex subproblems.Furthermore,we jointly optimized the number of local computing tasks,number of computing offloaded tasks,trajectories of UAV,and offloading matching relationship between multi-UAVs and multiuser terminals.Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading mode.The weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data assignment.Due to the non-analytic expression of the WSA w.r.t.the optimization variables and the unknowability of future network information,the problem cannot be solved with known solution methods.Therefore,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems separately.We analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of users.Simulation results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline methods.It is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.

Adaptive delay-energy balanced partial offloading strategy in Mobile Edge Computing networks

Mobile Edge Computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy requirements.In this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy consumption.To address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing tasks.Different from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power constraints.Since the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading solution.Numerical experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

Task offloading mechanism based on federated reinforcement learning in mobile edge computing

With the arrival of 5G,latency-sensitive applications are becoming increasingly diverse.Mobile Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among researchers.To improve the Quality of Service(QoS),this study focuses on computation offloading in MEC.We consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new users.The QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in MEC.The proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing delay.To solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model universality.In the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay cost.Therefore,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of FL.The simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.

Dynamic Task Offloading for Digital Twin-Empowered Mobile Edge Computing via Deep Reinforcement Learning

Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by themselves.Mobile edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to MEC.In this paper,we study the problem of dynamic task offloading for digital twin-empowered MEC.Digital twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT devices.We formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ESs.Then,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve it.Comparative experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning

Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our lives.The data generated by mobile devices has reached a massive level.The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load.Mobile Edge Computing(MEC)has been proposed to solve these problems.Because of limited computation ability and battery capacity,tasks can be executed in the MEC server.However,how to schedule those tasks becomes a challenge,and is the main topic of this piece.In this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC.In view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification problem.Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.