Mobile Computing Environments Research Articles

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

Elite‐guided equilibrium optimiser based on information enhancement: Algorithm and mobile edge computing applications

Elite‐guided equilibrium optimiser based on information enhancement: Algorithm and mobile edge computing applications

MCOTM: Mobility-aware computation offloading and task migration for edge computing in industrial IoT

Mobility-aware devices are crucial components of Industrial Internet of Things (IIoT). However, they face limitations in terms of battery capacity and computation power, which restrict their ability to provide services requiring broad bandwidth and strong computation power for computation-intensive tasks. While offloading can strengthen device computation power, ineffective offloading decisions result from device mobility and limited adaptability to changes in environmental resources, or are not applicable to the current mobile edge computing (MEC) environment. In this paper, we address these challenges by proposing a mobility-aware computation offloading and task migration approach (MCOTM) based on trajectory and resource prediction to address this issue of mobility offloading, which minimizes task turnaround time and system energy consumption. Simultaneously, our approach enhances the decision agent continuously to decrease task migration rates. MCOTM uses Lagrange interpolation equations to determine the trajectory of mobile devices, and Long Short-Term Memory (LSTM) to track the time-varying resources characteristics in IIoT. These prediction results will be used to assist Deep Deterministic Policy Gradient (DDPG) for making online computation offloading, task migration and resource allocation decisions. Experimental results show that the proposed MCOTM effectively reduces task turnaround time by at least 42% and system energy consumption by 10% while maintaining a low task migration rate of around 50%, even with an increasing number of tasks.

Optimization of the College Basketball Teaching Mode Based on the Applied Explainable Association Rule Algorithm and Cluster Analysis in Mobile Computing Environments

ABSTRACT Under the mobile computing environment, a large number of convenient mobile terminals and extensive mobile network application services have been produced. The technology has been used in college sports teaching to optimize the management of sports teaching. In this context, this paper studies the application of the association rule algorithm and cluster analysis in basketball teaching, which will effectively promote the practice and application of the association rule algorithm and cluster analysis in college PE teaching in China. This paper studies basketball teaching in colleges and universities based on the applied explainable association rule algorithm and cluster analysis. It is concluded that the p value of positioning shooting in the basic basketball technology test between the experimental class and the control class before the experiment is 0.883, which is greater than 0.06, indicating that there is no significant difference between the experimental class and the control class before the experiment. The p value of round-trip straight dribble in the whole field is 0.735, which is greater than 0.07, indicating that there is no significant difference between the experimental class and the control class before the experiment. This teaching mode plays a significant role in cultivating beginners’ learning interest and enthusiasm, preliminarily mastering movement skills and establishing solid technical concepts. With the complex emotional experience of the association rule algorithm, it is of great significance for teachers to grasp students’ various emotional experiences in learning, cultivate students’ team consciousness by cluster analysis, guide and dredge their negative emotions, and develop students’ sense of cooperation, team spirit and democratic spirit, personal responsibility and personality.

Multi-type task offloading for wireless Internet of Things by federated deep reinforcement learning

With the popularity of Internet of Things (IoT) smart devices, the amount of data generated by these devices has grown rapidly. In these mobile edge computing (MEC) environments, it is not only important to save time and energy in offloading tasks, but also to protect user data. In this paper, due to the dynamics and complexity of the system, a multi-type task offloading based on a multi-capability federated deep Q-network (M2FD) algorithm is proposed to optimize the bi-objective performance. The algorithm consists of two parts, federated learning protects user privacy by transmitting model for training instead of data, and deep reinforcement learning trains model accuracy and identifies suitable offloading nodes with heterogeneous capabilities for multi-type tasks. In addition, under the constraints of the service experience guarantee (SEG) model, the tasks are offloaded with the goal of improving system utility while reducing system cost. Experiments show that the M2FD increases system utility, guarantees privacy, and reduces task response time and energy consumption.

Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment

The offloading of computationally intensive tasks to edge servers is indispensable in the mobile edge computing (MEC) environment. Once the tasks are offloaded, the subsequent challenges lie in buffering them and assigning them to edge virtual machine (VM) resources to meet the multicriteria requirement. Furthermore, the edge resources’ availability is dynamic in nature and needs a joint prediction and optimal allocation for the efficient usage of resources and fulfillment of the tasks’ requirements. To this end, this work has three contributions. First, a delay sensitivity-based priority scheduling (DSPS) policy is presented to schedule the tasks as per their deadline. Secondly, based on exploratory data analysis and inferred seasonal patterns in the usage of edge CPU resources from the GWA-T-12 Bitbrains VM utilization dataset, the availability of VM resources is predicted by using a Holt–Winters-based univariate algorithm (HWVMR) and a vector autoregression-based multivariate algorithm (VARVMR). Finally, for optimal and fast task assignment, a parallel differential evolution-based task allocation (pDETA) strategy is proposed. The proposed algorithms are evaluated extensively with standard performance metrics, and the results show nearly 22%, 35%, and 69% improvements in cost and 41%, 52%, and 78% improvements in energy when compared with MTSS, DE, and min–min strategies, respectively.

A Federated Learning and Deep Reinforcement Learning-Based Method with Two Types of Agents for Computation Offload.

With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

A Blockchain-Based Authentication and Authorization Scheme for Distributed Mobile Cloud Computing Services.

Authentication and authorization constitute the essential security component, access control, for preventing unauthorized access to cloud services in mobile cloud computing (MCC) environments. Traditional centralized access control models relying on third party trust face a critical challenge due to a high trust cost and single point of failure. Blockchain can achieve the distributed trust for access control designs in a mutual untrustworthy scenario, but it also leads to expensive storage overhead. Considering the above issues, this work constructed an authentication and authorization scheme based on blockchain that can provide a dynamic update of access permissions by utilizing the smart contract. Compared with the conventional authentication scheme, the proposed scheme integrates an extra authorization function without additional computation and communication costs in the authentication phase. To improve the storage efficiency and system scalability, only one transaction is required to be stored in blockchain to record a user's access privileges on different service providers (SPs). In addition, mobile users in the proposed scheme are able to register with an arbitrary SP once and then utilize the same credential to access different SPs with different access levels. The security analysis indicates that the proposed scheme is secure under the random oracle model. The performance analysis clearly shows that the proposed scheme possesses superior computation and communication efficiencies and requires a low blockchain storage capacity for accomplishing user registration and updates.

Proposed Data Sanitization for Privacy Preservation in Mobile Computing

This article has developed a novel dual-data sanitization and dual-data restoration model for privacy preservation in mobile cloud computing (MCC) environment. The original medical data have been sanitized at the transmitter end utilizing the newly suggested dual-data sanitization technique. The wireless channel transports the sanitized data from the transmitter to the receiver. The dual-sanitized input has been recovered at the other end. More significantly, the exact key generation, which has been found by a novel hybrid optimization model, is essential to both operations (dual-data cleaning and dual-data restoring). Electric fish customized dolphin swarm optimization (ECDO) is a suggested hybrid optimization model that is a conceptual merger of the electric fish optimization (EFO) and the dolphin swarm method. Ultimately, to confirm the effectiveness of the anticipated strategy, a study has been made. The present architecture has been contrasted against the previous models, including the DSOA, EFO, SMO, WOA, and J-SSO, respectively. The restoration effectiveness of the projected model is 57.84%, 26.3%, 5.26%, 31.5%, and 15.7% improved over DSOA, EFO, SMO, WOA, and J-SSO, respectively. As a result of the overall assessment, the proposed model appears to be extremely secure during data transfer.

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

Social Issues of Mobile in the Wireless Market: An Overview of the Rapid Changes in the Mobile Computing Environment

The use of mobile devices embedded in the surrounding physical environment, users can be provided with transparent computing and communication services at all times and in all places. The complexity of providing such services stems from the fact that the communication devices and the objects with which they interact may both be mobile. Their implementation requires advances in wireless network technologies and devices, development of infrastructures supporting cognitive environments, discovery and identification of mobile computing applications and services, security and an understanding of the cross-layer interactions between all of these components. This paper provides the idea of new mobile computing applications, including location aware and context aware applications, improved networking communication protocols to support evolving needs of mobile computing applications, and better internet backbone services to support next generation mobile computing. This paper also targets IT developers, system architects, and managers looking at how best to develop and deploy mobile applications.

Multiparty Dynamic Data Integration Scheme of Industrial Chain Collaboration Platform in Mobile Computing Environment

At present, there are still many problems to be solved in the information interaction between internal platforms of different enterprises and third-party platform, especially the data integration problem between them, specifically mentioning the two aspects of data nonstandardization and difficult data integration. To solve the above problems, this paper proposes a multiparty dynamic data integration scheme for the industrial chain collaboration platform. On the one hand, this scheme builds a standard data classification system from the perspective of the type of data to solve the problem of data irregularity; on the other hand, it builds a multiparty dynamic data integration model based on rules and requests to solve the problem of difficult data integration. This paper verifies the feasibility, flexibility, and rationality of the scheme through case analysis and experimental analysis.

Complexity Construction of Intelligent Marketing Strategy Based on Mobile Computing and Machine Learning Simulation Environment

Mankind’s research on marketing has a history of hundreds of years, and it has been fruitful in continuous summary and research. Now the theory of marketing has gradually penetrated into the minds of every company and even individual. A successful marketing strategy is the inevitable result of scientific planning and effective implementation. However, the current marketing strategy has gradually failed to meet the needs of corporates. In order to find the best solution for corporate marketing strategy, we built a simulation environment based on mobile computing and machine learning and compared the differences by simulating several companies of the same size in this city (corporate efficiency and revenue and expenditure under the marketing strategy). The results of the study found that intelligent marketing based on machine learning is more suitable for enterprises than general marketing strategies. The efficiency of enterprises has increased by about 20%, and the income of enterprises has increased by more than 30% compared with traditional marketing strategies. This shows that the intelligent marketing strategy based on mobile computing and machine learning to build a simulated environment plays an extremely important role in the peculiarities.

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including cli-max greedy and uti-positive guarantee , are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of the proposal.

Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment

Abstract Efficient task scheduling improves offloading performance in mobile edge computing (MEC) environment. The jobs offloaded by different users would have different dependent tasks with diverse resource demands at different times. Meanwhile, due to the heterogeneity of edge servers configurations in MEC, offloaded jobs may frequently have placement constraints, restricting them to run on a particular class of edge servers meeting specific software running settings. This spatio-temporal information gives the opportunity to improve the resource utilization of the computing system. In this paper, we study the scheduling method for the jobs consisting of dependent tasks offloaded by different users in MEC. A new task offloading scheduler, Horae, is proposed to not only improve the resource utilization of MEC environment but also guarantees to select the edge server which could satisfy placement constraints for each offloaded task. Concretely, considering the fact that each job would experience slack time as a result of competing for limited resource with other jobs in MEC, Horae minimizes the sum of all slack time values of all the jobs while guaranteeing placement constraints, and therefore improve the resource utilization of the system. Horae was validated for its feasibility and efficiency by means of extensive experiments, which are presented in this paper.

Online user allocation in mobile edge computing environments:A decentralized reactive approach

As a newly emerging computing paradigm, mobile edge computing (MEC) can energize novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. However, considering the complex cyber–physical environment and varied time-space user behaviors, as mobile application vendors, how to decide which edge server to serve which edge user in real-time becomes the key challenge. Instead of assuming the simultaneous-batch-arrival pattern of incoming edge users and handling the edge user allocation (EUA) problem as a static optimization, in this paper, we consider the online EUA problem where edge users’ resource demands arrive and depart dynamically. We consider the long-term edge user allocation rate, edge server hiring cost, and edge server energy consumption as allocation targets from the mobile application vendor perspective, and propose a decentralized reactive approach by employing a fuzzy control mechanism to yield the real-time allocation decisions. Experiments based on real-world MEC environment datasets demonstrate our approach outperforms state-of-the-art and baseline ones in terms of user allocation rate, server hiring cost, and energy consumption. • This paper targets at the online edge user allocation (online EUA) problem in mobile edge computing (MEC) environments. • We propose a decentralized reactive architecture for the proposed online EUA problem. • We develop a fuzzy-control-based reactive approach which named DRoEUA to yield real-time allocation decisions.

A Novel IM Sync Message-Based Cross-Device Tracking

Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high F1-scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.

Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method.

In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.

Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment

Internet of Things (IoT) is an emergent communication technology connecting heterogeneous users, services and applications for granting seamless support for end-user demands. The heterogeneous nature of the devices and intelligent computing, storage, and processing paradigms are exploited for serving different application requests of the end-user devices. Edge computing inherits the functions of IoT platform to the brim of the user network to grant tardiness-free service/ communication support. This manuscript discusses a novel delay-aware concurrent data management (DCDM) method for IoT collaborative mobile edge computing (MEC) environment. This method categorizes request and response data independently in the edge layer through timeout and request density based optimization. A differential evolution (DE) based optimization is introduced for optimizing the constraints of the framework in favor of concurrent request processing and precise data allocation. This method is tested through appropriate experiments and the corresponding results are verified to prove the consistency of the method. The metrics such as serviced and processed request, response time, resource utilization, and communication span are validated using the experimental results. The performance of the proposed DCDM is compared with the existing methods.

EPerícia: Web and Mobile Computing Environment for Public Forensic Data Management and Collect

ePerícia: Web and Mobile Computing Environment for Public Forensic Data Management and Collect