Limitations Of Mobile Devices Research Articles

Multi-objective optimization in order to allocate computing and telecommunication resources based on non-orthogonal access, participation of cloud server and edge server in 5G networks

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile devices by enabling users to offload computational tasks to edge servers, rather than relying on distant cloud servers. This approach significantly reduces the latency associated with cloud processing, thereby enhancing the quality of service. In this paper, we propose a system in which a cellular network, comprising multiple users, interacts with both cloud and edge servers to process service requests. The system assumes non-orthogonal multiple access (NOMA) for user access to the radio spectrum. We model the interactions between users and servers using queuing theory, aiming to minimize the total energy consumption of users, service delivery time, and overall network operation costs. The problem is mathematically formulated as a multi-objective, bounded non-convex optimization problem. The Structural Correspondence Analysis (SCA) method is employed to obtain the global optimal solution. Simulation results demonstrate that the proposed model reduces energy consumption, delay, and network costs by approximately 50%, under the given assumptions.

Data traffic unloading method of internet of things based on mobile edge computing

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks. Simulation resultsThe energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Partial and cost-minimized computation offloading in hybrid edge and cloud systems

Nowadays, numerous mobile devices (MDs) provide nearly anytime and anywhere services, running on top of various computation-intensive applications. However, bearing limited battery, bandwidth, computing, and storage resources, MDs cannot completely execute all tasks of such applications in real-time. Cloud data centers (CDCs) possess enormous resources and energy, which can help execute tasks offloaded from MDs. Nonetheless, CDCs reside in remote sites, thereby leading to long transmission latency. In recent years, small base stations (SBSs) have emerged to offer close proximity, high bandwidth, and low latency services to their nearby and limited MDs. However, it becomes a new challenge to minimize the total system cost in a complex and heterogeneous architecture. To address it, this work proposes an energy-minimized partial computation offloading technique. First, a limited optimization problem of cost minimization for the system is formulated. Afterward, an improved hybrid meta-heuristic algorithm is developed, which synergistically combines a Metropolis acceptance criterion of simulated annealing and genetic operations. One uniqueness of the proposed algorithm is that it simultaneously determines task allocation among MDs, an SBS, and a CDC, the transmission power of MDs, and bandwidth allocation of wireless channels between MDs and SBS. Experiments with real-life tasks from Google data centers have shown that the proposed Genetic Simulated-annealing-based Particle swarm optimization (GSP) significantly achieves lower system cost and faster convergence speed than benchmark peers.

Design and assessment of a novel mobile carbon capture system: Energy and exergy analyses

In this work, we designed a mobile carbon capture (MCC) system that can effectively capture CO2 from the exhaust gas of diesel engines for applications in commercial vehicles. We present the results of energy and exergy analyses conducted on this system, which includes an energy recovery unit, a cooling unit, CO2 adsorption and desorption units, and a CO2 liquefaction unit. Three processes were considered to capture CO2: temperature vacuum swing adsorption (TVSA), vacuum pressure swing adsorption (VPSA), and vacuum swing adsorption (VSA). Energy is produced using turbines and generators, which supply the necessary power to run the MCC system, thus overcoming the limitation of mobile devices, i.e., lack of energy source. The energy requirement for each process was found to be in the order of VPSA > TVSA > VSA. In terms of specific energy consumption (SEC) and exergy efficiency, VSA was the most suitable process. As an MCC system, the VSA process consumes a small amount of energy in the designed system, and CO2 can be captured, liquefied, and stored by utilizing the energy contained in the exhaust gas. Because the TVSA process is expected to operate with less additional energy input, it can be used as an MCC system to improve the efficiency of each process unit. Our study can serve as a basis for advancing carbon capture technologies that can be implemented in commercial vehicles to reduce the greenhouse effect and help control global warming.

PERFORMANCE ANALYSIS OF HYBRID FSO / RF COMMUNICATION SYSTEM WITH RECEIVE DIVERSITY IN THE PRESENCE OF CHI-SQUARE/GAMMA TURBULENCE AND RICIAN FADING

Within mobile communication systems, the inherent limitations of mobile devices necessitate a considerable portion of signal processing to occur at the base station. The technique of multiple access at the base station, entailing the amalgamation of diverse received signals, plays a pivotal role in the reconstruction of the original signal. This investigation introduces an innovative hybrid FSO/RF system that operates via a single-hop transfer, adeptly incorporating the concept of receive diversity to effectively counteract the challenges posed by saturated atmospheric turbulence. It is important to note that the FSO channel is observed under the Gamma/Chi-Square turbulence model, while the RF channel adheres to the Rician fading model. Notably, the study also yields a closed-form expression for the outage probability of the system. The results underscore significant enhancements in comparison to conventional FSO and RF systems. Despite the demand for an advanced receiver, the proposed hybrid architecture presents substantial improvements that significantly impact the performance of the communication system. It's noteworthy that at the reception, the Switch and Stay (SSC) combining technique along with Selection Combining diversity (SC) strategies are being observed. This holistic approach not only addresses the challenges posed by atmospheric conditions but also harnesses the benefits of diversity techniques to enhance the overall reliability and performance of the communication system.

Mobile Learning Among University Students: Adoption and Application of M-Learning

The successful implementation of mobile learning is mainly impacted by learners’ attitudes to accepting and applying mobile learning systems. In the current study, an m-learning application was developed and implemented for university students, and their perspective toward the adoption and application of m-learning was investigated. This cross-sectional study was carried out in 2020. The participants were 114 university students at Abadan University of Medical Sciences. Data were collected by means of a valid questionnaire containing 42 questions in 13 subscales in addition to an open-ended question about the positive and negative aspects of the m-learning application. There was a significant relationship between acceptance and major (P=0.001), mobile usage (P=0.035), and familiarity with m-learning (P<0.001). The total mean of m-learning application acceptance was 3.95±0.32, which shows a good level. Self-efficacy, perceived ease of use, and user interface were, respectively, the most influencing factors; mobile device limitation and governmental support were the least influencing factors. “Direct communication with the teacher/instructor” and “Internet disconnection during video teaching” were among the positive and negative aspects of applying the m-learning application, respectively. The m-learning application was well adopted among the students. Mobile device limitations are a reason for the student's concerns. “Usability features” can be a point for covering mobile device limitations.

Ultralow Power Always-On Intelligent and Connected SNN-Based System for Multimedia IoT-Enabled Applications

The recent advances in artificial neural networks (ANNs) have created immense opportunities to achieve excellent results on the Internet of Things (IoT), which serve the uses of various real-time smart applications, such as in computer vision and speech recognition. However, the efficiency of ANNs comes at the expense of a huge number of computational resources. This tends to necessitate larger and wider ANNs unapplicable for embedded systems with limited hardware resources, e.g., mobile and wearable devices. To that end, biologically realistic spiking neural networks (SNNs) are first employed to build an always-on intelligent and connected integrated IoT-enabled system with ultralow power consumption, where we encode the temporal dynamic stimuli into effective, efficient, and reconstructable spike patterns to facilitate the subsequent processing. Herein, neural encoding plays a key role in faithfully describing the temporally rich patterns for downstream cognitive tasks. Therefore, a novel nonlinear piecewise latency coding approach for a fully event-driven SNN system is developed. Moreover, a surrogate postsynaptic potential kernel function is utilized to address the nondifferential nature of the spike generation scheme when using the error backpropagation learning method. The effectiveness of the proposal tandem with SNNs has been corroborated by indicative empirical results on different data sets serving cognitive tasks.

Stochastic Geometric Analysis of Handover Based on Fuzzy Logic in MIMO IoT Systems

High-density base station (BS) will bring the problem of frequent handover to mobile Internet of Things (IoT) devices. Frequent handover will lead to Quality-of-Service (QoS) problems, such as increased handover delay and reduced data transmission rate. In order to overcome the limitation of mobile IoT devices’ QoS caused by frequent handover BSs, the multiple-input–multiple-output (MIMO) systems can be used to improve the channel capacity and reduce the BS density. However, because the handover decision of mobile IoT devices will affect the data rate and network stability, how to implement the handover to reduce the impact of handover on mobile IoT devices is a problem worthy of study. Therefore, this article proposes a handover strategy based on fuzzy logic in MIMO systems, which are used to reduce the handover frequency and improve the average data rate of mobile IoT devices. First, the mobile IoT devices convert the devices’s speed, the distance from the devices to the MIMO systems, and the transmission power of the MIMO systems to fuzzy values. Then, the fuzzy values obtained in the first step are taken as the input, and the fuzzy-logic-based handover algorithm is implemented through the fuzzy logic controller. Finally, according to the results of the handover algorithm and the theoretical results about the average throughput of mobile IoT devices derived in this article, the number of antennas used to provide transmission services for IoT devices with different data rate requirements and different mobile speeds can be allocated more flexibly. Simulation results show that the proposed scheme achieves a better performance.

The relationship between the accuracy of curling athletes' duration judgment and delivery performance.

ObjectiveTime perception is a critical point for curling athletes to have in order to successfully complete interactions between themselves and their environment. Exploring the relationship between the accuracy of duration judgment and curling athletes’ performance is helpful to reveal the influencing factors on their performance and to provide a reference for the training of athletes’ delivery performance.MethodsThirty curling athletes and 30 non-athletes were recruited as participants. Using 3D modeling technology, curling videos of different situations were presented to the participants as stimulus information, and the participants were required to complete the duration judgment task. The neural activation of the participants during the entire process of duration judgment was recorded using electroencephalogram (EEG) equipment. The performance of the 30 curlers participating in the experiment was measured. Variance analyses were conducted on the collected behavioral and EEG data, and correlation and regression analyseswere conducted between behavioral data and delivery performance.ResultsThe accuracy of the distance judgment of curlers was higher than that of non-curlers (P < 0.05). In the stimulus video presentation stage, the power in the alpha band of curlers was higher than that of non-athletes (P < 0.05). In the task decision stage, the power in the alpha band of curlers was higher than that of non-athletes (P < 0.05), and the power in the theta band was higher than that of non-athletes (P < 0.05). There was a correlation between the accuracy of the curlers’ perception of specific situational time intervals and the accuracy of delivery (P < 0.05). Regression analysis results were y = 3.422 + 1.415x.ConclusionThe accuracy of curling athletes’ duration judgment is high in a specific situation. There is a correlation between the accuracy of duration judgment and delivery performance in a specific situation: the higher the accuracy of specific duration perception, the higher the performance accuracy of delivery. The cognitive strategies adopted by curlers differ from those adopted by non-athletes in the completion of duration judgment. Specifically, in a specific situation, fewer attention resources are utilized in the stimulus presentation and decision-making stages, while more memory resources are utilized in the decision-making stage to ensure higher accuracy of interval judgment. This study provides a new idea for exploring the causes of curling athletes’ excellent technical performance and provides a reference for future curling research on competition training practice. Given the limitations of mobile EEG devices in this study, future studies can measure neural activity during actual delivery preparation and execution in an environment of high ecological validity to obtain more direct evidence.

Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

SMASG: Secure Mobile Authentication Scheme for Global Mobility Network

The rapid growth of the Internet of Things (IoT) has enabled prompt services over mobile devices. The Global Mobility Network (GLOMONET) is an important global network that allows mobile users to access the Internet anywhere. Although implementing a secure mechanism in GLOMONET is a difficult and complex task due to the computational and processing limitations of most mobile devices, an authentication system is vital for secure communications among such mobile devices. In 2021, Rahmani <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed an authentication method, called the advanced mobile authentication protocol for GLOMONET (AMAPG). However, we found three serious vulnerabilities in AMAPG. First, AMAPG contains large amounts of information on the smart card of the mobile phone. Therefore, they are vulnerable to attacks that steal critical information. Second, it is susceptible to password-guessing attacks. Third, AMAPG cannot guarantee the security of future messages because attackers can steal the session key. In this study, we discuss the weaknesses of AMAPG and propose a new three-factor authentication scheme called the secure mobile authentication scheme for GLOMONET (SMASG). We performed informal and formal security analyses using ProVerif and BAN Logic on SMASG. In addition, we analyzed and compared its performance with that of the latest GLOMONET-based authentication schemes. SMASG saves an average of 93% time in user login and authentication phase.

Toward a Better Understanding of Mobile Users’ Behavior: A Web Session Repair Scheme

Using mobile devices to browse the Internet has become increasingly popular over the years. However, the risk of being exposed to malicious content, such as online scams or malware installations, has also increased significantly. In this study, we collected smartphone data from volunteer users by monitoring their use of the Web and the applications they install on their devices. However, the collected data is sometimes incomplete due to the technical limitations of mobile devices. Thus, we propose a data repair scheme to restore incomplete data by inferring missing attributes. Here, the restored data represent the browsing history of a mobile user, which can be used to determine if and how the user has been the victim of web or mobile-specific attacks to compromise their sensitive data. The accuracy of the proposed data repair scheme was evaluated using a machine learning algorithm, and the results demonstrate that the proposed scheme properly reconstructed a user’s browsing history data with an accuracy of 95%. The usability of the repaired data is demonstrated by a practical use case. The user’s browsing history was correlated with other types of data, such as received SMSs and the applications installed by the user. The results demonstrate that a user can fall victim to SMS-based phishing (SMShing) attacks, where the attacker sends an SMS message to a user to trick them install a malicious application. We also present a case of a social engineering attack, where the victim was manipulated to provide their Amazon credentials and credit card details.

Multi-Task Multi-User Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) is a new method to overcome the resource limitations of mobile devices by enabling Computation Offloading (CO) with low latency. This paper proposes a multi-user multi-task effective system to offload com-putations for MEC that guarantees in terms of energy, latency for MEC. To begin, radio and computation resources are integrated to ensure the efficient utilization of shared resources when there are multiple users. The energy consumed is positively correlated with the power of transmission and the local CPU frequency. The values can be adjusted to accommodate multi-tasking in order to minimize the amount of energy consumed. The current methods for offloading aren’t appropriate when multiple tasks and multiple users have high computing density. Additionally, this paper proposes a multi-user system that includes multiple tasks and high-density computing that is efficient. Simulations have confirmed the Multi-User Multi-Task Offloading Algorithm (MUMTOD). The results in terms of execution time and energy consumption are extremely positive. This improves the effectiveness of offloading as well as reducing energy consumption.

Work Allocating Strategy Using a Powerful Prioritized Tasks in Mobile Cloud Computing Atmosphere

Abstract: In recent times, users necessitate and expect more demanding criteria to perform computational in-depth applications on their mobile devices. Based on the mobile device limitations such as processing power and battery life, Mobile Cloud Computing (MCC) is turned to be a more attractive choice to influence these drawbacks as a mobile computation can be provided to the cloud, which is coined as Mobile computation deceive. Prevailing researches on mobile computation offloading determines offloading mobile computation to single cloud. Moreover, in real time environment, computation service can be offered by multiple clouds for every computation services. Therefore, a novel and an interesting research crisis in mobile computation offloading begins with, how to choose a computation service for every tasks of mobile computation like computation time, energy consumption and cost of using these computation services. This is also termed as multi-site computation offloading in mobile cloud computation. In this examination deceive computation to diverse cloudlets/data centres with respect to task scheduling is formulated for examination. So, a Searching algorithm known as Accelerated Cuckoo Search Algorithm based job splittingis designed to attain higher data transmission rate in the MCC. The results of the certain method outperform the prevailing methods in terms of effectual job splitting; transmission speed, Bandwidth used, execution time of a job, transmission value, through put value, buffering overhead and reduced waiting time. The simulation was carried out in Clouds environment for good output. Keywords: Computation Deceive, Mobile Cloud Computing, Scheduling, Searching Algorithm, WorkSplitting.

The Role of Mobile Devices on Online EFL Skill Courses During Covid-19 Emergency Remote Education

The recent Covid-19 based emergency remote education caught educational practitioners unprepared and caused learners to rely on any technology which offered them the best pedagogical solution. Since mobile devices such as smartphones, tablets, and notebooks are widely used in daily life, EFL learners also preferred to utilize them for online courses due to their undisputable mobility. However, using mobile devices in long online EFL courses is not without its limitations. This study aimed to examine the mobile experience during EFL skill courses in emergency remote education. 47 EFL preparatory class students who were attending online courses for 4 months with their mobile devices were asked to write a reflection for each skill course based on three main categories: Limitations of mobile devices, effects of mobile devices on in-classroom interaction, and device satisfaction rates. The initial findings addressed that EFL learners commonly used smartphones or notebooks for online courses rather than desktop PCs and tablets. Secondly, our results revealed various limitations for each EFL course such as limited screen size, overheating, microphone problems, and internet connection efficiency. On the other hand, although mobile devices were reported to have worked fine for in-class communication, device satisfaction rates regarding online courses were low, especially for smartphones.

AMAPG: Advanced Mobile Authentication Protocol for GLOMONET

Roaming is when the mobile user goes out of his/her home agent network coverage and loses its signal. Loss of coverage and signals may be limited to a remote area or may occur when mobile user leaves the country and moves to a country where his/her mobile carrier network is not available. In this case, the mobile device is in roaming mode. In this mode, mobile user through connection to a Foreign Agent can still use its home agent services if his/her authentication be successful. In such situations, the authentication mechanism plays a key and important role, where the mobile user often needs to integrate and secure roaming service over multiple foreign agents. Designing a secure mechanism in Global Mobility Network (GLOMONET) is a difficult and complex task due to the computational and processing limitations of most mobile devices, as well as the wireless nature of communication environment. Unfortunately, most of the authentication schemes that have been proposed so far to meet this goal have failed to achieve their goal. In this line, Shashidhara et al. recently reported security vulnerabilities of Xu et al.'s mobile authentication scheme, and also presented an amended version of it. This paper shows that this proposed scheme has security flaws against impersonation, traceability, forward secrecy contradiction, and stolen smart card attacks, which implies that this protocol may not be a proper choice to be used on GLOMONET. On the other hand, we propose AMAPG, as a cost-efficient remedy version of the protocol which provides desired security against various attacks and also prove its security using BAN logic. We also evaluate AMAPG's security using Scyther as a widely used formal tool to evaluate the security correctness of the cryptographic protocols.

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumed to complete a single round of FL process. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this article, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. To that end, we analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption. Finally, we present the convergence analysis of the proposed solution, and evaluate its performance against the traditional FL approach. Simulation results demonstrate the efficacy of our proposed solution approach.

Development of a Smart Technique for Mobile Web Services Discovery

Web service (WS) presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited environment. With the increasing demand for mobile WS (MWS), the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service requests. This discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile devices. Meanwhile, a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable resources. This paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking algorithm. The discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services, such as Quality of Web Service, device context, and user preferences. The WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime performance. The cosine similarity between the user request and services repository is then assessed to generate the most relevant WS. The relationships among concepts in the ontology are considered to improve the recall and precision ratio. After the WS discovery process, users can invoke and test these services in a mobile environment through a dynamic user interface. The interface of the invocation process is changed according to the WS description document. An application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

Research gaps based virtualization in mobile cloud computing

Recently, mobile computing is known as a fast-growing utilization of people's daily life. However, the main is the limited mobile devices’ resources such as processing capability, storage space and battery life. With the development of cloud computing, mobile devices’ resources are improved with the help of cloud services, which resulted an emerged technology named Mobile Cloud Computing (MCC). Although the MCC has several advantages for mobile users, it is also challenged by many critical issues like security and privacy of the mobile user's data that offloaded on the cloud’ servers and processed on the virtual machines (VMs). In virtualization, various investigations showed that malicious users are able to break down the cloud security methods by spreading their VMs in order to alter or violate the user sensitive data that executed on cloud’ VMs. This paper deeply analyzes the recent MCC based virtualization approaches and methods by criticizing them. We found out that no approach protects the data from being stolen while distributed VMs that deployed on different cloud servers exchanging data. Hence, the paper provides practical gaps related to virtualization in MCC and future perspectives.