Mobile Cloud Computing Environment Research Articles

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

A blockchain-based privacy-preserving auditable authentication scheme with hierarchical access control for mobile cloud computing

Blockchain-based authentication mode, a fundamental solution to prevent unauthorized access behavior, gradually becomes a focus in future distributed mobile cloud computing (MCC) services. However, due to the transparent and immutable characteristics of blockchain, users’ access behaviors are facing huge security and privacy threats. Storing the encrypted data on chain is an effective way to address these issues, but access permission confirmation and update in the form of ciphertext is the main bottleneck. To this end, this paper proposes a blockchain-based unified authentication and hierarchical access control scheme for the MCC environment, which provides both privacy protection and auditability. In the proposed scheme, users can access multiple MCC services with different access permissions using a single credential. To protect the privacy of both users and service providers, while still supporting auditability, the data on the public ledger is blinded using Pedersen commitments. Besides, the proposed scheme provides flexible dynamic updating in encrypted form. Theoretical analysis indicates that the proposed scheme can meet various security and privacy requirements in the MCC environment. Compared with related schemes, it has better communication efficiency. Therefore, the proposed scheme is more suitable for the actual MCC environment.

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.

Enhancing Security of Mobile Cloud Computing by Trust- and Role-Based Access Control

The tremendous development in mobile technology attracts users’ attention. Thus, the users are shifting from traditional computational devices to smartphones and tablets, and because of that, mobile devices have anticipated most of the global IP traffic. However, mobile device’s resource-constrained behaviour cannot handle the heavy computational load. Mobile cloud computing (MCC) mitigates resource-constrained issues by enabling computing resources with minimal effort. However, providing security in MCC is an obstacle due to users’ uncertain and dynamic behaviour and the explosion of online computerized data. Providing security, confidentiality, and authentication is not enough in MCC; therefore, the users need authorization. Thus, the paper designs an access control mechanism by computing the trust based on the user’s uncertain behaviour. This mechanism mitigates the malicious actions caused by authenticated users. Performance results indicate that the access control mechanism accurately detects and mitigates malicious users from the MCC environment.

Efficient Delay-Aware Task Scheduling for IoT Devices in Mobile Cloud Computing

The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.

Architecture for Fault Tolerance in Mobile Cloud Computing using Disease Resistance Approach

The mobile cloud computing (MCC) is one of the emerging fields in the distributed computing. MCC is an integration of both mobile computing and cloud computing. The limitations of the mobile devices are storage, battery and processing proficiency.These sensitive characteristics of mobile devices can be effectively handled with the introduction of cloud computing. The increasing functionality of the cloud and complexity of the applications causes resource failures in the cloud computing and it reduces the overall performance of the MCC environment. On the other hand, the existing approaches for resource scheduling in MCC proposed several architectures and they are only concentrated on the allocation of resources. The existing architectures are lack of fault tolerance mechanism to handle the faulty resources. To overcome the issues stated above, this paper proposes architecture for fault tolerance in MCC using Disease Resistance approach (DRFT). The main aim of the DRFT approach is to effectively handle the faultyVMs in the MCC. This DRFT approach utilizes the human disease resistance mechanism which is used as materials and methods in the proposed model. The DRFT is capable of identifying the faulty virtual machines and reschedules the tasks to the identified suitable virtual machines. This procedure ultimately leads to minimization of makespan value and it improves the overall performance of the scheduling process. To validate the effectiveness of the proposed approach, a series of simulations has been carried out using CloudSim simulator. The performance of the proposed DRFT approach is compared with the Dynamic group based fault tolerance approach (DGFT-approach). The makespan value of DRFT is reduced to 7% and the performance of DRFT is increased when compare to the DGFT approach. The experimental results show the effectiveness of the proposed approach.

Dynamic offloading for energy-aware scheduling in a mobile cloud

Mobile cloud computing (MCC) brings rich computational resources to mobile users, network operators, and cloud computing providers. The battery capacity of mobile devices poses several complex challenges, hence it is necessary to save energy by offloading applications to the remote cloud resources, especially when the scheduling is in a dynamic mobile cloud computing environment. To make a tradeoff decision involving energy consumption, deadline, and the system load, we proposed an iterated greedy taboo-mechanism algorithm (IGTMA) to solve the above issues in MCC environment. Compared to state-of-art approaches such as Adaptive First Come First Served (AFCFS), Minimize Execution Time (MINET), and tradeoff decisions for code offloading (TRADEOFF), the simulation experiment results show that our proposed IGTMA reduces energy consumption and enhances the number of finished jobs.

Job Offloading Mechanism for Improving the Energy Efficiency in Mobile Cloud Computing Environment

Job Offloading Mechanism for Improving the Energy Efficiency in Mobile Cloud Computing Environment

Providing impersonation resistance for biometric-based authentication scheme in mobile cloud computing service

Mobile cloud computing (MCC) is an emerging computing model with the growing demands of mobile devices by services diversity using mobile cloud technology. Authentication plays a fundamental role between the smart portable devices located in different places and the mobile cloud computing service providers in MCC. Several authors presented many authentication schemes using different cryptographic approaches for MCC in the past years. However, those schemes have not solved the impersonation attack well. To conquer this suffering and support more security functionalities, we propose an impersonation resistance for a biometric-based authentication scheme using hashing and symmetric parameter function in an MCC environment. The proposed scheme can provide mutual authentication and anonymity using the software AVISPA. The security of the proposed scheme is formally proved in the random oracle model. Regarding functionality and performance analysis, the comparative studies demonstrate that the design has a trade-off between resource consumption and security strengthens for MCC services. Therefore, the proposed scheme is more practical in MCC application.

Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.

Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing

Internet-of-Things (IoT) applications are becoming more resource-hungry and latency-sensitive, which are severely constrained by limited resources of current mobile hardware. Mobile cloud computing (MCC) can provide abundant computation resources, while mobile-edge computing (MEC) aims to reduce the transmission latency by offloading complex tasks from IoT devices to nearby edge servers. It is still challenging to satisfy the quality of service with different constraints of IoT devices in a collaborative MCC and MEC environment. In this article, we propose three constrained multiobjective evolutionary algorithms (CMOEAs) for solving IoT-enabled computation offloading problems in collaborative edge and cloud computing networks. First of all, a constrained multiobjective computation offloading model considering time and energy consumption is established in the mobile environment. Inspired by the push and pull search framework, three CMOEAs are developed by combing the advantages of population-based search algorithms with flexible constraint handling mechanisms. On one hand, three popular and challenging constrained benchmark suites are selected to test the performance of the proposed algorithms by comparing them to the other seven state-of-the-art CMOEAs. On the other hand, a multiserver multiuser multitask computation offloading experimental scenario with a different number of IoT devices is used to evaluate the performance of three proposed algorithms and other compared algorithms as well as representative offloading schemes. The experimental results of the benchmark suites and computation offloading problems demonstrate the effectiveness and superiority of the proposed algorithms.

Compromise-resilient anonymous mutual authentication scheme for n by m-times ubiquitous mobile cloud computing services

Mobile cloud computing (MCC) combines mobile devices with cloud computing for efficient extension of infrastructures and cloud services to mobile devices using the existing mobile network. However, this usually attracts security and privacy-related attacks on both mobile cloud service providers and mobile users. Aside from this, an effective approach for limiting users to a specific number of services for a specific period on the cloud server is also a major issue in the MCC environment. To securely solve these, we propose a novel compromise-resilient anonymous mutual authentication scheme that allows mobile users to subscribe for n number of services at m-period on the mobile cloud server without compromising their privacy. The security of the proposed scheme is formally verified, and its functionalities and performances are compared with other state-of-the-art schemes. The results show that the scheme has insignificant computational and communication overheads compared to other schemes, and with more functionalities such as mutual authentication, resilience to forgery, insider attacks, masquerade attacks, user anonymity, and selective subscriptions feature for the prepaid subscription.

Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

Mobile Cloud Computing (MCC) is a computing model that makes mobile devices resourceful by executing mobile applications (apps) in the cloud and storing data in cloud servers. MCC faces several security threats in both the Cloud and Mobile environments. Among several threats, malicious apps are the most threatening ones, because they can perform various malicious activities in both environments. The traditional malware detection methods may not detect new types of malware or rapidly changing malware behavior. So, there is a need to develop an accurate model for detecting malicious apps in the MCC environment. Scalability and Knowledge Reusability are challenging issues in existing detection methods. To overcome these issues, the proposed model uses an effective Ontology-based intelligent model based on app permissions to detect malware apps. This model extracts the relationship between the static features from the apps and builds an Apps Feature Ontology (AFO). A concept vector set for apps is created using the items obtained from the AFO. The most discriminant features are selected using optimization algorithms like Particle Swarm Optimization, Social Spider Algorithm (SSA), and Gravitational Search Algorithm to reduce the dimension of the concept vector set. Various classifiers are applied to the reduced set. The efficiency of the proposed approach was evaluated on datasets obtained from the AndroZoo repository and VirusShare. The experimental results reveal that the proposed model can correctly detect malware using the Random Forest (RF) classifier with SSA and achieve higher detection accuracy with the lesser fall-out and less detection speed than existing Android malware detection techniques. Specifically, RF with SSA obtained higher accuracy, F1-score, and reduction in the fall-out of 94.11%, 93%, and 3%, respectively.

A Novel Encryption and Decryption of Data using Mobile Cloud Computing Platform

As the need for super-fast mobile devices incorporating cloud computing technology continues to be the need of the hour, Mobile Cloud Computing (MCC) serves as the platform for mobile users to share data with others, store information on the cloud and also compute using the data. Over the years, the most widely preferred encryption that has proven to be reliable is Attribute Based Encryption (ABE). However, this encryption methodology requires expensive pairing operation which makes it unsuitable for MCC. As a result of this, MCC remains slow in reaching the crowd due to the challenge of resource-constrained mobile devices. To tackle this resource-constraint we propose a novel method of outsourcing operations to resource-rich cloud servers so that the constraint on resources does not hinder proper functioning of the mobile device. There are a number of advantages when data sharing is incorporated with lightweight fine-grain data sharing methodology. This method has a number of advantages such as CCA security level, resisting decryption key exposure and supporting verifiable outsourced decryption. Simulation results indicate that the performance analysis and concrete security proof is apt for MCC environment.

Enhancing Security of Health Information Using Modular Encryption Standard in Mobile Cloud Computing

Despite the numerous and noticeable inherited gains of Mobile Cloud Computing (MCC) in healthcare, its growth is being hindered by privacy and security challenges. Such issues require the utmost urgent attention to realize its full scale and efficient usage. There is a need to secure Health Information worldwide, regionally, and locally. To fully avail of the health services, it is crucial to put in place the demanded security practices for the prevention of security breaches and vulnerabilities. Hence, this research is deliberated on to provide requirement-oriented health information security using the Modular Encryption Standard (MES) based on the layered modeling of the security measures. The performance analysis shows that the proposed work excels, compared to other commonly used algorithms against the health information security at the MCC environment in terms of better performance and auxiliary qualitative security ensuring measures.

An Efficient Privacy-Aware Authentication Scheme With Hierarchical Access Control for Mobile Cloud Computing Services

In the last few years, mobile cloud computing (MCC) gains a huge development because of the popularity of mobile applications and cloud computing. User authentication and access control are two indispensable security components in the MCC environment. To the best of our knowledge, they are generally designed in different procedures. Access control can be executed after the authentication completes successfully. In order to improve efficiency, this article constructs an integrated scheme of authentication and hierarchical access control using self-certified public key cryptography (SCPKC) and the Chinese remainder theorem (CRT) for MCC environment. The proposed scheme can achieve mutual authentication while determining the access rights of mobile users without storing any access control list in the MCC service provider side. Besides, we also give a dynamic adding or deletion of MCC service provider to efficiently address potential changes in the hierarchy. The security of our proposed scheme is proved by the random oracle model. Compared with recently related multi-server authentication schemes for the MCC environment, the proposed scheme not only adds a new function of hierarchical access control but also has better computation and communication efficiencies. Therefore, the proposed scheme is more suitable for real-life MCC applications.

Dynamic Reciprocal Authentication Protocol for Mobile Cloud Computing

A combination of mobile and cloud computing delivers many advantages such as mobility, resources, and accessibility through seamless data transmission via the Internet anywhere at any time. However, data transmission through vulnerable channels poses security threats such as man-in-the-middle, playback, impersonation, and asynchronization attacks. To address these threats, we define an explicit security model that can precisely measure the practical capabilities of an adversary. A systematic methodology consisting of 16 evaluation criteria is used for comparative evaluation, thereby leading other approaches to be evaluated through a common scale. Finally, we propose a dynamic reciprocal authentication protocol to secure data transmission in mobile cloud computing (MCC). In particular, our proposed protocol develops a secure reciprocal authentication method, which is free of Diffie–Hellman limitations, and has immunity against basic or sophisticated known attacks. The protocol utilizes multifactor authentication of usernames, passwords, and a one-time password (OTP). The OTP is automatically generated and regularly updated for every connection. The proposed protocol is implemented and tested using Java to demonstrate its efficiency in authenticating communications and securing data transmitted in the MCC environment. Results of the evaluation process indicate that compared with the existing works, the proposed protocol possesses obvious capabilities in security and in communication and computation costs.

Toward Serverless and Efficient Encrypted Deduplication in Mobile Cloud Computing Environments

With the proliferation of new mobile devices, mobile cloud computing technology has emerged to provide rich computing and storage functions for mobile users. The explosive growth of mobile data has led to an increased demand for solutions that conserve storage resources. Data deduplication is a promising technique that eliminates data redundancy for storage. For mobile cloud storage services, enabling the deduplication of encrypted data is of vital importance to reduce costs and preserve data confidentiality. However, recently proposed solutions for encrypted deduplication lack the desired level of security and efficiency. In this paper, we propose a novel scheme for serverless efficient encrypted deduplication (SEED) in mobile cloud computing environments. Without the aid of additional servers, SEED ensures confidentiality, data integrity, and collusion resistance for outsourced data. The absence of dedicated servers increases the effectiveness of SEED for mobile cloud storage services, in which user mobility is essential. In addition, noninteractive file encryption with the support of lazy encryption greatly reduces latency in the file-upload process. The proposed indexing structure (D-tree) supports the deduplication algorithm and thus makes SEED much more efficient and scalable. Security and performance analyses prove the efficiency and effectiveness of SEED for mobile cloud storage services.

A QoE-Aware Strategy for Supporting Service Continuity in an MCC Environment

Mobile cloud computing (MCC) enables ubiquitous access to a diverse range of Internet multimedia services in a pay-as-you-go economic model. In an MCC environment with highly mobile users, the migration of service requests from one cloud server to another due to user movement may frequently occur. We note that when the load offered to the cloud server is increased beyond the capacity limit, particularly when migrated traffic due to user movement suddenly appears, the probability to disrupt existing services gets higher, consequently resulting in the degradation of user quality of experience (QoE). To keep the service disruption probability at an acceptable level so as to maintain a high user-perceived QoE for different classes of multimedia services, this paper proposes a QoE-aware service continuity strategy for the cloud server in an MCC environment. The strategy is based on the buffer-occupancy threshold policy that differentiates newly arriving service requests coming from the mobile users and offers effective protection for migrated service requests against traffic fluctuation in newly arriving service requests. With the proposed strategy, the cloud server can dynamically change the buffer thresholds for different classes of service requests based on the offered traffic load and the user mobility to improve resource utilization, and, most importantly, to keep the service disruption probability at an acceptable level. Besides, by taking the effect of migrated traffic into account, we develop an analytical model to study the performance of the cloud server using the proposed strategy. With the analytical model, we propose an iterative method to determine the optimal buffer thresholds that maximize resource utilization while keeping an acceptable user QoE for different classes of services.