Toward Serverless and Efficient Encrypted Deduplication in Mobile Cloud Computing Environments

Mobile Cloud Computing is a combination of Mobile Computing, Cloud Computing and wireless networks to convey rich computational resources to mobile users, network operators, as well as cloud computing providers. Nowadays, the market of mobile devices and its applications are growing at an alarming speed. In general, single application for a specific purpose is not compatible to different operating systems. So the developer has to develop various versions of application which is compatible to different platforms. In order to overcome the drawback of compatibility and interoperability issues, HTML5-based mobile applications are built by using standard web technologies. HTML5-based mobile applications support same version of application in different platforms, but these applications are vulnerable to attacks because of the data and code being fused together. A new form of Code Injection Attack found in this type of mobile applications inherits the property of XSS attack and also uses many channels to inject malicious code such as Contact, SMS, WIFI, NFC, Barcode etc. This allows the attacker to inject malicious code to exhaust all the resources of the victim. Therefore, security is the major issue which impedes the development of Mobile Cloud Computing. This paper surveys the malicious code injection attacks in Mobile Cloud Computing environment and the possible solutions.

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

The popularity of mobile devices has been growing at a very fast rate and it is evident from the fact that it is possessed by almost each and every person and some may have even more than a single mobile device. MCC helps in computation and running of various complex applications on the mobile device and also offloads to the cloud when it requires lot of resources for computation or storage purposes. However, as energy is limited in the mobile device, processing of complex applications using big data is a challenge that needs to be addressed using energy efficient architectures. In this work, we mainly focuses on identifying energy-aware issues for handling big data in Mobile Cloud Computing (MCC) environment and their current solutions. Also, we have included the review of few techniques available to handle big data in mobile devices. This chapter will also include a brief discussion of techniques available to process big data in MCC in an energy efficient manner. Finally, we conclude with an analysis of identified issues for handling big data in MCC and future scope of research.

The rapid development of information & communication technology has led to the wide popularity of mobile devices, which have helped to improve business efficiency and enabled simple mobility as small and light devices and convenience of being available anytime, anywhere for cyber-physical-social big data. There are many ongoing studies on mobile cloud computing (MCC) to overcome the limited computing capability and storage capacity and internal battery limitation by taking advantage of the popularity of mobile devices for the processing cyber-physical-social big data. MCC consists of service-oriented architecture, agent-client architecture, and collaborative architecture, with job splitting and allocation as the critical factor. As such, job allocation techniques considering the performance resources of mobile devices have been studied. Note, however, that there is a problem of job reallocation due to continuous battery consumption, since the studies consider only the performance resources of mobile devices at the time of job allocation or take into account the performance resources and remaining battery power only. This paper proposes the job allocation mechanism (JAM) for battery consumption minimization of cyber-physical-social big data processing in MCC, which continuously reflects the battery consumption rate to process jobs with mobile devices only without an external cloud server in a collaborative architecture-based MCC environment. JAM allocates jobs considering the periodic measurement of battery consumption and surplus resource to minimize the problem of job reallocation due to battery rundown of the mobile devices. This paper designs and implements a system for verifying JAM and demonstrated that the job processing speed increased in an MCC environment for cyber-physical-social big data.

Introduction/Importance of Study: Cloud computing is a model facilitating ubiquitous, convenient, and on-demand network access to a shared pool of computing resources, offering flexibility, reliability, and scalability. Objective:This study investigates authentication mechanisms in Mobile Cloud Computing (MCC) to enhance security and address emerging challenges.Novelty statement:Our research contributes novel insights into authentication protocols in MCC, offering solutions to security issues not previously addressed.Material and Method:The study analyzed various authentication mechanisms in MCC using NIST evaluation criteria, considering their alignment with security needs and resource constraints.Result and Discussion:Our findings underscore the importance of selecting authentication mechanisms that balance security and performance in MCC environments, highlighting the need for ongoing innovation in security measures.Concluding Remarks:The study emphasizes the significance of robust authentication protocols tailored to MCC's unique security requirements for ensuring data integrity and privacy.

Mobile Cloud Computing (MCC) fuels innovation in Mobile Computing and opens new pathways between mobile devices and infrastructures. There are several issues in MCC environment as it integrates various technologies. Among all issues, security lies on the top where many users are not willing to adopt the cloud services. This paper focuses on the authentication. The objective of this paper is to provide a mechanism for authenticating all the entities involved in accessing the cloud services. A mechanism called Key Based Mutual Authentication (KBMA) is proposed which is divided into two processes namely registration and authentication. Registration is a one-time process where the users are registered for accessing the cloud services by giving the desired unique information. Authentication process is carried out mutually to verify the identities of Device and Cloud Service Provider (CSP). Scyther tool is used for analysing the vulnerability in terms of attacks. The result claims show that the proposed mechanism is resilient against various attacks.

The combination of Mobile computing and Cloud computing gave rise to a new computing paradigm called Mobile Cloud Computing (MCC) that gives the flexibility to access information and computing resources anywhere anytime. In many applications, mobile nodes capture images/video clips and exchange with other mobile peers (on demand) and use local access points for efficient smooth distribution of information in the wireless network. In such a situation, a three-way data management and dissemination technique is helpful because it provides both data management and distribution at different levels of granularity. The main motivation of this paper is that we seek a balance between accessing information from a remote cloud server, at the cost of increasing latency, and accessing data from other mobile hosts, at a cost of power, bandwidth and increased traffic. To manage this balance, we propose a layered architecture supported by mobile hosts, access points and cloud together for efficiency. The MCC architecture described in this paper also provides fault tolerance in case one of the mobile nodes fails or gets disconnected. In this paper, we discuss a layered architecture for MCC and present a data pre-distribution scheme for efficient sharing of data among potential users in MCC environment. We also propose update propagation and cache replacement policies in MCC. In addition, we also report complexity analysis for cache accesses by mobile nodes using the proposed architecture in terms of communication messages.

Providing mobile cloud services requires seamless integration between various platforms to offer mobile users optimum performance. To achieve this, many fundamental problems such as bandwidth availability and reliability, resource scarceness, and finite energy must be addressed before rolling out such services. This chapter aims to explore technological challenges for mobile cloud computing in the area of resource management focusing on both parts of the infrastructure: mobile devices and cloud networks. Starting with introducing mobile cloud computing, it then stresses the importance of resource management in the operation of mobile cloud services presenting various types of resources available for cloud computing. Furthermore, it examines the various types of resource management techniques available for mobile clouds. Finally, future directions in the field of resource management for mobile cloud computing environment are presented.

Mobile cloud computing (MCC) is the availability of cloud computing services in a mobile environment. By providing optimal services for mobile users, MCC incorporates the elements of mobile networks and cloud computing. In mobile cloud computing, all the data and complicated computing modules can be processed in clouds, and mobile devices do not need a powerful configuration like CPU speed, memory capacity, etc. However, the mobile devices are facing up with many struggles in their resources (e.g., battery life, storage, and bandwidth) and communications (e.g., privacy, mobility, and security). These challenges have a significant effect on the improvement of service qualities. In this paper, a literature survey on the MCC and security issues in MCC has presented.

Mobile cloud computing (MCC) allows smart mobile devices (SMD) to access the cloud resources in order to offload data from smartphones and to acquire computational services for application processing. A distinctive factor in accessing cloud resources is the communication link. However, the communication links between SMD and cloud resources are weak, which allows intruders to perform malicious activities by exploiting their vulnerabilities. This makes security a key challenge in the MCC environment. Several intrusion detection and response systems (IDRSs) are adapted to address the exploitation of vulnerabilities that affect smartphones, communication links between cloud resources and smartphones, as well as cloud resources. In this article, we discuss the cloud-based IDRS in the context of SMD and cloud resources in the MCC infrastructure. The stringent security requirements are provided as open issues along with possible solutions. The article aims at providing motivations for researchers, academicians, security administrators, and cloud service providers to discover mechanisms, frameworks, standards, and protocols to address the challenges faced by cloud-based IDRS for SMD.

Recent trends in Cloud Computing have further stimulated the popularization of mobile device industry, creating a novel computing paradigm called Mobile Cloud Computing (MCC). MCC takes advantage of the powerful computation and storage capability of cloud servers by offloading heavy computing or storing tasks from mobile devices to cloud servers to keep a thin frontend on the mobile devices. Such benefit is important to MCC leveraging various sensors equipped in modern mobile devices. We explore the sensing capability of MCC and design an application framework that enables a class of exciting mobile applications to be developed in the sensing-oriented MCC environment. A critical issue in such an environment is accountability. We provide a comprehensive analysis of the accountability issues in this new computing context and show how the accountability function is integrated into the application framework.

In mobile cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, which requiring large-scale cloud data centers. The mobile cloud computing enables customers to outsource the large and complex tasks to the cloud data centers for the execution and result generations. To provide resources such as CPU, RAM, memory, and bandwidth cloud data centers host incoming tasks, due to the large data centers that provide the basic resources for the host operations, and it consumes a huge amount of energy, which results in very high operational costs. In order to improve the energy efficiency and SLA violation of a cloud data center, energy-aware methods for overloaded host detection and VM selection from an overloaded host are needed. Cloud computing research community felt the need to provide or improve the energy-efficient solutions that reduce the impact of the issues. In this paper, we have shown many existing algorithm for energy efficiency and SLA violation reduction.

In mobile cloud computing systems, cloud computing has a significant impact on wireless networks. Cloud computing and wireless networks have traditionally been addressed separately in the literature. In this paper, we jointly study the operations of cloud computing and wireless networks in mobile computing environments, where the objective is to improve the end-to-end performances of cloud mobile media delivered through mobile cloud computing systems. Unlike most existing studies on wireless networks, where only the spectrum efficiency is considered, we consider not only the spectrum efficiency in wireless networks but also the pricing information in the cloud, based on which power allocation and interference management in the wireless networks are performed. We formulate the problems encountered in the operations of mobile cloud computing environments, including determining the price to charge for media services, resource allocation, and interference management, as a Stackelberg game model. Moreover, we extend this game model with multiple players through network virtualization technology, and adopt the replicator dynamics method to solve the evolutionary game between the different groups of small cells. Furthermore, a backward induction method is used to analyze the proposed Stackelberg game. Simulation results are presented to show the effectiveness of the proposed techniques.

Mobile cloud computing offers computing, storage, and memory resources to mobile users through its high computing cloud servers. Mobile services are facilitated with efficient resource provisioning in the cloud. However, due to its inherent nature such as disconnections and limited battery life mobile users experience poor quality of service (QoS) in their connectivity with the cloud. This paper proposes an efficient request state aware resource provisioning technique that provisions the resources considering the current context information viz., battery, and connection quality of the mobile client. Various state viz., Accepted, Submitted and Running, Submitted and Paused, Resumed, Fulfilled, Rejected and Exit has been used to monitor the Client’s state. Besides, an intelligent resource capacity prediction technique based on a random forest algorithm has been incorporated to predict the future resource capacity requirement and schedule the client’s job in the cloud. The proposed technique has been implemented using CloudSim and analyzed using a mobile cloud storage dataset. Performance analysis proves that the proposed technique outperforms the state of art techniques in terms of waiting time, deadline violation, and accuracy of the prediction technique.

Mobile cloud computing (MCC) combines cloud computing and mobile internet to improve the computational capabilities of resource-constrained mobile devices (MDs). In MCC, mobile users could not only improve the computational capability of MDs but also save operation consumption by offloading the mobile applications to the cloud. However, MCC faces the problem of energy efficiency because of time-varying channels when the offloading is being executed. In this paper, we address the issue of energy-efficient scheduling for wireless uplink in MCC. By introducing Lyapunov optimization, we first propose a scheduling algorithm that can dynamically choose channel to transmit data based on queue backlog and channel statistics. Then, we show that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in a channel-aware MCC system. Simulation results show that the proposed scheduling algorithm can reduce the time average energy consumption for offloading compared to the existing algorithm.