Pre-distribution scheme for data sharing in mobile cloud computing

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.

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.

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.

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.

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.

Encouraging the mobile devices to contribute their idle resources is critical to promote the quality of service of Mobile Cloud Computing (MCC), especially when the remote cloud service is absent or the access bandwidth is limited. In order to achieve this goal, a carefully devised MCC architecture with appreciate incentive mechanismis needed. In this paper, inthe view point of cooperative game, an incentive structure based on Shapley Value is brought forward for Hybrid Local Mobile Cloud Model-with stabilityanalysis. Moreover, the implementation issues of the incentive structure and its experimental evaluation are presented.

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 (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.

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.

Following the growth of mobile device applications in the past years, mobile computing and cloud computing are suggesting a new computing paradigm. Demands for computing capability regarding potential technologies that are provided as mobile service are also growing. Meanwhile, necessity of Mobile cloud computing (MCC) is increasing due to the problems of mobile devices, such as resource poverty, battery constraint. However, clear definition is required as the concept of MCC is still ambiguous. Currently, it is necessary to classify the configuration methods of the resource-providing infrastructure environment of MCC. This paper provides a comprehensive overview of MCC and proposes a basic scheme. We suggest instance MCC (iMCC), which is a framework implementing offloading for the purpose of achieving efficient computing process and service provision. iMCC includes improved resource allocation method in MCC infrastructure as well as efficient offloading method. Finally, we summarize the conclusion and discuss future research.

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

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.

Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile cloud (MC) computing can meet the demands of even the most computationally and resource‐intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a trade‐off. To counter this problem, we propose a data‐driven technique that (1) does instrumentation by allowing class‐, method‐, and hybrid‐level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade‐off. Our experimental evaluation considers two MC hybrid Android‐based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations. The analysis of our results shows that from the measured data, Pareto‐efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth.

Business process modelling is an important part of system design. When designing or redesigning a business process, stakeholders specify, negotiate, and agree on business requirements to be satisfied, including non-functional requirements that concern the quality of the business process. This thesis addresses the question of how to specify and compute the quality of a business process, given the model that stakeholders use. The motivation for this thesis is the increasing importance of the quality of business processes. Knowing the quality of specific business processes enables stakeholders to judge if these processes need improvement. Knowing the quality of the constructs of those processes (viz., events, inputs, activities, and outputs) and the way they are structured enables a more detailed analysis of their shortcomings and provides a basis for the design of improvements. The research challenge of this thesis is grounded in the assumption that: “Organisations need an appropriate means to effectively compute achievement of their goals and objectives by their business processes.” Given this challenge, the main research question on which this thesis focuses is: “Can the quality of a business process be computed quantitatively at different levels of granularity?” The research objective is: “To develop frameworks, factors, and metrics for computing non-functional requirements (quality) of business processes quantitatively at different levels of granularity.” The outcomes of this thesis are: 1) BPIMM, a language-independent business process integrating meta-model, based on the concepts of seven mainstream business process modelling languages: BPMN, EPC, RAD, UML AD, SADT, IDEF0, and IDEF3. 2) BPC-QC (Business Process Concept - Quality Computation), an approach to quality computation at the lowest level of granularity of a business process. The approach consists of: i. BPC-QEF (Business Process Concept - Quality Evaluation Framework), a language-independent generic framework and algorithm to compute the quality of the constructs of a business process: event, input, activity, and output. ii. A set of business process quality dimensions and factors. The following quality dimensions are distinguished: performance, efficiency, reliability, recoverability, permissibility, and availability. Each dimension categorises different quality aspects in terms of factors. A non-exhaustive set of sixteen quantitative factors is provided. iii. Quality metrics for each of the quality factors, to facilitate a quantitative computation of the quality of a specific construct of a business process. 3) BP-QC (Business Process - Quality Computation), an approach to compute the quality at the highest level of granularity of a business process. The approach consists of: i. BP-CQCF (Business Process - Compositional Quality Computation Framework), a language-independent generic framework and algorithm to compute the quality of a business process as a whole, given the quality of its constructs. ii. A set of generic business process modelling patterns to decompose a business process into more succinct parts, namely: sequential, parallel with synchronisation, exclusive, inclusive, simple loop, and complex loop. iii. A set of over one hundred computational formulae. For each combination of modelling pattern and a quality factor, there is a formula to compute the quality. 4) AAV (Approach to Application and Validation), an evaluation plan to evaluate BPIMM, BPC-QC and BP-QC in practice, together with expert stakeholders. The plan consists of the units of measure, a measurement model, and a case study procedure. To evaluate the applicability of the contributions of this thesis to real world business needs, four case studies have been conducted in different environments: a Dutch educational institution, a global financial institution, an international financial service provider, and a Dutch research project on crisis management. Each of these case studies concerns a different, single business process. This thesis shows that: 1) A quality computation approach can be adopted independent of a business process modelling language. 2) Quantitative quality factors can be introduced specifically for the constructs of a business process. 3) Quantitative metrics and computational formulae can be developed for specific quality factors, allowing the computation of different aspects of the quality of a business process quantitatively at different levels of granularity. 4) An evaluation plan can be developed to evaluate the applicability of the contributions of this thesis (viz., BPIMM, BPC-QC, and BP-QC). The contributions of this thesis are designed to be beneficial to the areas of business and management, requirements engineering, software engineering, and business process modelling. In the areas of requirements engineering and software engineering, these contributions are intended to help practitioners to consider non-functional requirements at the earliest stage. In the area of business process modelling, information systems, service computing, and cloud computing, the contributions can be used for quality-driven modelling, design, and redesign. To conclude, knowing the quality value of a business process at different levels of granularity provides a basis for its improvement.