Provable dynamic auditing in mobile cloud computing for secure storage of ehealth data

In spite of the growing usage of smart mobile devices, the inherent restrictions like resource scarcity, mobility and frequent disconnections makes it difficult to exploit its full potential. Cloud computing simplifies the deployment of large-scale distributed systems and enables advanced computational power with improved storage capabilities. The evolution of Mobile Cloud Computing (MCC) exploits the services of Cloud Service Providers (CSP) by outsourcing the local data storage and computation to the remote servers and thereby relieving the overhead incurred by mobile clients for data storage and processing. In spite of the relief, the clients no longer have control over their outsourced data which demands the purpose of ensuring the data intactness maintained by the Cloud Storage Server (CSS). The resource constrained mobile devices act as a thin client for the cloud server access and hence the computation for ensuring the proof of correctness for secure data storage becomes an overhead with existing solutions. The proposed auditing protocol introduces a trusted third party auditor (TPA) who will be in charge of verifying the correctness of data stored at cloud storage on behalf of the user without the need for retrieving the entire data. The usage of Bilinear pairing and Merkle Hash Trees guarantees Blockless verification by TPA. The verification process in the proposed method reduces the auditing overhead at the mobile device using short signatures. The proposed auditing protocol saves the mobile client's computational resource, thereby achieving cost-effectiveness to gain trust in the Cloud Storage Service.

SummaryAmong today's rapidly evolving technologies, artificial intelligence plays a significant role in making decisions by a system without any human intervention. An Emergency Management System (EMS) is a decision support system where emergencies such as tsunami, landslide, fire, cyclone, and electrical short circuits can be prevented with prior detection and can be addressed immediately in an efficient way after the emergency occurred. Automation of EMS can avoid or manage multiple emergencies, which alternatively can save lives, economy, and environment. Quality demand response with fast data transfer, error minimized computation, and effective resource utilization is very much essential while developing the EMSs. This will provide a bridge between the technology and emergency responders. Resource‐limited smart devices can be made rich in computational behavior by outsourcing their requirements such as storage, virtual servers, and web services using Mobile Cloud Computing (MCC). In this work, we have done a detailed survey on both MCC applications and EMS applications proposed in the literature, and we have also identified the design challenges handled in both MCC and EMS applications. We have presented the design challenges and possible solutions for development of EMS using MCC. We propose an architecture for building an automated EMS using MCC. Finally, we conclude the paper with specific future directions.

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

Mobile cloud computing (MCC) enables mobile devices to exploit seamless cloud services via offloading, and has numerous advantages and increased security and complexity. Penetration testing of mobile applications has become more complex and expensive due to several parameters, such as the platform, device heterogeneity, context event types, and offloading. Numerous studies have been published in the MCC domain, whereas few studies have addressed the common issues and challenges of MCC testing. However, current studies do not address MCC and penetration testing. Therefore, revisiting MCC and penetration testing domains is essential to overcoming the inherent complexity and reducing costs. Motivated by the importance of revisiting these domains, this paper pursues two objectives: to provide a comprehensive systematic literature review (SLR) of the MCC, security and penetration testing domains and to establish the requirements for penetration testing of MCC applications. This paper has systematically reviewed previous penetration testing models and techniques based on the requirements in Kitchenham's SLR guidelines. The SLR outcome has indicated the following deficiencies: the offloading parameter is disregarded; studies that address mobile, cloud, and web vulnerabilities are lacking; and a MCC application penetration testing model has not been addressed by current studies. In particular, offloading and mobile state management are two new and vital requirements that have not been addressed to reveal hidden security vulnerabilities, facilitate mutual trust, and enable developers to build more secure MCC applications. Beneficial review results that can contribute to future research are presented.

Over the last decade, many enterprises around the world migrating from traditional infrastructure to cloud resources in order to cut down operational and capital expenditure. With cloud computing, huge amount of data transactions is communicated between cloud consumers and cloud service providers. However, this cloud computing enables surplus security challenges associated to unauthorized access and data breaches. We proposed in this paper a trusted third-party auditor (TPA) model which uses lightweight cryptographic system and lightweight hashing technique to ensure data security and data integrity to audit the cloud users outsourced data from cloud service providers. With our proposed system, we solve the concern of data reliability using data correctness and verification analysis and error recovery analysis. The time complexity of our proposed system is less as compared with other TPA model. Our proposed system also shows resistance against various known cryptanalytic attacks, the performance and extensive compression technique of our proposed system are probably secure and highly proficient.

In today's fast advancing world, sensors are used in various applications to provide complete data about different objects present around us. The sensor data when integrated with Mobile Cloud Computing (MCC) is used for further computation to provide useful results advanced warning, forecasting, planning, and plethora of other applications in this uncertain world. Few works have been proposed where sensor nodes are integrated with Wireless Sensor Networks (WSNs) which give a new direction in modern research for developing new technologies which help the users for fast access of sensory data over mobile devices. Herein, a systematic literature review of both MCC and WSNs are conducted and subsequently assimilated them via an architecture which is efficient and achievable within short time‐span for a variety of applications. The MCC applications are reviewed from two main perspectives that are energy and information management. The communication aspect in MCC and WSNs has been discussed and the need for integration between the two has been justified in this work. The work done on mobility management in MCC and WSNs is reviewed. Also, the challenges in MCC‐WSNs integration along with the comprehensive analysis and findings of the review are identified. Finally, the conclusion and specific future direction of research in this area are provided.

Mobile cloud computing is a new paradigm to improve the quality of mobile services, which has drawn considerable attentions in both industrial and academic fields. In this paper, we consider the resource management and sharing problems for radio and computing resources to support mobile applications in mobile cloud computing. In such an environment, service providers can cooperate to form coalition to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory. The coalition game model efficiently reflects the scalable cooperation among the service providers for sharing their idle resources. As a result, the resources can be better utilized and the quality of service for users can be improved. The simulation results indicate that our scheme can optimize the resource utilization and significantly improve the quality of service of the users.

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloud-based frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

Cloud computing is an emerging technology in modern society. In recent years mobile users and cloud service providers are increased rapidly. In the cloud or any other distributed environment, if the users increased then security risks also increase parallelly. Many security problems are still unclear even after the advanced security measures have been taken. Security of stored data in the server is one of the most challenging issues in the cloud-based environment. Only authorized users should access the stored data of the server. Authentication plays a significant role in providing authorization. Therefore, in cloud computing, authentication is the primary measure to protect the data and the server from an unauthorized user. In this paper, an efficient authentication scheme using the smart card in the cloud environment is proposed. In the proposed scheme, with the help of a single private key, the user can access many cloud services. Also, the proposed scheme resists all security attacks. Through the simulation using the AVISPA tool, it is proved that the proposed scheme is robust and suitable for practical implementation.

The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.

As the technology is developing with a huge rate, the functionality of smartphone is also getting higher. But the smartphones have some resource constraints like processing power, battery capacity, limited bandwidth for connecting to the Internet, etc. Therefore, to improve the performance of smartphone in terms of processing power, battery and memory, the technology namely, augmented execution is the best solution in the mobile cloud computing (MCC) scenario. Mobile cloud computing works as the combination of mobile computing and cloud computing. Augmented execution alleviates the problem of resource scarcity of smartphone. To get the benefits from the resource-abundant clouds, massive computation intensive tasks are partitioned and migrated to the cloud side for the execution. After executing the task at the cloud side, the results are sent back to the smartphone. This method is called as the computation offloading. The given survey paper focuses on the partitioning techniques in mobile cloud computing.

In mobile cloud computing, data storage and its processing is done externally from the mobile devices. The mobile user can access the applications or data from cloud servers (Dinh et al., Wireless Commun. Mobile Comput. 13(8), 1587–1611 [1]). The client can store his/her multimedia data to the cloud storage server. The client may be given assurance that the access rights of data will only be restricted to authorized access. The cloud service provider assures the clients for proper security, data confidentiality, and privacy for data in their service level agreement (SLA). Clients may be given full assurance in proper privacy policies and procedures for data safety in cloud but there may be risk of unauthorized access of client sensitive data by cloud service provider or cloud attacker. Another risk is the mobility of data. Data may be transferred from one location to other locations. Frequently, data may be transferred from one place to another place so there may be security concerns of data while mobility. To secure the client’s confidential data, we propose a framework designed with modified RSA encryption technique so that client’s multimedia data may be preserved before storing on the cloud from client side.

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.

The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users' demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users' satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.

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.