Mapping the quantity, quality and structural indicators of Asian (48 countries and 3 territories) research productivity on cloud computing

PurposeThe purpose of this study was to map the quantity (frequency), quality (impact) and structural indicators (correlations) of research produced on cloud computing in 48 countries and 3 territories in the Asia continent.Design/methodology/approachTo achieve the objectives of the study and scientifically map the indicators, data were extracted from the Scopus database. The extracted bibliographic data was first cleaned properly using Endnote and then analyzed using Biblioshiny and VosViewer application software. In the software, calculations include citations count; h, g and m indexes; Bradford's and Lotka's laws; and other scientific mappings.FindingsResults of the study indicate that China remained the most productive, impactful and collaborative country in Asia. All the top 20 impactful authors were also from China. The other most researched areas associated with cloud computing were revealed to be mobile cloud computing and data security in clouds. The most prominent journal currently publishing research studies on cloud computing was “Advances in Intelligent Systems and Computing.”Originality/valueThe study is the first of its kind which identified the quantity (frequencies), quality (impact) and structural indicators (correlations) of Asian (48 countries and 3 territories) research productivity on cloud computing. The results are of great importance for researchers and countries interested in further exploring, publishing and increasing cross country collaborations related to the phenomenon of cloud computing.

Proxy-3S

Virtualization plays a key role in the area of Mobile Cloud Computing (MCC). In MCC, the protection of distributed VMs and mobile users’ sensitive data, in terms of security and privacy, is highly required. This paper presents a novel cloud proxy known as Three Policies Secure Cloud Proxy (Proxy-3S) that combines three security policies: VM users’ access control, VMs’ secure allocation and VMs’ secure communication. The proposed approach aims to keep the distributed VMs safe in different servers on the cloud. It enhances the access authorization to permit intensive distributed application tasks on the cloud or mobile devices while processing and communicating private information between VMs. Furthermore, an algorithm that enables secure communication among distributed VMs and protection of sensitive data in VMs on the cloud is proposed. Several experiments were conducted using a real-world healthcare distributed application. The experiments achieved promising results for high-level data protection and good efficiency rating compared to existing works.

Review of Mobile Cloud Computing in Healthcare for Diabetics Patients Using Machine Learning Techniques

The number of chronic diseases such as diabetes, cancer, heart disease, and others is fast increasing in our daily lives. The disadvantages of the traditional healthcare system are becoming more prevalent. One of the most important is that healthcare is only offered in hospitals. No one has access to it and no one is monitoring it. Patients' information is securely acquired from the hospital, with their consent, and monitored on a regular basis using their smart phones in mobile cloud computing. On a daily basis, a real-time mobile cloud health monitoring system is used. The patient's specifics concerning various metrics for data collection, such as blood glucose level, high/low blood pressure, high cholesterol, oxygen level, and so on, are being monitored. Diabetic patients are tracked via mobile cloud-IoT and certain wearable health tracking devices and sensors. Doctors will review the individuals' medical records and make recommendations for improving their health. In the future, it will aid in the control or recovery of diabetics. To provide improved security and performance, the proposed system can leverage advanced encryption techniques in conjunction with a machine learning classifier. Keyword: Predictive analytics; Prediction models; Machine learning; Classifications, Healthcare, Diabetes, Blood Glucose and privacy module.

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

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

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.

Data Storage Security in Mobile Cloud Computing (MCC) using Improved Blowfish Algorithm

Mobile cloud computing (MCC) combines cloud computing and mobile computing to deliver vast computational resources to mobile consumers, network operators, and cloud computing providers. You may access your data from anywhere in the globe using any mobile device that is linked to the Internet. Cloud computing provides access to data in real-time whenever and wherever want. Any conventional mobile device can benefit from MCC's infrastructure, computational capacity, software, and platform services. Network security, web application security, data access, authentication, authorization, data confidentiality, and data breach are all concerns of MCC's security. Because mobile devices lack sufficient storage and processing power, their data storage capacity is limited. Users of mobile devices may inadvertently provide sensitive information over the network or through the application. Therefore, data security is the main concern for mobile device users. The objective of this paper is to find a solution that can enhance technical requirements with relation to user’s data security and privacy in mobile cloud computing. To achieve this improved blowfish encryption algorithm is used to encrypt each user’s data security and where the shared secret key is hash down using message digest called secured hash function. Hashing can increase the integrity and privacy of user data. The proposed algorithm is evaluated with a normal blowfish algorithm and 3DES with different parameters. Improved blowfish algorithm shows better performance than normal blowfish algorithm and 3DES. In this work, we have developed web-based application where the Amazon MySQL RDS database is used for data storage.