Mobile Cloud Architecture Research Articles

Trust models for efficient communication in Mobile Cloud Computing and their applications to e-Commerce

ABSTRACTManaging the large dimensions of data processed in distributed systems that are formed by datacentres and mobile devices has become a challenging issue with an important impact on the end-user. Therefore, the management process of such systems can be achieved efficiently by using uniform overlay networks, interconnected through secure and efficient routing protocols. The aim of this article is to advance our previous work with a novel trust model based on a reputation metric that actively uses the social links between users and the model of interaction between them. We present and evaluate an adaptive model for the trust management in structured overlay networks, based on a Mobile Cloud architecture and considering a honeycomb overlay. Such a model can be useful for supporting advanced mobile market-share e-Commerce platforms, where users collaborate and exchange reliable information about, for example, products of interest and supporting ad-hoc business campaigns

Processing geo-dispersed big data in an advanced mapreduce framework

Big data has emerged as a new era of information generation and processing. Big data applications are expected to provide a lot of benefits and convenience to our lives. Cloud computing is a popular infrastructure that has the resources for big data processing. As the number of mobile devices is fast increasing, mobile cloud computing is becoming an important part of many big data applications. In this article, we propose a novel MapReduce-based framework to process geo-dispersed big data in mobile cloud architecture. The proposed framework supports simple as well as complex operations on geo-dispersed big data, and uses various data aggregation schemes to satisfy different application requirements.

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.

Real-Time City-Scale Taxi Ridesharing

We proposed and developed a taxi-sharing system that accepts taxi passengers’ real-time ride requests sent from smartphones and schedules proper taxis to pick up them via ridesharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ridesharing and get compensated if their travel time is lengthened due to ridesharing; taxi drivers will make money for all the detour distance due to ridesharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/∼sma/ridesharing) in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system demonstrated its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.

Recognition of familiar people with a mobile cloud architecture for Alzheimer patients

Purpose: This article aims to the evaluation of a prototypal assistive technology for Alzheimer’s disease (AD) patients that helps them to remember personal details of familiar people they meet in their daily lives. Method: An architecture is proposed for a personal information system powered by face recognition, where the main AD patient’s interaction is performed in a smart watch device and the face recognition is carried out on the Cloud. A prototype was developed to perform some tests in a real-life scenario. Results: The prototype showed correct results as a personal information system based on face recognition. However, usability flaws were identified in the interaction with the smart watch. Conclusions: Our architecture showed correct performance and we realized that it could be introduced in other fields, apart from assistive technology. However, when being targeted to patients with dementia some usability problems appeared, such as difficulties to read information in a small screen or take a proper photo. These problems should be addressed in further research.Implications for RehabilitationThis article presents a prototypal assistive technology for Alzheimer’s disease (AD) patients.It targets AD patients to recognize their familiars, especially in medium-advanced stages of the disease.Analysing pictures taken by a smart watch, which the patient carries, the person in front is recognized and information about him is sent to the watch.This technology enables patients to have all the information of any close person, as a remainder, easing their daily lives, improving their self-esteem and stimulating the patient with novel technology.

Energy efficiency based on joint data packet fragmentation and cooperative transmission

Energy efficiency (EE) is a key requirement for the design of short-range communication network. In order to alleviate energy consumption (EC) constraint, a novel layered heterogeneous mobile cloud architecture is proposed in this paper. Based on the proposed layered heterogeneous mobile cloud architecture, we establish an appropriate energy consumption model, and design an energy efficiency scheme based on joint data packet fragmentation and cooperative transmission and analyze the energy efficiency corresponding to different packet sizes and the cloud size. Simulation results show that, when all nodes of the cloud are accessing the same size of data packet fragmentation, the proposed layered heterogeneous mobile cloud architecture can provide significant energy savings. The results provide useful insights into the possible operation of the strategies and show that significant energy consumption reductions are possible.

Mobile-cloud assisted video summarization framework for efficient management of remote sensing data generated by wireless capsule sensors.

Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.

Modeling decoupled mobile cloud computing using Mobile UNITY

SummaryA mobile cloud computing paradigm supports different resource‐intensive mobile applications by leasing various mobile cloud services, thus helping these applications to overcome the inherent shortcomings of mobile ∕ portable devices of end users. However, mobile cloud services in existing architectures become tightly coupled with mobile applications during servicing, which is highly undesirable because of the dynamic nature of the mobile cloud computing paradigm. A new mobile cloud architecture, where the interactions between mobile applications and mobile cloud services are decoupled during service invocation and delivery phases, is proposed in this paper. The tuple space model is used in this architecture to decouple such interactions, thereby improving adaptability and robustness of mobile cloud computing. This paper also suggests an approach of modeling and formally verifying the correctness of the proposed architecture, which also validates its adaptability and robustness during interactions between mobile applications and mobile cloud services. The modeling and verification are carried out using the Mobile UNITY model. Copyright © 2014 John Wiley & Sons, Ltd.

Task Scheduling Based on Degenerated Monte Carlo Estimate in Mobile Cloud

Mobile cloud computing, which comes up in recent years, is a new computing paradigm. It enables people to access remote clouds by mobile device, even to build mobile microcloud(MuCloud) with mobile device to provide lightweight service. Despite extensive studies of task scheduling in wired cloud, effective scheduling in mobile cloud still remains challenges:1) Unreliable wireless connection and dynamic join and quit of MuCloud often result in decreased reliability of scheduling; 2) As the process capacities of wired clouds and MuClouds vary greatly, it is hard to achieve load balancing; 3) During moving, tasks, such as traffic navigation, may be scheduled consecutively by mobile users as space-time changes. Such application scenarios often incur makespan accumulation which impairs user experience, even causes system crash. Our work aims at such problems. We firstly illustrate the reason for selection of makespan and load balancing as two key performance indicators for task scheduling in the proposed architecture of mobile cloud which integrates MuClouds. Then after introduction to Monte Carlo method, degenerated Monte Carlo estimate is defined and a scheduling algorithm based on degenerated Monte Carlo estimate (DMCE) is presented. With extensive simulation experiments, the two above-mentioned indicators of task scheduling using different algorithms including DMCE, Max-Min, Min-Min and IGA are compared and evaluated. Accumulative effect and relative load are introduced to measure scheduling performance. The experimental results show that: 1)Compared with other algorithms, DMCE achieves smallest makespan on average when scheduled respectively; 2) DMCE has least accumulative effect when task sets scheduled consecutively, which makes makespan of a task set hardly relevant to the order of scheduling; 3)Among these algorithms, DMCE outperforms others in keeping relative load balancing by assigning tasks to clouds in proportion to each cloud’s process capacity.

A Survey of Mobile Cloud Computing Application Models

Smart phones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smart phones are resource-constrained devices with limited computation power, memory, storage, and energy. Fortunately, the cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Therefore, researchers envision extending cloud computing services to mobile devices to overcome the smartphones constraints. The challenge in doing so is that the traditional smartphone application models do not support the development of applications that can incorporate cloud computing features and requires specialized mobile cloud application models. This article presents mobile cloud architecture, offloading decision affecting entities, application models classification, the latest mobile cloud application models, their critical analysis and future research directions.

Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

Mobile cloud computing, with its promise to meet the urgent need for richer applications and services of resource-constrained mobile devices, is emerging as a new computing paradigm and has recently attracted significant attention. However, there is no clear definition and no well defined scope for mobile cloud computing due to commercial hype, and diverse ways of combining cloud computing and mobile applications. This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages. Specifically, we introduce three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending. Considering the energy bottleneck and user context of mobile devices, we discuss the research challenges and opportunities of introducing cloud computing to assist mobile devices, including energy-efficient interactions, virtual machine migration overhead, privacy, and security. Moreover, we demonstrate three real-world applications enabled by mobile cloud computing, in order to stimulate further discussion and development of this emerging field.

CACTSE: Cloudlet aided cooperative terminals service environment for mobile proximity content delivery

Along with the rapid development of communications, the Internet, and smart terminals, mobile Internet has become a hot topic with both opportunities and challenges. In this article, a new perspective on edge content delivery service for mobile Internet is described, based on cooperating terminals. A mobile cloud architecture named Cloudlet Aided Cooperative Terminals Service Environment (CACTSE) is proposed as an edge network service environment. The Service Manager (SM), a cloudlet like module, is introduced into the local service domain in order to manage the in-domain terminals and help coordinate the content delivery requests for better bandwidth efficiency as well as user experience. The reference model is presented in this article with architecture and mechanism design. Moreover, the research progress and potential technology trends of CACTSE are analysed based on the related R&D directions.