In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a ph...
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In the recent years, cloud computing frameworks such as Amazon Web Services, Google AppEngine and Windows Azure have become increasingly popular among IT organizations and developers. Simultaneously, we have seen a phenomenal increase in the usage and deployment of smartphone platforms and applications worldwide. This paper discusses the current state of the art in the merger of these two popular technologies, that we refer to as mobile Cloud Computing (MCC). We illustrate the applicability of MCC in various domains including mobile learning, commerce, health/wellness and social medias. We further identify research gaps covering critical aspects of how MCC can be realized and effectively utilized at scale. These include improved resource allocation in the MCC environment through efficient task distribution and offloading, security and privacy.
mobile computation offloading enables resources-constrained mobile devices to offload their computation intensive tasks to other available computing resources for local energy savings. In this paper, we study the offl...
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ISBN:
(纸本)9781538620700
mobile computation offloading enables resources-constrained mobile devices to offload their computation intensive tasks to other available computing resources for local energy savings. In this paper, we study the offloading decision and task scheduling issues when multiple serving vehicles (SVs) can be utilized in vehicular network. The overall energy consumption of users with Dynamic Voltage Scaling (DVS) technology is minimized subject to the delay constraint of each task. For the ideal cases, the assignment and scheduling in an offline style are firstly formulated as a mixed-integer nonlinear programming (MINLP) problem, and the optimal solution is derived based on dynamic programming. After that, two online strategies, Energy Consumption Minimization (ECM) based low-complexity assignment, and Resource Reservation (RR) assignment are also proposed. Simulation results demonstrate the improvements in energy saving when the proposed strategies incorporated with the DVS technology are adopted.
In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computationoffloading scheme in wireless body area networks (WBANs), where each sensor firstly rec...
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ISBN:
(纸本)9781728182988
In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computationoffloading scheme in wireless body area networks (WBANs), where each sensor firstly receives radio frequency (RE) energy and then offloads body physiological computation tasks via low-power BackCom to the access point (AP) for edge computing. Specifically, only rough first and second-order moment statistics are obtained for the estimation errors of CSI. Based on all the possible distributions of CSI errors, we aim to minimize the end-to-end system latency by jointly optimizing the power of the signal transmitted by the AP and the power reflection coefficient with energy chance restrictions and throughput requirement constraints. In order to solve the proposed non-convex chance-constrained optimization problem, we approximate chance constraints by the conditional value-at-risk (CVaR), and apply an efficient block coordinate descent (BCD) algorithm to solve it. Simulation results are provided to corroborate that the proposed method outperforms other methods for the non-Gaussian mismatch.
mobile computation offloading (MCO) is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices to resource-rich devices (such as a cloud server) via wireless access. For ...
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ISBN:
(纸本)9781538661192
mobile computation offloading (MCO) is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices to resource-rich devices (such as a cloud server) via wireless access. For applications that are time sensitive, offloading to nearby cloudlets is preferred, compared to offloading to a remote cloud server, in order to save the data transmission delay. On the other hand, the limited computing capabilities and the wireless transmission conditions to access the cloudlet servers can both affect the offloading performance, especially when multiple users are competing for offloading services. In this paper, we study joint computationoffloading and radio resource allocations in small cell cellular systems, where cloudlet servers are colocated at the base stations. Our objective is to minimize the total energy consumption of the system, for both data transmissions and task executions, subject to the hard latency requirements of the applications. The problem is first formulated as a mixed integer nonlinear optimization problem, and then decomposed into multiple power allocation subproblems and an offloading decision subproblem. The power allocation subproblems are non-convex, which are reformulated and solved iteratively. Their results are fed into the offloading decision subproblem, which then becomes a linear integer (binary) problem, and can be converted into a matching problem and solved using a modified Kuhn-Munkres (K-M) algorithm. Simulation results demonstrate that the joint optimization can significantly improve the offloading efficiency, compared to other resource allocation methods.
Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monito...
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Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users' needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user's smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase
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