2,781 research outputs found
Mobile Cloud Computing: A Review on Smartphone Augmentation Approaches
Smartphones have recently gained significant popularity in heavy mobile
processing while users are increasing their expectations toward rich computing
experience. However, resource limitations and current mobile computing
advancements hinder this vision. Therefore, resource-intensive application
execution remains a challenging task in mobile computing that necessitates
device augmentation. In this article, smartphone augmentation approaches are
reviewed and classified in two main groups, namely hardware and software.
Generating high-end hardware is a subset of hardware augmentation approaches,
whereas conserving local resource and reducing resource requirements approaches
are grouped under software augmentation methods. Our study advocates that
consreving smartphones' native resources, which is mainly done via task
offloading, is more appropriate for already-developed applications than new
ones, due to costly re-development process. Cloud computing has recently
obtained momentous ground as one of the major cornerstone technologies in
augmenting smartphones. We present sample execution model for intensive mobile
applications and devised taxonomy of augmentation approaches. For better
comprehension, the results of this study are summarized in a table
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
To overcome the limited coverage in traditional wireless sensor networks,
\emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To
achieve longer battery lives of user devices and incentive human involvement,
this paper presents a novel approach that seamlessly integrates MCS with
wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS),
for supporting crowd sensing with energy consumption and offering rewards as
incentives. The optimization problem is formulated to simultaneously maximize
the data utility and minimize the energy consumption for service operator, by
jointly controlling wireless-power allocation at the \emph{access point} (AP)
as well as sensing-data size, compression ratio, and sensor-transmission
duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the
optimal power allocation policy is shown to have a \emph{threshold}-based
structure with respect to a defined \emph{crowd-sensing priority} function for
each MS. Given fixed sensing-data utilities, the compression policy achieves
the optimal compression ratio. Extensive simulations are also presented to
verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206
User-centric Networks Selection with Adaptive Data Compression for Smart Health
The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users’ objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques
TAME: an Efficient Task Allocation Algorithm for Integrated Mobile Gaming
We consider an integrated mobile gaming platform, in which the mobile device (e.g., smartphone) of a player can offload some game tasks toward a server as well as some neighboring mobile devices. The advantages of such a platform are manyfold: it can lead to an improved game experience, to a better use of energy resources, and, while offloading tasks to other mobile users, to the exploitation of the unused computing and storage resources of the mobile equipments, thus reducing the bandwidth and computing costs of the overall system. In this context, we formulate the problem of offloading the game computational tasks as an optimization problem that minimizes the maximum energy consumption across a set of mobile devices, under the constraints of a maximum response time and a limited availability of computation, communication and storage resources. In light of the problem complexity, we then propose a heuristic, called TAME, which is shown to closely approximate the optimal solution in all scenarios we considered. TAME also outperforms state-of-the-art algorithms under both synthetic and real scenarios, which have been devised based on a realistic and detailed energy consumption model for computation and communication resources. Our results, although tailored to mobile gaming, could be extended to other applications where it may be beneficial to offload computational and storage tasks through device-to-device communications, as enabled by Wi-Fi, Bluetooth, or the upcoming 5G technology
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Mobile Edge Computing Empowers Internet of Things
In this paper, we propose a Mobile Edge Internet of Things (MEIoT)
architecture by leveraging the fiber-wireless access technology, the cloudlet
concept, and the software defined networking framework. The MEIoT architecture
brings computing and storage resources close to Internet of Things (IoT)
devices in order to speed up IoT data sharing and analytics. Specifically, the
IoT devices (belonging to the same user) are associated to a specific proxy
Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes
the IoT data (generated by its IoT devices) in real-time. Moreover, we
introduce the semantic and social IoT technology in the context of MEIoT to
solve the interoperability and inefficient access control problem in the IoT
system. In addition, we propose two dynamic proxy VM migration methods to
minimize the end-to-end delay between proxy VMs and their IoT devices and to
minimize the total on-grid energy consumption of the cloudlets, respectively.
Performance of the proposed methods are validated via extensive simulations
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