62 research outputs found
Keep Your Nice Friends Close, but Your Rich Friends Closer -- Computation Offloading Using NFC
The increasing complexity of smartphone applications and services necessitate
high battery consumption but the growth of smartphones' battery capacity is not
keeping pace with these increasing power demands. To overcome this problem,
researchers gave birth to the Mobile Cloud Computing (MCC) research area. In
this paper we advance on previous ideas, by proposing and implementing the
first known Near Field Communication (NFC)-based computation offloading
framework. This research is motivated by the advantages of NFC's short distance
communication, with its better security, and its low battery consumption. We
design a new NFC communication protocol that overcomes the limitations of the
default protocol; removing the need for constant user interaction, the one-way
communication restraint, and the limit on low data size transfer. We present
experimental results of the energy consumption and the time duration of two
computationally intensive representative applications: (i) RSA key generation
and encryption, and (ii) gaming/puzzles. We show that when the helper device is
more powerful than the device offloading the computations, the execution time
of the tasks is reduced. Finally, we show that devices that offload application
parts considerably reduce their energy consumption due to the low-power NFC
interface and the benefits of offloading.Comment: 9 pages, 4 tables, 13 figure
Decentralizing indexing and bootstrapping for online applications
https://doi.org/10.1049/blc2.12001Abstract Peer-to-peer (P2P) networks utilize centralized entities (trackers) to assist peers in finding and exchanging information. Although modern P2P protocols are now trackerless and their function relies on distributed hash tables (DHTs), centralized entities are still needed to build file indices (indexing) and assist users in joining DHT swarms (bootstrapping). Although the functionality of these centralized entities are limited, every peer in the network is expected to trust them to function as expected (e.g. to correctly index new files). In this work, a new approach for designing and building decentralized online applications is proposed by introducing DIBDApp. The approach combines blockchain, smart contracts and BitTorrent for building up a combined technology that permits to create decentralized applications that do not require any assistance from centralized entities. DIBDApp is a software library composed of Ethereum smart contracts and an API to the BitTorrent protocol that fully decentralizes indexing, bootstrapping and file storing. DIBDApp enables any peer to seamlessly connect to the designed smart contracts via the Web3J protocol. Extensive experimentation on the Rinkeby Ethereum testnet shows that applications built using the DIBDApp library can perform the same operations as in traditional back-end architectures with a gas cost of a few USDÂ cents.Peer reviewe
Workflow Optimization for Parallel Split Learning
Split learning (SL) has been recently proposed as a way to enable
resource-constrained devices to train multi-parameter neural networks (NNs) and
participate in federated learning (FL). In a nutshell, SL splits the NN model
into parts, and allows clients (devices) to offload the largest part as a
processing task to a computationally powerful helper. In parallel SL, multiple
helpers can process model parts of one or more clients, thus, considerably
reducing the maximum training time over all clients (makespan). In this paper,
we focus on orchestrating the workflow of this operation, which is critical in
highly heterogeneous systems, as our experiments show. In particular, we
formulate the joint problem of client-helper assignments and scheduling
decisions with the goal of minimizing the training makespan, and we prove that
it is NP-hard. We propose a solution method based on the decomposition of the
problem by leveraging its inherent symmetry, and a second one that is fully
scalable. A wealth of numerical evaluations using our testbed's measurements
allow us to build a solution strategy comprising these methods. Moreover, we
show that this strategy finds a near-optimal solution, and achieves a shorter
makespan than the baseline scheme by up to 52.3%.Comment: IEEE INFOCOM 202
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data
The multitude of data generated by sensors available on users' mobile
devices, combined with advances in machine learning techniques, support
context-aware services in recognizing the current situation of a user (i.e.,
physical context) and optimizing the system's personalization features.
However, context-awareness performances mainly depend on the accuracy of the
context inference process, which is strictly tied to the availability of
large-scale and labeled datasets. In this work, we present a framework
developed to collect datasets containing heterogeneous sensing data derived
from personal mobile devices. The framework has been used by 3 voluntary users
for two weeks, generating a dataset with more than 36K samples and 1331
features. We also propose a lightweight approach to model the user context able
to efficiently perform the entire reasoning process on the user mobile device.
To this aim, we used six dimensionality reduction techniques in order to
optimize the context classification. Experimental results on the generated
dataset show that we achieve a 10x speed up and a feature reduction of more
than 90% while keeping the accuracy loss less than 3%
Emerging ExG-based NUI Inputs in Extended Realities : A Bottom-up Survey
Incremental and quantitative improvements of two-way interactions with extended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduceto the areaof XR.Peer reviewe
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