149,667 research outputs found
RCFD: A Novel Channel Access Scheme for Full-Duplex Wireless Networks Based on Contention in Time and Frequency Domains
In the last years, the advancements in signal processing and integrated
circuits technology allowed several research groups to develop working
prototypes of in-band full-duplex wireless systems. The introduction of such a
revolutionary concept is promising in terms of increasing network performance,
but at the same time poses several new challenges, especially at the MAC layer.
Consequently, innovative channel access strategies are needed to exploit the
opportunities provided by full-duplex while dealing with the increased
complexity derived from its adoption. In this direction, this paper proposes
RTS/CTS in the Frequency Domain (RCFD), a MAC layer scheme for full-duplex ad
hoc wireless networks, based on the idea of time-frequency channel contention.
According to this approach, different OFDM subcarriers are used to coordinate
how nodes access the shared medium. The proposed scheme leads to efficient
transmission scheduling with the result of avoiding collisions and exploiting
full-duplex opportunities. The considerable performance improvements with
respect to standard and state-of-the-art MAC protocols for wireless networks
are highlighted through both theoretical analysis and network simulations.Comment: Submitted at IEEE Transactions on Mobile Computing. arXiv admin note:
text overlap with arXiv:1605.0971
When Mobile Blockchain Meets Edge Computing
Blockchain, as the backbone technology of the current popular Bitcoin digital
currency, has become a promising decentralized data management framework.
Although blockchain has been widely adopted in many applications, e.g.,
finance, healthcare, and logistics, its application in mobile services is still
limited. This is due to the fact that blockchain users need to solve preset
proof-of-work puzzles to add new data, i.e., a block, to the blockchain.
Solving the proof-of-work, however, consumes substantial resources in terms of
CPU time and energy, which is not suitable for resource-limited mobile devices.
To facilitate blockchain applications in future mobile Internet of Things
systems, multiple access mobile edge computing appears to be an auspicious
solution to solve the proof-of-work puzzles for mobile users. We first
introduce a novel concept of edge computing for mobile blockchain. Then, we
introduce an economic approach for edge computing resource management.
Moreover, a prototype of mobile edge computing enabled blockchain systems is
presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
POEM: Pricing Longer for Edge Computing in the Device Cloud
Multiple access mobile edge computing has been proposed as a promising
technology to bring computation services close to end users, by making good use
of edge cloud servers. In mobile device clouds (MDC), idle end devices may act
as edge servers to offer computation services for busy end devices. Most
existing auction based incentive mechanisms in MDC focus on only one round
auction without considering the time correlation. Moreover, although existing
single round auctions can also be used for multiple times, users should trade
with higher bids to get more resources in the cascading rounds of auctions,
then their budgets will run out too early to participate in the next auction,
leading to auction failures and the whole benefit may suffer. In this paper, we
formulate the computation offloading problem as a social welfare optimization
problem with given budgets of mobile devices, and consider pricing longer of
mobile devices. This problem is a multiple-choice multi-dimensional 0-1
knapsack problem, which is a NP-hard problem. We propose an auction framework
named MAFL for long-term benefits that runs a single round resource auction in
each round. Extensive simulation results show that the proposed auction
mechanism outperforms the single round by about 55.6% on the revenue on average
and MAFL outperforms existing double auction by about 68.6% in terms of the
revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on
Algorithms and Architectures for Parallel Processing (ICA3PP
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