2,945 research outputs found
Device-Aware Routing and Scheduling in Multi-Hop Device-to-Device Networks
The dramatic increase in data and connectivity demand, in addition to
heterogeneous device capabilities, poses a challenge for future wireless
networks. One of the promising solutions is Device-to-Device (D2D) networking.
D2D networking, advocating the idea of connecting two or more devices directly
without traversing the core network, is promising to address the increasing
data and connectivity demand. In this paper, we consider D2D networks, where
devices with heterogeneous capabilities including computing power, energy
limitations, and incentives participate in D2D activities heterogeneously. We
develop (i) a device-aware routing and scheduling algorithm (DARS) by taking
into account device capabilities, and (ii) a multi-hop D2D testbed using
Android-based smartphones and tablets by exploiting Wi-Fi Direct and legacy
Wi-Fi connections. We show that DARS significantly improves throughput in our
testbed as compared to state-of-the-art
OSCAR: A Collaborative Bandwidth Aggregation System
The exponential increase in mobile data demand, coupled with growing user
expectation to be connected in all places at all times, have introduced novel
challenges for researchers to address. Fortunately, the wide spread deployment
of various network technologies and the increased adoption of multi-interface
enabled devices have enabled researchers to develop solutions for those
challenges. Such solutions aim to exploit available interfaces on such devices
in both solitary and collaborative forms. These solutions, however, have faced
a steep deployment barrier.
In this paper, we present OSCAR, a multi-objective, incentive-based,
collaborative, and deployable bandwidth aggregation system. We present the
OSCAR architecture that does not introduce any intermediate hardware nor
require changes to current applications or legacy servers. The OSCAR
architecture is designed to automatically estimate the system's context,
dynamically schedule various connections and/or packets to different
interfaces, be backwards compatible with the current Internet architecture, and
provide the user with incentives for collaboration. We also formulate the OSCAR
scheduler as a multi-objective, multi-modal scheduler that maximizes system
throughput while minimizing energy consumption or financial cost. We evaluate
OSCAR via implementation on Linux, as well as via simulation, and compare our
results to the current optimal achievable throughput, cost, and energy
consumption. Our evaluation shows that, in the throughput maximization mode, we
provide up to 150% enhancement in throughput compared to current operating
systems, without any changes to legacy servers. Moreover, this performance gain
further increases with the availability of connection resume-supporting, or
OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput
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