3,547 research outputs found
D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service
In this paper, we consider the use of UAVs to provide wireless connectivity
services, for example after failures of wireless network components or to
simply provide additional bandwidth on demand, and introduce the concept of
UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework,
dubbed D3S, which consists of four phases: demand, decision, deployment, and
service. The main objective of this framework is to develop efficient and
realistic solutions to implement these four phases. The technical problems
include determining the type and number of UAVs to be deployed, and also their
final locations (e.g., hovering or on-ground), which is important for serving
certain applications. These questions will be part of the decision phase. They
also include trajectory planning of UAVs when they have to travel between
charging stations and deployment locations and may have to do this several
times. These questions will be part of the deployment phase. The service phase
includes the implementation of the backbone communication and data routing
between UAVs and between UAVs and ground control stations
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
VECTORS: Video communication through opportunistic relays and scalable video coding
Crowd-sourced video distribution is frequently of interest in the local
vicinity. In this paper, we propose a novel design to transfer such content
over opportunistic networks with adaptive quality encoding to achieve
reasonable delay bounds. The video segments are transmitted between source and
destination in a delay tolerant manner using the Nearby Connections Android
library. This implementation can be applied to multiple domains, including farm
monitoring, wildlife, and environmental tracking, disaster response scenarios,
etc. In this work, we present the design of an opportunistic contact based
system, and we discuss basic results for the trial runs within our institute.Comment: 13 pages, 6 figures, and under 3000 words for submission to the
SoftwareX journa
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
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