4,745 research outputs found
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
Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization
We consider a system that is composed of an energy constrained sensor node
and a sink node, and devise optimal data compression and transmission policies
with an objective to prolong the lifetime of the sensor node. While applying
compression before transmission reduces the energy consumption of transmitting
the sensed data, blindly applying too much compression may even exceed the cost
of transmitting raw data, thereby losing its purpose. Hence, it is important to
investigate the trade-off between data compression and transmission energy
costs. In this paper, we study the joint optimal compression-transmission
design in three scenarios which differ in terms of the available channel
information at the sensor node, and cover a wide range of practical situations.
We formulate and solve joint optimization problems aiming to maximize the
lifetime of the sensor node whilst satisfying specific delay and bit error rate
(BER) constraints. Our results show that a jointly optimized
compression-transmission policy achieves significantly longer lifetime (90% to
2000%) as compared to optimizing transmission only without compression.
Importantly, this performance advantage is most profound when the delay
constraint is stringent, which demonstrates its suitability for low latency
communication in future wireless networks.Comment: accepted for publication in IEEE Transactions on Wireless
Communicaiton
Efficient Data Compression with Error Bound Guarantee in Wireless Sensor Networks
We present a data compression and dimensionality reduction scheme for data
fusion and aggregation applications to prevent data congestion and reduce
energy consumption at network connecting points such as cluster heads and
gateways. Our in-network approach can be easily tuned to analyze the data
temporal or spatial correlation using an unsupervised neural network scheme,
namely the autoencoders. In particular, our algorithm extracts intrinsic data
features from previously collected historical samples to transform the raw data
into a low dimensional representation. Moreover, the proposed framework
provides an error bound guarantee mechanism. We evaluate the proposed solution
using real-world data sets and compare it with traditional methods for temporal
and spatial data compression. The experimental validation reveals that our
approach outperforms several existing wireless sensor network's data
compression methods in terms of compression efficiency and signal
reconstruction.Comment: ACM MSWiM 201
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
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