19,629 research outputs found
Doped Fountain Coding for Minimum Delay Data Collection in Circular Networks
This paper studies decentralized, Fountain and network-coding based
strategies for facilitating data collection in circular wireless sensor
networks, which rely on the stochastic diversity of data storage. The goal is
to allow for a reduced delay collection by a data collector who accesses the
network at a random position and random time. Data dissemination is performed
by a set of relays which form a circular route to exchange source packets. The
storage nodes within the transmission range of the route's relays linearly
combine and store overheard relay transmissions using random decentralized
strategies. An intelligent data collector first collects a minimum set of coded
packets from a subset of storage nodes in its proximity, which might be
sufficient for recovering the original packets and, by using a message-passing
decoder, attempts recovering all original source packets from this set.
Whenever the decoder stalls, the source packet which restarts decoding is
polled/doped from its original source node. The random-walk-based analysis of
the decoding/doping process furnishes the collection delay analysis with a
prediction on the number of required doped packets. The number of doped packets
can be surprisingly small when employed with an Ideal Soliton code degree
distribution and, hence, the doping strategy may have the least collection
delay when the density of source nodes is sufficiently large. Furthermore, we
demonstrate that network coding makes dissemination more efficient at the
expense of a larger collection delay. Not surprisingly, a circular network
allows for a significantly more (analytically and otherwise) tractable
strategies relative to a network whose model is a random geometric graph
Analysis of Power-aware Buffering Schemes in Wireless Sensor Networks
We study the power-aware buffering problem in battery-powered sensor
networks, focusing on the fixed-size and fixed-interval buffering schemes. The
main motivation is to address the yet poorly understood size variation-induced
effect on power-aware buffering schemes. Our theoretical analysis elucidates
the fundamental differences between the fixed-size and fixed-interval buffering
schemes in the presence of data size variation. It shows that data size
variation has detrimental effects on the power expenditure of the fixed-size
buffering in general, and reveals that the size variation induced effects can
be either mitigated by a positive skewness or promoted by a negative skewness
in size distribution. By contrast, the fixed-interval buffering scheme has an
obvious advantage of being eminently immune to the data-size variation. Hence
the fixed-interval buffering scheme is a risk-averse strategy for its
robustness in a variety of operational environments. In addition, based on the
fixed-interval buffering scheme, we establish the power consumption
relationship between child nodes and parent node in a static data collection
tree, and give an in-depth analysis of the impact of child bandwidth
distribution on parent's power consumption.
This study is of practical significance: it sheds new light on the
relationship among power consumption of buffering schemes, power parameters of
radio module and memory bank, data arrival rate and data size variation,
thereby providing well-informed guidance in determining an optimal buffer size
(interval) to maximize the operational lifespan of sensor networks
A Search Strategy of Level-Based Flooding for the Internet of Things
This paper deals with the query problem in the Internet of Things (IoT).
Flooding is an important query strategy. However, original flooding is prone to
cause heavy network loads. To address this problem, we propose a variant of
flooding, called Level-Based Flooding (LBF). With LBF, the whole network is
divided into several levels according to the distances (i.e., hops) between the
sensor nodes and the sink node. The sink node knows the level information of
each node. Query packets are broadcast in the network according to the levels
of nodes. Upon receiving a query packet, sensor nodes decide how to process it
according to the percentage of neighbors that have processed it. When the
target node receives the query packet, it sends its data back to the sink node
via random walk. We show by extensive simulations that the performance of LBF
in terms of cost and latency is much better than that of original flooding, and
LBF can be used in IoT of different scales
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