11,656 research outputs found
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Quality-optimized predictive analytics
On-line statistical and machine learning analytic tasks over large-
scale contextual data streams coming from e.g., wireless sensor networks, Inter-
net of Things environments, have gained high popularity nowadays due to their
significance in knowledge extraction, regression and classification tasks, and,
more generally, in making sense from large-scale streaming data. The quality
of the received contextual information, however, impacts predictive analytics
tasks especially when dealing with uncertain data, outliers data, and data con-
taining missing values. Low quality of received contextual data significantly
spoils the progressive inference and on-line statistical reasoning tasks, thus,
bias is introduced in the induced knowledge, e.g., classification and decision
making. To alleviate such situation, which is not so rare in real time contextual
information processing systems, we propose a progressive time-optimized data
quality-aware mechanism, which attempts to deliver contextual information
of high quality to predictive analytics engines by progressively introducing a
certain controlled delay. Such a mechanism progressively delivers high qual-
ity data as much as possible, thus eliminating possible biases in knowledge
extraction and predictive analysis tasks. We propose an analytical model for
this mechanism and show the benefits stem from this approach through com-
prehensive experimental evaluation and comparative assessment with quality-
unaware methods over real sensory multivariate contextual data
Consensus and Products of Random Stochastic Matrices: Exact Rate for Convergence in Probability
Distributed consensus and other linear systems with system stochastic
matrices emerge in various settings, like opinion formation in social
networks, rendezvous of robots, and distributed inference in sensor networks.
The matrices are often random, due to, e.g., random packet dropouts in
wireless sensor networks. Key in analyzing the performance of such systems is
studying convergence of matrix products . In this paper, we
find the exact exponential rate for the convergence in probability of the
product of such matrices when time grows large, under the assumption that
the 's are symmetric and independent identically distributed in time.
Further, for commonly used random models like with gossip and link failure, we
show that the rate is found by solving a min-cut problem and, hence, easily
computable. Finally, we apply our results to optimally allocate the sensors'
transmission power in consensus+innovations distributed detection
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