784 research outputs found
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
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
Multivariate time series analysis of temperatures in the archaeological museum of L'Almoina (Valencia, Spain)
[EN] An earlier study carried out in 2010 at the archaeological site of L'Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019-October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.The authors are grateful to Angel Perles for his support in this research. In addition, the authors would like to thank the Museum of L'Almoina, managed by the local government of Valencia (Spain) and his director Vicent Escriva. Thanks are also given to Esther Nebot Diaz from the "Department of Conservation and Restoration of Cultural Heritage" of the Polytechnic University of Valencia (UPV) for her help in the calibration of sensors. S.R. wants to thank the grant received from Instituto Colombiano de Credito Educativo y Estudios Tecnicos en el Exterior (ICETEX) by means of Programa credito Pasaporte a la Ciencia ID 3595089, as well as by Pontificia Universidad Javeriana Cali (Nit 860013720-1) through the Convenio de Capacitacion para Docentes O. J. 086/17.RamĂrez, S.; Zarzo CastellĂł, M.; GarcĂa Diego, FJ. (2021). Multivariate time series analysis of temperatures in the archaeological museum of L'Almoina (Valencia, Spain). Sensors. 21(13):1-40. https://doi.org/10.3390/s21134377140211
Multi-dimensional data stream compression for embedded systems
The rise of embedded systems and wireless technologies led to the emergence of
the Internet of Things (IoT). Connected objects in IoT communicate with each
other by transferring data streams over the network. For instance, in Wireless
Sensor Networks (WSNs), sensor-equipped devices use sensors to capture
properties, such as temperature or accelerometer, and send 1D or nD data streams
to a host system. Power consumption is a critical problem for connected objects
that have to work for a long time without being recharged, as it greatly affects
their lifetime and usability. Data summarization is key for energy-constrained
connected devices, as transmitting fewer data can reduce energy usage during
transmission. Data compression, in particular, can compress the data stream
while preserving information to a great extent. Many compression methods have
been proposed in previous research. However, most of them are either not
applicable to connected objects, due to resource limitation, or only handle
one-dimensional streams while data acquired in connected objects are often
multi-dimensional. Lightweight Temporal Compression (LTC) is among the lossy
stream compression methods that provide the highest compression rate for the
lowest CPU and memory consumption. In this thesis, we investigate the extension
of LTC to multi-dimensional streams. First, we provide a formulation of the
algorithm in an arbitrary vectorial space of dimension n. Then, we implement the
algorithm for the infinity and Euclidean norms, in spaces of dimension 2D+t and
3D+t. We evaluate our implementation on 3D acceleration streams of human
activities, on Neblina, a module integrating multiple sensors developed by our
partner Motsai. Results show that the 3D implementation of LTC can save up to
20% in energy consumption for slow-paced activities, with a memory usage of
about 100 B. Finally, we compare our method with polynomial regression
compression methods in different dimensions. Our results show that our extension
of LTC gives a higher compression ratio than the polynomial regression method,
while using less memory and CPU
Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks
Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms
Experiments and analysis of quality andEnergy-aware data aggregation approaches inWSNs
A wireless sensor network consists of autonomous devices
able to collect various data from the area that surrounds
them. However, the resources associated with sensors are
limited and, thus, in order to guarantee a longer life of all
the network components, it is necessary to adopt energysavings
methods. This paper, considering that the transmission
phase is the main cause of energy dissipation, presents
an approach aimed to save energy by capturing and aggregating
signals instead of sending them in raw form. Anyway,
aggregation should not imply the loss of useful data. For this
reason, information about possible outliers is preserved and
the aggregated values have to satisfy data quality (i.e., accuracy,
precision, and timeliness) requirements. In order to
show the correctness and validity of the proposed method,
it has been tested on a real case study and its performance
has been compared with two other consolidated approaches
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application
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