1,324 research outputs found
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
Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation
The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method
Neural Network based Short Term Forecasting Engine To Optimize Energy And Big Data Storage Resources Of Wireless Sensor Networks
Energy efficient wireless networks is the primary
research goal for evolving billion device applications like IoT,
smart grids and CPS. Monitoring of multiple physical events
using sensors and data collection at central gateways is the
general architecture followed by most commercial, residential
and test bed implementations. Most of the events monitored at
regular intervals are largely redundant/minor variations leading
to large wastage of data storage resources in Big data servers and
communication energy at relay and sensor nodes. In this paper
a novel architecture of Neural Network (NN) based day ahead
steady state forecasting engine is implemented at the gateway
using historical database. Gateway generates an optimal transmit
schedules based on NN outputs thereby reducing the redundant
sensor data when there is minor variations in the respective
predicted sensor estimates. It is observed that NN based load
forecasting for power monitoring system predicts load with less
than 3% Mean Absolute Percentage Error (MAPE). Gateway
forward transmit schedules to all power sensing nodes day ahead
to reduce sensor and relay nodes communication energy. Matlab
based simulation for evaluating the benefits of proposed model
for extending the wireless network life time is developed and
confirmed with an emulation scenario of our testbed. Network
life time is improved by 43% from the observed results using
proposed model
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