English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Data acquisition from a multi-hop large-scale outdoor wireless sensor network(WSN) deployment for environmental monitoring is full of challenges. This isbecause the severe resource constraints on small battery-operated motes (e.g.,bandwidth, memory, power, and computing capacity), the big data acquisitionvolume from the large-scale WSN,and the highly dynamic wireless link conditionsin an outdoor communication environment. We present a novel compressed sensingapproach which can recover the sensing data at the sink with high fidelity whenvery few data packets are collected, leading to a significant reduction of thenet-work transmissions and thus an extension of the WSN lifetime. Interplayingwith the dynamic WSN routing topology, the proposed approach is efficient andsimple to implement on the resource-constrained motes without a mote's storingof any part of the random projection matrix, as opposed to other existingcompressed sensing based schemes. We propose a systematic method via machinelearning to find a suitable representation basis, for any given WSN deploymentand data field, which is both sparse and incoherent with the random projectionmatrix in the compressed sensing for data acquisition. We validate our approachand evaluate its performance using a real-world multi-hop WSN testbeddeployment in situ. The results demonstrate that our approach significantlyoutperforms existing compressed sensing approaches by reducing data recoveryerrors by an order of magnitude for the entire WSN observation field, whiledrastically reducing wireless communication costs at the same time.

Compressed Sensing in Multi-Hop Large-Scale Wireless Sensor Networks Based on Routing Topology Tomography