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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Optimal data aggregation aimed at maximizing IoT network lifetime byminimizing constrained on-board resource utilization continues to be achallenging task. The existing data aggregation methods have proven thatcompressed sensing is promising for data aggregation. However, they compromiseeither on energy efficiency or recovery fidelity and require complex on-nodecomputations. In this paper, we propose a novel Light Weight Compressed DataAggregation (LWCDA) algorithm that randomly divides the entire network intonon-overlapping clusters for data aggregation. The random non-overlappingclustering offers two important advantages: 1) energy efficiency, as each nodehas to send its measurement only to its cluster head, 2) highly sparsemeasurement matrix, which leads to a practically implementable framework withlow complexity. We analyze the properties of our measurement matrix usingrestricted isometry property, the associated coherence and phase transition.Through extensive simulations on practical data, we show that the measurementmatrix can reconstruct data with high fidelity. Further, we demonstrate thatthe LWCDA algorithm reduces transmission cost significantly against baselineapproaches, implying thereby the enhancement of the network lifetime.

Novel Light Weight Compressed Data Aggregation Using Sparse Measurements for IoT Networks