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 Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The next era of information revolution will rely on aggregating big data frommassive numbers of devices that are widely scattered in our environment. Mostof these devices are expected to be of low-complexity, low-cost, and limitedpower supply, which impose stringent constraints on the network operation. Inthis regard, this paper investigates aerial data aggregation and fieldestimation from a finite spatial field via an unmanned aerial vehicle (UAV).Instead of fusing, relaying, and routing the data across the wireless nodes tofixed locations access points, a UAV flies over the field and collects therequired data for two prominent missions; data aggregation and fieldestimation. To accomplish these tasks, the field of interest is divided intoseveral subregions over which the UAV hovers to collect samples from theunderlying nodes. To this end, we formulate and solve an optimization problemto minimize total hovering and traveling time of each mission. While the formerrequires the collection of a prescribed average number of samples from thefield, the latter ensures for a given field spatial correlation model that theaverage mean-squared estimation error of the field value is no more than apredetermined threshold at any point. These goals are fulfilled by optimizingthe number of subregions, the area of each subregion, the hovering locations,the hovering time at each location, and the trajectory traversed betweenhovering locations. The proposed formulation is shown to be np-hard mixedinteger problem, and hence, a decoupled heuristic solution is proposed. Theresults show that there exists an optimal number of subregions that balance thetradeoff between hovering and traveling times such that the total time forcollecting the required samples is minimized.

Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering & Traveling Time Dilemma of UAVs