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

Driven by the rapid growth of Internet of Things applications, tremendousdata need to be collected by sensors and uploaded to the servers for furtherprocess. As a promising solution, mobile crowd sensing enables controllablesensing and transmission processes for multiple types of data in a singledevice. In this paper, a typical user is considered that is required to senseand transmit data to a server, while it is assumed to remain busy and incapableof sensing data during an interval. An optimization problem is formulated tominimize the energy consumption of data sensing and transmission by controllingthe sensing and transmission rates over time, subject to the constraints on thesensing data sizes, transmission data sizes, data casualty, and sensing busytime. This problem is highly challenging, due to the coupling between the ratesas well as the existence of the busy time. To deal with this problem, we firstshow that it can be equivalently decomposed into two subproblems, correspondingto a search for the amount of data size that needs to be sensed before the busytime (referred to as the height), as well as the sensing and transmission ratecontrol given the height. Next, we show that the latter problem can beefficiently solved by using the classical string-pulling method, while anefficient algorithm is proposed to progressively find the optimal heightwithout the exhaustive search. Moreover, the solution approach is extended to amore complex scenario where there is a finite-size buffer at the server forreceiving data. Last, simulations are conducted to evaluate the performance ofthe proposed design.

Joint Sensing and Communication-Rate Control for Energy Efficient Mobile Crowd Sensing