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

The scale of wireless technologies penetration in our daily lives, primarilytriggered by the Internet-of-things (IoT)-based smart cities, is beaconing thepossibilities of novel localization and tracking techniques. Recently,low-power wide-area network (LPWAN) technologies have emerged as a solution tooffer scalable wireless connectivity for smart city applications. LoRa is onesuch technology that provides energy efficiency and wide-area coverage. Thisarticle explores the use of intelligent machine learning techniques, such assupport vector machines, spline models, decision trees, and ensemble learning,for received signal strength indicator (RSSI)-based ranging in LoRa networks,on a training dataset collected in two different environments: indoors andoutdoors. The suitable ranging model is then used to experimentally evaluatethe accuracy of localization and tracking using trilateration in the studiedenvironments. Later, we present the accuracy of LoRa-based positioning system(LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-basedsolutions. In the end, we discuss the challenges of satellite-independenttracking systems and propose future directions to improve accuracy and providedeployment feasibility.

RSSI Fingerprinting-based Localization Using Machine Learning in LoRa Networks