Sensor Networks

9th International Conference, SENSORNETS 2020, Valletta, Malta, February 28–29, 2020, and 10th International Conference, SENSORNETS 2021, Virtual Event, February 9–10, 2021, Revised Selected Papers

AvAndreas Ahrens,RangaRao Venkatesha Prasad

Häftad, Engelska, 2022

Del i serien Communications in Computer and Information Science

552 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This book constitutes selected and revised papers of the 9th International Conference, SENSORNETS 2020, Valletta, Malta, held in February 2020, and the 10th International Conference, SENSORNETS 2021, held virtually in February 2021. The 7 full papers presented were carefully reviewed and selected from 60 submissions. They focus in all aspects of sensor networks, including hardware of sensor networks, wireless communication protocols, sensor networks software, hardware and architectures, wireless information networks, channel characterization, data manipulation, signal processing, localization and object tracking through sensor networks, modelling and simulation, applications and uses.

Produktinformation

Utgivningsdatum:2022-09-27
Mått:155 x 235 x 10 mm
Vikt:265 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:153
Förlag:Springer International Publishing AG
ISBN:9783031177170

Utforska kategorier

Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT
Tillämpad datateknik inom Data och IT

Innehållsförteckning

Anomaly Detection in Beehives: An Algorithm Comparison.- Evaluation of a CDM Interrogation Scheme Allowing Spectrally Overlapping FBG Sensors.- Strength Exercise Monitoring with Inertial Sensors.- Improved Wake-up Receiver Architectures with Carrier Sense Capabilities for Low-power Wireless Communication.- Seismic Sensor Network for High Density Deployments: Concept, Design, Deployment and Results.- Acquisition of EFS and Capacitive Measurement Data on Low-Power and Connected IoT Devices.- Performance Evaluation of Embedded Time Series Indexes using Bitmaps, Partitioning, and Trees.