An Innovative Dynamic Mobility Sampling Scheme Based on Multiresolution Wavelet Analysis in IoT Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10249964" target="_blank" >RIV/61989100:27740/22:10249964 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/22:10249964

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9608980" target="_blank" >https://ieeexplore.ieee.org/document/9608980</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/JIOT.2021.3126550" target="_blank" >10.1109/JIOT.2021.3126550</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Innovative Dynamic Mobility Sampling Scheme Based on Multiresolution Wavelet Analysis in IoT Networks

Popis výsledku v původním jazyce

Mobility is a key aspect of modern networking systems. To determine how to better manage the available resources, many architectures aim to a priori know the future positions of mobile nodes. This can be determined, for example, from mobile sensors in a smart city environment or wearable devices carried by pedestrians. If we consider infrastructure networks, frequently changing the coverage cell may lead to service disruptions if a predictive approach is not deployed in the system. All predictive systems are based on the storage of old mobility samples to adequately train the model. Our focus is based on the possibility to determine an approach for adaptively sampling mobility patterns based on the intrinsic features of the human/node behavior. Several works in the literature examine mobility prediction mobile networks, but all of them are dedicated to the study of time features in mobility traces: none took into account the spectral content of historical mobility patterns for predictive purposes. In contrast, we take into account this spectral content in mobility samples. Through a set of wavelet transforms, we adapted the sampling frequency dynamically and obtained a considerable set of advantages (space, energy, accuracy, etc.). In fact, this issue covers an important role in the IoT paradigm, where energy consumption is one of the main variables requiring optimization (frequent and unnecessary mobility samplings can disrupt battery life). We performed several simulations using real-world traces to confirm the merit of our proposal.

Název v anglickém jazyce

An Innovative Dynamic Mobility Sampling Scheme Based on Multiresolution Wavelet Analysis in IoT Networks

Popis výsledku anglicky

Mobility is a key aspect of modern networking systems. To determine how to better manage the available resources, many architectures aim to a priori know the future positions of mobile nodes. This can be determined, for example, from mobile sensors in a smart city environment or wearable devices carried by pedestrians. If we consider infrastructure networks, frequently changing the coverage cell may lead to service disruptions if a predictive approach is not deployed in the system. All predictive systems are based on the storage of old mobility samples to adequately train the model. Our focus is based on the possibility to determine an approach for adaptively sampling mobility patterns based on the intrinsic features of the human/node behavior. Several works in the literature examine mobility prediction mobile networks, but all of them are dedicated to the study of time features in mobility traces: none took into account the spectral content of historical mobility patterns for predictive purposes. In contrast, we take into account this spectral content in mobility samples. Through a set of wavelet transforms, we adapted the sampling frequency dynamically and obtained a considerable set of advantages (space, energy, accuracy, etc.). In fact, this issue covers an important role in the IoT paradigm, where energy consumption is one of the main variables requiring optimization (frequent and unnecessary mobility samplings can disrupt battery life). We performed several simulations using real-world traces to confirm the merit of our proposal.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

11336-11350

Kód UT WoS článku

000812536000078

EID výsledku v databázi Scopus

—