0000000000136409

AUTHOR

Alessio Martino

0000-0003-1730-5436

showing 3 related works from this author

Exploratory approach for network behavior clustering in LoRaWAN

2021

AbstractThe interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as I…

IoTGeneral Computer ScienceComputer sciencek-meansReliability (computer networking)02 engineering and technologyLoRaMachine LearningHome automation0202 electrical engineering electronic engineering information engineeringCluster AnalysisWirelessCluster analysisIoT LoRa LoRaWAN Machine Learning k-means Anomaly Detection Cluster AnalysisNetwork packetbusiness.industry020206 networking & telecommunicationsIoT; LoRa; LoRaWAN; Machine Learning; k-means; Anomaly Detection; Cluster AnalysisLoRaWANWireless network interface controllerScalabilityAnomaly Detection020201 artificial intelligence & image processingAnomaly detectionbusinessComputer networkJournal of Ambient Intelligence and Humanized Computing
researchProduct

A Clustering approach for profiling LoRaWAN IoT devices

2019

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality. Also, IoT information related to the network level (wireless or wired) may be stored in databases and can be processed to improve the technology operation and to detect network anomalies. Acquired data can be also used for profiling operation, in order to group devices according…

050101 languages & linguisticsIoTComputer scienceIoT; LoRa; LoRaWAN; machine learning; k-means; anomaly detection; cluster analysisk-means02 engineering and technologyLoRaSilhouette0202 electrical engineering electronic engineering information engineeringProfiling (information science)Wireless0501 psychology and cognitive sciencesCluster analysisbusiness.industryNetwork packetSettore ING-INF/03 - Telecomunicazioni05 social sciencesk-means clusteringanomaly detectionLoRaWANmachine learning020201 artificial intelligence & image processingAnomaly detectionInternet of ThingsbusinessComputer networkcluster analysis
researchProduct

Predicting lorawan behavior. How machine learning can help

2020

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets a…

IoTComputer Networks and CommunicationsComputer scienceDecision treeChannel occupancy; cluster analysis; IoT; LoRa; LoRaWAN; machine learning; network optimization; prediction analysisMachine learningcomputer.software_genreChannel occupancyLoRalcsh:QA75.5-76.95network optimizationNetwork performanceProtocol (object-oriented programming)Profiling (computer programming)Artificial neural networkNetwork packetbusiness.industrySettore ING-INF/03 - TelecomunicazioniPipeline (software)LoRaWANHuman-Computer Interactionmachine learningprediction analysisArtificial intelligencelcsh:Electronic computers. Computer sciencebusinesscomputerCommunication channelcluster analysis
researchProduct