Abstract
The 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 IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.
Highlights
The Internet of Things (IoT) is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other
We have presented a study on the behavioral clustering of IoT End Devices (EDs)
We explored the dataset by using a Machine Learning (ML) approach on 997, 183 packets generated by 2169 EDs running 3 different IoT applications
Summary
The Internet of Things (IoT) is a new technology paradigm envisioned as a global network of machines and devices capable of interacting with each other. We develop a framework that, starting from a database at the NS, produces the clustering1 This means that, by leveraging these ML tools, we are able to derive profiles of the behavior of IoT EDs connected to the LoRAWAN network. It paves the way toward a labeling approach that can be used by network operators to identify the EDs that are connected and in case to plan new radio resources, more suitable parameter settings and eventually different configurations of the IoT EDs and services.
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