Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Smart objects and home automation tools are becoming increasingly popular, and the number of smart devices that each dedicated application has to manage is increasing accordingly. The emergence of technologies such as serverless computing and dedicated machine-to-machine communication protocols represents a valuable opportunity to facilitate management of smart objects and replicability of new solutions. The aim of this paper is to propose a framework for home automation applications that can be applied to control and monitor any appliance or object in a smart home environment. The proposed framework makes use of a dedicated messages-exchange protocol based on MQTT and cloud-deployed serverless functions. Furthermore, a vocal command interface is implemented to let users control the smart object with vocal interactions, greatly increasing the accessibility and intuitiveness of the proposed solution. A smart object, namely a smart kitchen fan extractor system, was developed, prototyped, and tested to illustrate the viability of the proposed solution. The smart object is equipped with a narrowband IoT (NB-IoT) module to send and receive commands to and from the cloud. In order to evaluate the performance of the proposed solution, the suitability of NB-IoT for the transmission of MQTT messages was evaluated. The results show how NB-IoT has an acceptable latency performance despite some minimal packet loss.

This article describes a dataset of acceleration signals acquired from a low-cost Wireless Sensor Network (WSN) during seismic events that occurred in Central Italy. The WSN consists of 5 low-cost sensor nodes, each embedding an ADXL355 tri-axial MEMS accelerometer with a fixed sampling frequency of 250 Hz. The data was acquired from February 2023 to the end of June 2023. During this period, several earthquake sequences affected the area where the sensor network was installed. Continuous data was acquired from the WSN and then trimmed around the origin time of seismic events that occurred near the installation site, close to the city of Pollenza (MC), Italy. A total of 67 events were selected, whose data is available at the Istituto Nazionale di Geofisica e Vulcanologia (INGV) Seismology data center. The traces acquired from the WSN were then manually annotated by analysts from INGV. Annotations include picking time for P and S phases, when distinguishable from the background noise, alongside an associated uncertainty level for the manual annotations. The resulting dataset consists of 328 3 × 25,001 arrays, each associated with its metadata. The metadata includes event data (hypocenter position, origin time, magnitude, magnitude type, etc.), trace-related data (mean, median, maximum, and minimum amplitudes, manual picks, and picks uncertainty), and sensor-specific data (sensor name, sensitivity, and orientation). Furthermore, a small dataset consisting of non-seismic traces is included, with the goal of providing records of noise-only traces, relative to both electronic and environmental/anthropic noise sources. The dataset holds potential for training and developing Machine Learning or signal processing algorithms for seismic data with low signal-to-noise ratios. Additionally, it is valuable for research about earthquakes, structural health monitoring, and MEMS accelerometer performance in civil and seismic engineering applications.

An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave “picking” directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

Seismic wave picking is an essential task the implementation of Earthquake Early Warning (EEW) systems. While artificial intelligence picking methods show excellent accuracy, most were designed for devices with high computational resources. At the same time, distributed approaches for Early Warning systems show promise for the implementation of viable, widespread alert systems. This paper introduces a complete AIoT system for earthquake picking on resource-constrained devices. An algorithm has been developed to enable AIoT devices to switch between a detection mode, in which inferences are run on the data measured by the device, and a transmission mode, in which the device transmits alarms or other event information. To reduce inference times and input window duration, a set of deep learning pickers, called Fast PNet, derived from the PhaseNet model, were developed, achieving 74.4% inference time reduction for the shortest input model, which also showed a significant decrease in computational and power consumption. Despite a slight reduction in picking precision compared to the baseline model (from 0.09 to 0.25 s), detection performance remains high (96% precision and 98% recall). The overall system has been tested on a real event from Central Italy, displaying a 0.16-second picking error for the selected event, besides also showing its ability to reduce the amount of data to be processed for transmission to about 26.5% of the total observed input data, a great benefit compared to fully centralized EEW systems.

Bluetooth Low Energy (BLE) Mesh networks are a promising solution for Internet of Things applications, offering scalability, low power consumption, and resilience to node loss. Different factors in BLE Mesh networks may severely affect performance, from network topology, to the position of relay nodes, to the setting of advertising and scanning parameters that regulate message relaying. The relaying process is the backbone of BLE Mesh networks, enabling many-to-many communication where devices can communicate with any other device in the network, including those outside their coverage range. This paper investigates the effects on communication performance of relaying implementations with and without preemption of the scanning process. Two possible methods of preemption are implemented, compared and analyzed: stateless preemption, which starts a new scanning after the previous event has been interrupted to initiate advertising, and stateful preemption, which maintains information on the current scanning across interrupts. The implementation with stateful preemption displayed better latency than the implementation without any form of preemption, while also achieving a good Packet Delivery Ratio and counterbalancing reliability issues of the stateless preemption approach. Based on these results, stateful implementation proved to be an excellent solution for scenarios where it is essential to ensure low latencies, both in low and high traffic topologies.