Mosquitoes are considered one of the more severe worldwide health hazards. Their populations vary heterogeneously in urban and rural landscapes, and fluctuate with seasonal or climatic trends and human activities. Thus, frequent widespread high-resolution surveillance of mosquitoes is essential for both understanding their complex ecology and behavior, and also predicting disease risk and formulating effective control strategies against mosquito-borne diseases such as malaria, dengue and Zika. To this end, in this paper, we present LOCOMOBIS, a LOw-COst MOsquito BIoacoustic Sensor where the wingbeat sounds produced by mosquitoes in flapping-wing flight are used to identify different species automatically. The sensor has been deployed and tested in a real field deployment and our results demonstrate the practical feasibility of this low-cost nonintrusive approach for monitoring mosquitoes in places requiring vector monitoring aimed at mosquito control or eradication. Moreover, to make the system more ubiquitous and easy to interact with, we implemented a web-based application where it is possible to check the collected data and the automatic species and gender classification.

As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strategies against mosquito-borne diseases. One technique involves using bioacoustics to automatically identify different species from their wing-beat sounds during flight. In this dataset, we collect sounds of three species of mosquitoes:Aedes Aegypti,Culex Quinquefasciatus&Pipiens, andCuliseta. These species were collected and reproduced in the laboratory of the Natural History Museum of Funchal, in Portugal, by entomologists trained to recognize and classify mosquitoes. For collecting the samples, we used a microcontroller and a mobile phone. The dataset presents audio samples collected with different sampling rates, where 34 audio features characterize each sound file, making it is possible to observe how mosquito populations vary heterogeneously. This dataset provides the basis for feature extraction and classification of flapping-wing flight sounds that could be used to identify different species.

While ubiquitous computing remains vastly applied in urban environments, their applications in ocean environment remain scarce due to the limitations in range and cost of current radio technology. This hinders environmental telemetry in the oceans and other remote areas. In this study, we explore the usage of IoT and Long Range Radio Communication (LoRa) in ocean environments. We study the maximum distance for LoRa and a potential location estimation based on the same technology using the passive RSSI analysis. Using three coastal based nodes and a node mounted on a sea vessel, we report a maximum range of 83.6km. We also achieve a location error within a radius of 3.4km (4% of maximum distance) in the sea. These results support marine biologist expeditions, allowing them to use low-cost, long-lasting and easy to deploy solutions for tracking marine objects and species in open ocean, providing them data in near-real-time. We discuss the findings from used models, outlining limitations, and providing a scenario for future ubiquitous IoT applications for tracking sea objects.

The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.

Mosquitoes are the vector of diseases that kill more than one million people per year worldwide. Surveillance systems are essential for understanding their complex ecology and behaviour. This is fundamental for predicting disease risk caused by mosquitoes and formulating effective control strategies against mosquito-borne diseases such as malaria, dengue, and Zika. Mosquito populations vary heterogeneously in urban and rural landscapes, fluctuating with seasonal and climatic trends and human activity. Several approaches provide environmental data for mosquito mapping and risk prediction. However, they rely traditionally upon labour-intensive techniques such as manual traps. This paper presents the optimal audio features for mosquito identification using ecoacoustics signals to automatically identify different mosquito species from their wingbeat sounds based on popular audio features. The audio selection method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Silhouette coefficient to evaluate the clusters in the data through the optimal-combined audio features. To classify the mosquito species and distinguish them from environmental-urban noise, the method comprises the Gaussian Mixture Model (GMM) and Gibbs approach for Aedes aegypti, and Culex quinquefasciatus, using the acoustic recordings of their wingbeat signals. Finally, comparing GMM and Gibbs, the two have very similar accuracy, but the classification time is much faster for Gibbs sampling, making it a good candidate for a lightweight solution. These are essential when deploying the described models to monitor mosquito vectors in the wild with Internet of Things (IoT) technologies.