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Counting mosquitoes in the wild is a crucial capability for monitoring, prediction, and control of vector-borne diseases. Current approaches are mainly manual, where specially designed mosquito traps or ovitraps are placed in areas of interest and recovered the next day. The counting itself is performed in an entomological laboratory, where individual mosquitoes are classified into species and counted. This process is costly, slow and inefficient. At the same time, mosquito counting is most relevant in tropical and sub-tropical countries, where mosquitoes spread deadly diseases like malaria, yellow fever and dengue fever. Many countries in these regions have relatively weak public health systems and so cannot support large-scale vector counting efforts. In this paper, we present a system architecture and a prototype to count mosquitoes in the wild with an Internet of Things approach. A sensor board is developed to gather audio data, and models are developed to detect, classify, and count mosquito species. Here, we present our prototype and an extensive background study of classifying mosquitoes based on sound recordings and some preliminary results and discussion.

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, and Culiseta. 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., Measurement(s) Sound • audio feature Technology Type(s) bioacoustic sensing • audio board • Microphone Device • sensor • mobile phone Factor Type(s) species of mosquito • sampling rate Sample Characteristic - Organism Aedes aegypti • Culex pipiens x Culex quinquefasciatus • Culiseta Sample Characteristic - Location Madeira Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13034597

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.

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.

Recent years demonstrate an increased interest in Passive Acoustic Monitoring (PAM) applications when studying cetaceans. However, they remain expensive underwater systems and targeted for industrial and military purposes. While the usage of smartphones as acoustic sensors has been observed in terrestrial environments, ocean and nautical PAM applications remain greatly unexplored. This paper presents the design, deployment and testing of a POSEIDON system, used for real-time augmentation of whale-watching experiences. We collect and use cetaceans' vocal call acoustic samples (clicks, moans and whistles) and apply machine learning for offline model training and prediction. When discriminating the calls, we find that Extra Trees and Gradient Boosting outperform other classifiers (>0.95 confidence threshold). Collected samples are at disposal to citizen scientists and marine biologists. Future studies involve real-time on-boat user testing.