Mobile Embedded Wireless Sensor Network to Prevent Deforestation

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Choosing a TCP Version over Static Ad Hoc Wireless Networks: Wired TCP or Wireless TCP?

International audienceThe Transmission Control Protocol (TCP) was originally designed to operate on wired networks. However, nowadays the traffic on wireless networks has grown to a point where one must take into account the specific characteristics of such networks when setting up a particular TCP implementation on them. Particularly, the TCP performance has been studied over ad hoc wireless networks leading to several new implementations for TCP. The main issue for TCP on ad hoc wireless networks is to differentiate between losses due to congestion and losses occurred at lower network layers. In this paper, we analyze the TCP performance on two different scenarios of static ad hoc wireless networks over the DSDV routing protocol. Our findings show that choosing the right TCP version for ad hoc wireless networks is a key factor for their performance. We show for the scenarios we study, that TCP Reno outperforms TCP Westwood on the average loss rate as well as on the throughput

Illegal logging detection based on acoustic surveillance of forest

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB

RIMBAMON©: A Forest Monitoring System Using Wireless Sensor Networks

Forests are important to the lives of every human and animal living on this planet. It provides an environment for many species of plants and animals, thus protecting and sustaining the diversity of nature. However, it is constantly being threatened by illegal logging and indiscriminate development. This problem needs to be overcome in order to protect our forests before it is too late. Therefore, there is an urgent need to constantly monitor the conditions inside the forest especially under the forest canopy in order to develop a comprehensive, real-time Forest Decision Support System. This report presents the RIMBAMON© system which is aimed at providing a way to capture and manipulate forestry environmental data coming from the sensors in a sensor network. This system utilizes Wireless Sensor Networks for communication. As there are several existing technologies that employ the Wireless Sensor Networks technology, this project will focus on the TinyOS operating system for sensor networks. Various tools will be used to develop the system; such as the TinyOS Simulator (TOSSIM), Java, Cygwin and TinyViz. The concepts behind wireless sensor networks and the tools used in this project are examined and discussed alongside simulation and testing of TinyOS. The outcome of this project is a prototype system which is able to monitor and report the conditions under the forest canopy

Characterising soundscapes across diverse ecosystems using a universal acoustic feature set

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labor-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we have developed a generalizable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed soundscapes from a variety of ecosystems into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, providing a potential route for real-time automated detection of irregular environmental behavior including illegal logging and hunting. Our highly generalizable approach, and the common set of features, will enable scientists to unlock previously hidden insights from acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts

Assessment and validation of miniaturized technology for the remote tracking of critically endangered Galápagos pink land iguana (Conolophus marthae)

Abstract Background: Gathering ecological data for species of conservation concern inhabiting remote regions can be daunting and, sometimes, logistically infeasible. We built a custom-made GPS tracking device that allows to remotely and accurately collect animal position, environmental, and ecological data, including animal temperature and UVB radiation. We designed the device to track the critically endangered Galápagos pink land iguana, Conolophus marthae. Here we illustrate some technical solutions adopted to respond to challenges associated with such task and present some preliminary results from controlled trial experiments and field implementation. Results: Our tests show that estimates of temperature and UVB radiation are affected by the design of our device, in particular by its casing. The introduced bias, though, is systematic and can be corrected using linear and quadratic regressions on collected values. Our data show that GPS accuracy loss, although introduced by vegetation and orientation of the devices when attached to the animals, is acceptable, leading to an average error gap of less than 15 m in more than 50% of the cases. Conclusions: We address some technical challenges related to the design, construction, and operation of a custommade GPS tracking device to collect data on animals in the wild. Systematic bias introduced by the technological implementation of the device exists. Understanding the nature of the bias is crucial to provide correction models. Although designed to track land iguanas, our device could be used in other circumstances and is particularly useful to track organisms inhabiting locations that are difficult to reach or for which classic telemetry approaches are unattainable

Run-time fault diagnosis in wireless sensor systems

Software failures in wireless sensor systems are notoriously difficult to debug. Resource constraints in wireless deployments substantially restrict visibility into the root causes of system and application level faults. At the same time, the high deployment cost of wireless sensor systems often far exceeds the cumulative cost of all other sensor hardware, such that software failures that completely disable a node are prohibitively expensive to repair in real-world applications, e.g. by on-site visits to replace or reset nodes. This thesis describes NodeMD, a fault management system designed to improve node debugging capabilities prior to deployment, and enable remote debugging on in-situ sensor nodes that fail. This system successfully implements lightweight run-time detection, logging, and notification of software faults on wireless mote-class devices. NodeMD introduces a debug mode that catches a failure before it completely disables a node and drops the node into a state that enables further diagnosis and correction, thus avoiding on-site redeployment. We present a detailed analysis of NodeMD on real world applications of wireless sensor systems

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data