Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, they have played a crucial role in assessing air, water, and soil quality, as well as ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments in micro- and nanotechnology-enabled sensors for biomedical and environmental challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores the applications of these sensors in addressing current challenges in both biomedical and environmental domains. The article concludes by emphasizing the need for further research to expand the detection capabilities of sensors/devices, enhance sensitivity and selectivity, integrate wireless communication and energy-harvesting technologies, and optimize sample preparation, material selection, and automated components for sensor design, fabrication, and characterization.

Section: Overview Of Recent Innovations Addressing Different Challengesmentioning

confidence: 99%

Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges

Tovar-Lopez

2023

“…The work in [7] proposed a tree-cutting system based on LoRa. As in other works, it is based on sound sensors and accelerometers, using microcontrollers and systems on a chip (i.e., Arduino and Raspberry Pi) for sensor data elaboration and GPS data acquisition.…”

Section: Illegal Tree-cutting Detectionmentioning

confidence: 99%

“…LoRa is a physical layer technology operating in Industrial, Scientific and Medical (ISM) radio bands at 433 MHz, 868 MHz, and 915 MHz frequencies [6]. Furthermore, the LoRaWAN open protocol is implemented for the MAC layer, providing a data rate between 0.25 to 5.5 kbps depending on bandwidth, spreading factor, and coding rate, with wide coverage and low battery consumption [7]. The LoRaWAN standard envisages a network architecture organized in a star topology, foreseeing gateways that receive messages from end devices (i.e., IoT edge nodes) through LoRa.…”

Section: Wireless Communicationmentioning

confidence: 99%

“…Concerning WSN technologies and protocols, besides low-power short-range networks based on Zigbee and Bluetooth [5], Long Range (i.e., LoRa) [6] solutions have been introduced to achieve Low-Power Wide-Area Networks (LPWANs). LoRa offers low-power, low-cost, long-range, and long-term functionality with no maintenance, thus providing wide coverage and low battery consumption [7]. Adopting this long-range communication technology is very useful for designing and implementing capillary and pervasive sensing scenarios such as logging detection systems in forests and rural areas.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Illegal Tree Cutting through Ultra-Low-Power Smart IoT Devices

Andreadis

Giambene

Zambon

2021

Forests play a fundamental role in preserving the environment and fighting global warming. Unfortunately, they are continuously reduced by human interventions such as deforestation, fires, etc. This paper proposes and evaluates a framework for automatically detecting illegal tree-cutting activity in forests through audio event classification. We envisage ultra-low-power tiny devices, embedding edge-computing microcontrollers and long-range wireless communication to cover vast areas in the forest. To reduce the energy footprint and resource consumption for effective and pervasive detection of illegal tree cutting, an efficient and accurate audio classification solution based on convolutional neural networks is proposed, designed specifically for resource-constrained wireless edge devices. With respect to previous works, the proposed system allows for recognizing a wider range of threats related to deforestation through a distributed and pervasive edge-computing technique. Different pre-processing techniques have been evaluated, focusing on a trade-off between classification accuracy with respect to computational resources, memory, and energy footprint. Furthermore, experimental long-range communication tests have been conducted in real environments. Data obtained from the experimental results show that the proposed solution can detect and notify tree-cutting events for efficient and cost-effective forest monitoring through smart IoT, with an accuracy of 85%.

“…Sensor nodes can send data obtained from an environment to a sink in one hop or multiple hops. WSNs have been widely used for environment detection, surveillance and defense reconnaissance, and medical monitoring [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks

Han

Byun

Zhang

2020

Prolonging the network lifetime is one of the fundamental requirements in wireless sensor networks (WSNs). Sensor node clustering is a very popular energy conservation strategy in WSNs, allowing to achieve energy efficiency, low latency, and scalability. According to this strategy, sensor nodes are grouped into several clusters, and one sensor node in each cluster is assigned to be a cluster head (CH). The responsibility of each CH is to aggregate data from the other sensor nodes within its cluster and send these data to the sink. However, the distribution of sensor nodes in the sensing region is often non-uniform, which may lead to an unbalanced number of sensor nodes between clusters and thus unbalanced energy consumption between CHs. This, in turn, may result in a reduced network lifetime. Furthermore, a different number of clusters lead to a different quality of service of a WSN system. To address the problems of unbalanced number of sensor nodes between clusters and selecting an optimal number of clusters, this study proposes an energy-balanced cluster-routing protocol (EBCRP) based on particle swarm optimization (PSO) with five mutation operators for WSNs. The five mutation operators are specially proposed to improve the performance of PSO in optimizing sensor node clustering. A rotation CH selection scheme based on the highest residual energy is used to dynamically select a CH for each cluster in each round. Simulation results show that the proposed EBCRP method performs well in balancing energy consumption and prolonging the network lifetime.