Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Abstract

Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

Image Recognition Applied to Security Systems: The Case of Burkina Faso

“…The learning base is also a determining element in convolutional neural networks, it is necessary to have a large learning base to achieve the best results. The use of unmanned drones for aerial reconnaissance and evidence collection has the following characteristics: fast response, high real-time performance, and faithful and reliable images [39,40,41,42,43,44]. For future work it is desirable to apply our model to the use of drones for aerial image recognition.…”

WSN Architectures for Environmental Monitoring Applications

“…Hardware and software platforms supporting IoT for EMAs include Arduino, ESP8266, Raspberry Pi, Beagle Bone, Bluetooth, Wi-Fi, RFID, and microcontrollers [9]. Large-scale applications such as unmanned aerial vehicles (UAVs) and crowdsensing monitoring technologies also use radio and WSN protocols to achieve comprehensive area monitoring [2]. Smart cities have peculiar environmental monitoring concerns such as authentication, data security, device vulnerability, and sustainability.…”

“…As illustrated in Figure 1, WSNs have practical applications in various domains, including agriculture, water, animal tracking, oceanography, air quality, earthquake/landslide, forest fire, and flood detection. WSNs are self-configuring, infrastructure-free networks that monitor physical or environmental conditions [2]. WSNs can monitor various environmental conditions, including temperature, sound, vibration, acceleration, pressure, motion, humidity, and chemical or pollutant concentrations from the different application domains presented in Figure 1.…”

“…For instance, deploying nodes in highly dynamic environments may affect the data collected even for environments with low spatiotemporal variations. Such changes may arise from sudden changes in the weather or close human activities [2,5]. As such, real-time monitoring of the environment is necessary but without the cost and time needed to achieve even better results when compared to traditional monitoring systems.…”

Comparison between Supervised and Unsupervised Learning for Autonomous Delamination Detection Using Impact Echo

“…The performance of the developed models was also evaluated using the IE data of a bridge deck available on InfoBridge. Combining signal processing with other health monitoring devices, such as UAVs, to monitor structure health has been the subject of much current research [26][27][28][29]. Fascista et al reviewed UAV applications and assisted structural health monitoring to monitor bridge health conditions [26] and address using drones for infrastructure inspection.…”