A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network.
The right moment to harvest apples in fruit orchards is still decided after persistent monitoring of the fruit orchards via local inspection and using manual instrumentation. However, this task is tedious, time consuming, and requires costly human effort because of the manual work that is necessary to sample large orchard parcels. The sensor miniaturization and the advances in gas detection technology have increased the usage of gas sensors and detectors in many industrial applications. This work explores the combination of small-sized sensors under Unmanned Aerial Vehicles (UAV) to understand its suitability for ethylene sensing in an apple orchard. To accomplish this goal, a simulated environment built from field data was used to understand the spatial distribution of ethylene when subject to the orchard environment and the wind of the UAV rotors. The simulation results indicate the main driving variables of the ethylene emission. Additionally, preliminary field tests are also reported. It was demonstrated that the minimum sensing wind speed cut-off is 2 ms−1 and that a small commercial UAV (like Phantom 3 Professional) can sense volatile ethylene at less than six meters from the ground with a detection probability of a maximum of 10%. This work is a step forward in the usage of aerial remote sensing technology to detect the optimal harvest time.
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