DroneSense: Leveraging Drones for Sustainable Urban-scale Sensing of Open Parking Spaces

Zhang, Dong; Cao, Mingzhe; Ding, Lige; Han, Qiaoyue; Xing, Yunhao; Ma, Huadóng

doi:10.1109/infocom48880.2022.9796795

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…A novel drone-based collaborative sparse-sensing framework droneSense is proposed by Zhao et al [ 29 ]. droneSense selects a minimum number of points of interest (POIs) to schedule drones for physical data sensing and then infers the parking occupancy of the remaining POIs to meet the overall quality requirement.…”

Section: Work Descriptionmentioning

confidence: 99%

Data Collection in an IoT Off-Grid Environment Systematic Mapping of Literature

Goulart

Pinto

Boava

et al. 2022

Sensors

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The goal of this work is to present a systematic literature mapping (SLM) identifying algorithms for the search for data, determining the best path and types of communication between the local server and the drone, as well as possible simulators to validate proposed solutions. The concept, here considered as IoT Off-Grid, is characterized by being an environment without commercial electrical infrastructure and without communication connected to the internet. IoT equipment generates data to be stored on a local server. It collects these data through a drone that searches each local server for later integration with the commercial internet environment. As a result, we have algorithms to determine the best path based on the TSP—travelling salesman problem. Different types of communication between the drone and the server contain the data, predominantly WiFi 802.11. As a simulator, OMNeT++ stands out.

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Section: Work Descriptionmentioning

confidence: 99%