A Solution for Data collection of Large-Scale Outdoor Internet of Things Based on UAV and Dynamic Clustering

Cao, Huiru; Yao, Huaping; Cheng, Haixiu; Lian, Songyao

doi:10.1109/itaic49862.2020.9338964

Cited by 8 publications

(11 citation statements)

References 20 publications

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“…For Cao et al [ 7 ], the communication used the ZigBee 2.4 GHz protocol and the algorithm to determine the collection path based on an optimization algorithm such as the ant colony algorithm. He presented an experiment with collection at four centralizing points.…”

Section: Work Descriptionmentioning

confidence: 99%

“…A new system for agrometereological data collection in areas lacking communication networks [6] A precision adjustable trajectory planning scheme for UAV-based data collection in IoTs [7] A solution for data collection of large-scale outdoor internet of things based on UAV and dynamic clustering [8] A Survey of Key Issues in UAV Data Collection in the Internet of Things [9] BEE-DRONES: Ultra low-power monitoring systems based on unmanned aerial vehicles and wake-up radio ground sensors [10] Data collection using unmanned aerial vehicles for Internet of Things platforms [11] drone-Enabled Internet-of-Things Relay for Environmental Monitoring in Remote Areas Without Public Networks [12] Dynamic Rendezvous Node Estimation for Reliable Data Collection of a drone as a Mobile IoT Gateway [13] Efficient and Reliable Aerial Communication with Wireless Sensors [14] Efficient data collection by mobile sink to detect phenomena in internet of things [15] Environmental Monitoring Using a drone-Enabled Wireless Sensor Network [16] Internet of Things Data Collection Using Unmanned Aerial Vehicles in Infrastructure Free Environments [17] LoRa Communications as an Enabler for Internet of drones towards Large-Scale Livestock Monitoring in Rural Farms [18] Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges [19] Performance Evaluation of 802.11 IoT Devices for Data Collection in the Forest with drones [20] UAV path planning for emergency management in IoT [21] Area Division Cluster-based Algorithm for Data Collection over UAV Networks [22] A Brief Review of the Intelligent Algorithm for Traveling Salesman Problem in UAV Route Planning [23] Age-optimal trajectory planning for UAV-assisted data collection [24] Age-optimal path planning for finite-battery UAV-assisted data dissemination in IoT networks…”

Section: Reference Titlementioning

confidence: 99%

“…[6] PATP-Precision adjustable trajectory planning [7] Ant colony algorithm [9] TSP-ant Colony Optimization (ACO)…”

Section: Reference Algorithmmentioning

confidence: 99%

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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%

Section: Reference Titlementioning

confidence: 99%

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

Goulart

Pinto

Boava

et al. 2022

Sensors

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“…As examples of use for an off-grid environment, we could mention data collection in agricultural machines [ 26 ], data collection in meteorological stations [ 16 ], data collection in remote utilities managed by sensors [ 18 ], data collection in isolated environments [ 14 ], and other equivalent characteristics of an off-grid environment.…”

Section: Relevant Aspectsmentioning

confidence: 99%

IoT Off-Grid, Data Collection from a Machine Learning Classification Using UAV

Goulart

Pinto

Boava

et al. 2022

Sensors

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IoT encompasses various objects, technologies, communication standards, sensors, actuators in powered environments, and networked communication. The concept adopted here, IoT off-grid, considers an environment without commercial electricity and commercial internet. Managing various utilities with IoT and collecting the relevant information from this environment is the purpose of this project. It uses machine learning to select relevant data. These data are collected safely using a drone that travels through the off-grid stations. A systematic literature mapping is presented, identifying the state of the art. The result is a software architecture proposal with configurations in the drone and off-grid stations that contemplate data collection from the IoT off-grid environment. The results are also presented with different selection algorithms used in machine learning and final execution in the prototype.

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“…These devices are equipped with SIM card, so that they can directly upload sensing data to data center. In addition, some researchers pay attention to data collection with opportunistic networks in urban with the assistance of vehicles [54], [70]- [82]. These vehicles can collect data from IoT devices when they pass by or directly sense data from the urban environment.…”

Section: A Edge Cachingmentioning

confidence: 99%

Edge Intelligence: Empowering Intelligence to the Edge of Network

et al. 2021

Proc. IEEE

129

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Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data is captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protect the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e. edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This article provides a comprehensive survey to edge intelligence and its application areas. In addition, we summarise the development of the emerging research fields and the current stateof-the-art and discuss the important open issues and possible theoretical and technical directions.

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A Solution for Data collection of Large-Scale Outdoor Internet of Things Based on UAV and Dynamic Clustering

Cited by 8 publications

References 20 publications

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

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

IoT Off-Grid, Data Collection from a Machine Learning Classification Using UAV

Edge Intelligence: Empowering Intelligence to the Edge of Network

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