A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring
Abstract
:1. Introduction
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- offline versus online, which differentiates between the flight preparation phase or during the actual flight;
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- coverage versus waypoint passing: the former entails a discovery phase in which features like sensor position are estimated while the latter focuses on the interaction with the WSN;
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- static versus dynamic: is the path update carried out at runtime or not?
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2. Materials and Methods
3. Collaborative Operation in UAV–WSN Applications
3.1. Collaboration and Intelligence in the UAV–WSN System
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- for node deployment [30] in applications with low on-site accessibility or dangerous for human operation;
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- as actuators, as in [37] where drones are used for spraying pesticides;
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- as mobile sinks, this being the most widely used role integrated with either small or large scale WSNs. In this case, two data acquisition modes were identified:
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- clustered, when data is sent to a local CH;
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- direct communication, where the UAV collects data from each sensor node.
3.2. Satellite Information
3.3. UAV Path Generation and Tracking
3.3.1. Limitations in Path Generation and Tracking
3.3.2. Waypoint Selection, Ordering and Passing Through
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- characteristics of the used UAV agent: available energy, type (rotary or fixed wing); type of antenna and speed;
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- characteristics of the environment: if there are obstacles or the application is in open field; the availability of ground base-stations along the entire path.
3.3.3. Computational Aspects
3.4. Data Acquisition
Data Mule Scenario for Data Acquisition
3.5. Data Processing
4. Data Communication
4.1. Requirements and Protocols
4.2. Standardisation and Safety Considerations
5. Integrated UAV–WSN System Implementation
6. Applications
6.1. Agriculture
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- Some areas might not have the proper amount of chemicals, while other might have a higher level;
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- The efficiency of the process is highly influenced by weather conditions;
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- The chemicals must be spread only inside a predefined boundary.
6.2. Environment
6.3. Disaster Management
6.4. Simulators for UAV–WSN Systems
7. Discussion
8. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Acronym/Symbol | Description | Acronym/Symbol | Description |
---|---|---|---|
ACO | Ant colony optimization | mUAV | Multiple unmanned aerial vehicles |
AODV | Ad hoc on demand distance vector | NLoS | Non-line of sight |
BSN | Body sensor network | NN | Nearest neighbor |
CDR | Conflict detection and resolution | PCWAS | Priority-based contention window adjustment scheme |
CH | Cluster head of WSN | POFS | Priority-based optimized frame collection |
Csma/Ca | Carrier sense multiple access/collision avoidance | PSO | Particle swarm optimization |
cUAV | Unmanned aerial vehicle—multi copter type | RRT* | Optimal rapidly exploring random trees |
DS | Deterministic scanning—method for AC path planning | RRT | Rapidly exploring random trees |
FPPWR | Fast path planning with rules | RSSI | Received signal strength indicator |
FSRP | Frame selection-based routing protocol | SIG | Sensing information gathering |
GA | Genetic algorithm | SN | Sensor node |
GCS | Ground control station | STTT | Shortest travelling time trajectory |
GDT | Ground data terminal | TDMA | Communication through time-division multiple access |
GPS | Global positioning system | TSP | Travelling salesman problem |
GPSR | Greedy perimeter stateless routing | TTM | Threshold time minimization routing protocol |
GSM | Global system for mobile communications | UAV | Unmanned aerial vehicle |
IoT | Internet of things | VSI | Value of sensor information |
LLRA | Low-latency routing algorithm | WOS | Web of science |
MI | Mixed integer | WSN | Wireless sensor network |
MINLP | Mixed integer non-linear programming | wUAV | Unmanned aerial vehicle—fixed wing type |
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Constraints and Costs | Method | Application | Miscellaneous | Reference |
---|---|---|---|---|
path length | GA | crop monitoring | cUAV, experimental results | [22] |
WSN-based path update, communication radius, energy consumption | TSP | pesticide spraying, crop monitoring | cUAV, multi-pass, grid sensor deployment, experimental results | [19,24,25] |
harsh-undulating terrain, communication radius, energy consumption | MINLP, TSP, FPPWR | data gathering | cUAV, forward and axial flight | [15,16] |
path length, communication radius, energy consumption | greedy algorithm | data gathering | cUAV, experimental results | [50] |
NA | Particle filtering, Bayesian analysis | data gathering | cUAV, node localization based on RSSI value, experimental testing | [45] |
travel time, energy consumption | PSO, LEACH-C | data gathering | online CH selection | [55] |
path length, energy consumption, communication range | heuristic methods, greedy algorithm | data gathering | spiral, zig-zag, strip-based paths | [17,18] |
obstacle avoidance | MINLP, GA | area coverage, photogrammetry | mUAV, sensor placement | [28] |
static/mobile obstacle avoidance | RRT, RRT*, GA | data gathering | mUAV, wUAV, experimental results | [46,56] |
NA | heuristic algorithms, TSP | target tracking, area coverage | mUAV, experimental results | [48] |
sensor lifetime | linear programming | data gathering | limited sensor buffer capacity | [21] |
path length, travel time | heuristic methods | sensor node localization, data gathering | wUAV, multi-pass, experimental results, zig-zag path, sensor deployment | [47] |
communication radius, path length, energy consumption | MINLP | data gathering | wUAV, multiple GDTs | [20] |
obstacle avoidance, communication radius, path length, CH selection and estimation | MINLP, heuristic methods | precision agriculture | wUAV, B-spline parametrization | [1,57] |
path length, area coverage | GA, PSO, Simulated Annealing, Hill-Climbing | pesticide spraying | experimental results | [49,58] |
energy consumption, collision avoidance | MINLP, GA, RSCA, Set Cover Problem | data gathering | mUAV, TDMA, cyclic path | [59] |
multiobjective utility function; prohibited, flying and sensing cells | GA, A* algorithm | data gathering over large areas | heterogeneous IoT devices | [60,61] |
UAV payload and control restrictions | TTM | data gathering from sparse networks | experimental results, acoustic signals | [62] |
path time, energy consumption | TSP, STTT | data gathering, recharging of depleted IoT devices | IoT devices | [44] |
Data Acquisition Mode | Data Processing Characteristics | Optimization/Intelligence | Implementation/Description | Reference |
---|---|---|---|---|
UAV as relay between disconnected WSN nodes | Local processing, Fog computing | Universal Plug and Play node discovery; services-based approach | Large scale IoT applications | [73] |
WSN–UAV–Cloud | Fog computing | Area coverage | Distributed WSN | [50,74] |
UAV as mobile sink | Local processing at UAV level | Increasing reliability, maximizing data collection | cUAV—multiple WSN, path model; UAV—ground Rician fading channel model; data collection model | [77] |
WSN–UAV; UAV as mobile sink; WSN nodes with wake-up receivers | UAV local processing | A calibration step used for path planning, a probability map for data collection is built | Small scale WSN | [78,79] |
WSN–UAV; WSN with both normal and GPS sensors; UAV as mobile sink | Node localization, grid division and path planning at base station UAV local processing | Aerial node deployment Node localization Flying path optimization | Large scale WSN | [16,18] |
WSN–multiple wUAV UAV as mobile sink | UAV local processing | Path planning, CDR (Conflict Detection and Resolution) | Multiple small scale WSN; Matlab simulations for trajectory; Real experiments using Megastar wUAV | [46] |
UAV as relay between cluster head and GCS | Only messaging for data acquisition is considered at WSN and UAV | Energy consumption optimization | Mathematic simulations evaluating the effect of distance between cluster head and base station | [38] |
UAV as relay between WSN cluster heads | Only messaging for data communication is considered at WSN and UAV | Method for choosing the cluster head and routing protocol | Sparse WSNs with unbalanced traffic | [64] |
UAV as relay between WSN cluster heads | Only messaging for data communication is considered at WSN and UAV | Maximize area coverage | Sparse WSNs | [26] |
Multiple UAVs as relays between WSN cluster heads | Path computing at UAV level | Different messaging architectures Minimization of the sum of all distances | Sparse WSNs; Mathematical simulations evaluating different architectures and path planning methods | [39] |
UAV as relay between WSN cluster heads and sink nodes | Only messaging for data communication is considered at WSN and UAV | Reduce the consumption in data transmission; framework for monitoring linear infrastructure | Linear (deployed) sensor network with sink at the end line. | [40] |
Multiple UAVs as relays between WSN nodes | Only messaging for data communication is considered at WSN and UAV | UAV positioning | Multiple faults in linear WSN | [42] |
Multiple UAVs as mobile nodes | UAV route processing | Randomly selected routes Full area coverage | Mathematical analysis of the area coverage with the proposed algorithm | [41] |
Multiple UAV- WSN; UAV as mobile sink | Data aggregation | Path planning UAV energy optimization | Large area WSN with scattered nodes; OmNET++Simulator | [59] |
WSN–UAV–IoT | Data aggregation; Animal movement prediction; UAV route processing | Path planning defined by external factors | Large area WSN; Simulations using Zebranet dataset | [80] |
Data aggregation UAV route processing | Path design to four functions: sensing, energy, time, and risk | Large area WSN | [60] | |
Local processing, cloud computing | Energy consumption | Farm beats gateway for data collection from WSN, UAV; Azure Cloud | [81] | |
UAV for WSN node localization | UAV route processing | Node position estimation Path planning | Large scale WSN; Mica2 Crossbow as WSN nodes | [45] |
UAV for WSN node deployment | Messaging for status communication is considered at WSN and UAV | Node deployment reliability | AVATAR Autonomous helicopter; Mica Motes for WSN nodes | [30] |
WSN–UAV–BSN | Messaging for status and data communication | Latency, reliability, network dynamics | OmNET++Simulator | [82] |
Data Communication Type | Standards (Implementation) | Details | Reference |
---|---|---|---|
WSN–mUAV–fog | GSM, UMTS, HSPA | Standards are evaluated considering a bandwidth for sending video data from 1000 Kbps to 6000 Kbps | [73] |
wUAV–WSN (CHs) | ZigBee | Distance between UAV and WSN below 100 m for communication | [95] |
UAV–WSN UAV as mobile sink | ZigBee | Nodes in stand-by mode; WSN nodes with wake-up receivers; Point-to-point wake-up | [79] |
UAV–IoT | IEEE 802.11 (WiFi) For wide areas: LoRaWAN, LTE, LTE-A, IEEE 802.16 (WiMAX) | Data collection and energy harvesting | [44] |
UAV–WSN | LoRa | Distance between UAV and WSN 4 km | [10] |
UAV–WSN | TinyMesh | Distance between UAV and WSN 485 m | [96] |
UAV–WSN | RF | Distance between UAV and WSN 1–2 km | [97] |
UAV–WSN | RF@902-928 MHz | 200 m in NLoS conditions | [62] |
UAV–WSN | RF@900 MHz | UAV broadcasts wake-up messages every 1 s. UAV speed and ground distance not available | [45] |
UAV–WSN | RF@916 MHz | Maximum air-ground distance 13 m, median range 9 m | [30] |
UAV–WSN (single hop) | BLE | Nodes wake up each 10 s Distance between 10 m and 20 m | [98] |
cUAV–mWSN | CSMA/ CD/ IEEE 802.15.4 | Nodes in stand-by mode, wake up for sensing or for UAV data transmission, distance between 10 m and 30 m | [22] |
cUAV–WSN | IEEE 802.15.4g for data communications (920 MHz) IEEE 802.11n@5GHz for UAV ground control | Different wake-up mechanisms: broadcast and unicast, nodes in stand-by mode, a wake-up receiver installed at WSN nodes | [78] |
UAV–WSN | IEEE 802.15.4 for data acquisition from WSN, 6LoWPAN for data sinking | Dual stack single radio architecture, algorithm for improving data transmission reliability | [71] |
UAV–UAV | UHF band (400 MHz) | Direct communication, in near line of sight or in non-line of sight conditions | [90] |
cUAV–cUAV | OLSR dynamic protocol, Ad-hoc Wi-Fi infrastructure | One UAV serves as a relay between another UAV and GCS | [92] |
cUAV–cUAV | RTSP and RTP protocols | Video processing pipeline and control, quad copter AR Drone | [93] |
cUAV–GCS–cUAV | 4G/LTE, DR 915 MHz Radio Telemetry (UAV-GCS), IEEE 802.3 32-bit CRC polynomial | Indirect communication, connectivity for low altitude UAV through a terrestrial 4G/LTE network | [91] |
System Architecture | System Configuration | System Implementation | Reference |
---|---|---|---|
UAV–WSN | cUAV, wUAV—single WSN | Quadrotor | [22] |
Miniature UAV—single WSN | Hero 6 UAV, Mica2 WSN nodes | [45] | |
UAV for WSN node deployment | AVATAR helicopter, Mica Mote WSN nodes | [30] | |
cUAV–WSN | Different simulators: OmNET++, Glomosim, SSFNet, ns2, Java-Sim | [24] | |
wUAV–WSN | Fury UAV | [62] | |
mUAV with specific functions: mobile sink, node deployment | OmNET++ simulations | [82] | |
Multiple wUAV–WSN | Simulations using Matlab and C++, real evaluation using Megastar wUAV | [46,56] | |
wUAV–WSN | Matlab simulation | [100] | |
wUAV–WSN (single hop) | DUNE software to communicate with X8 UAV Pandaboard at UAV level ATXMEGA192C3 for sensor node | [97] | |
wUAV–WSN (single hop) | PIC24F PCB as sensor node BeagleBone Black (BBB) board as beacon node, buoy, Delta wUAV | [10] | |
wUAV–WSN (single hop) | Silicon Labs EFM32 Gecko microcontroller as WSN node, TBR-700 receiver and X8 UAV for field tests, Phantom quadrocopter for off-shore tests | [96] | |
cUAV–WSN | DJI Phantom 4 UAV, embedded controller for WSN nodes, PC with an Android terminal for ground control | [78] | |
UAV–WSN–IoT | mUAV–WSN | OmNET++Simulator | [108] |
cUAV | Simulations using Arduino boards for both WSN and UAV | [73] | |
cUAV–WSN | Arduino, Particle Photon or NodeMCU boards at sensor level, embedded Farmbeats gateway at UAV level, DJI Phantom 2, Phantom 3 and Inspire UAVs, Raspberry Pi at base station, Azure Cloud | [81] |
Application Domain | Application Details | Implementation Characteristics | Reference |
---|---|---|---|
Agriculture | Data driven, IoT base station, prediction models | Farm beats gateway for data collection from WSN, UAV Azure Cloud | [81] |
Crop monitoring in vineyards | Quadrotor | [22] | |
Pesticide spraying | Simulations with OMNeT++ and MiXiM | [24,37,58] | |
Precision agriculture | cUAV, multi-pass, grid sensor deployment, experimental results | [25] | |
Crop and soil monitoring in vineyards integrating environmental data with multispectral images | mUAV WSN node: Arduino | [23,109,110] | |
Farmland environmental monitoring | WSN sensor and relay nodes Octocopter | [111] | |
Environment monitoring | Underwater monitoring | Simulations using Arduino boards | [73] |
Ambient air pollution monitoring | Quadrocopter | [112] | |
Ambient monitoring: temperature, humidity, light intensity, wind speed | Fixed-wing UAV | [90] | |
Marine environment monitoring | PIC24F PCB/ ATXMEGA192C3 as sensor node, BeagleBone Black (BBB) board as beacon node, buoy, Delta mUAV | [10,96,97] | |
Animal monitoring | Endangered species movement monitoring without any attached devices | Simulations using Zebranet dataset | [80,113] |
Disaster monitoring/ emergency | Situational awareness, pre-event and post-event activity functions, integration with BSN | OmNET++ Simulator | [82] |
Disaster monitoring, situational awareness, pre-event and post-event activity functions | Framework model | [114] | |
Natural disaster management | Evaluation of different implementation solutions | [115] | |
Disaster recovery, post-event functions | Quadrocopter | [116] | |
Situational awareness, post-event activity functions | Numerical simulations of communication cases | [117] | |
Disaster monitoring, situational awareness | Framework architecture | [118] | |
Flood monitoring, post-event functions | Quadrocopter | [119] | |
Transport monitoring | Ground pipeline monitoring and control | Simulations using Arduino boards | [73] |
Energy-efficient urban surveillance, Intelligent transportation system | Multicopters in LoRaWAN-like networks | [120] | |
Utilities | Power meter reading | Sinalgo Simulator, scenarios with up to 16,000 nodes | [53] |
Multimedia | Capturing HD/3D content with augmented reality | Platform description | [121] |
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Popescu, D.; Stoican, F.; Stamatescu, G.; Chenaru, O.; Ichim, L. A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring. Sensors 2019, 19, 4690. https://doi.org/10.3390/s19214690
Popescu D, Stoican F, Stamatescu G, Chenaru O, Ichim L. A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring. Sensors. 2019; 19(21):4690. https://doi.org/10.3390/s19214690
Chicago/Turabian StylePopescu, Dan, Florin Stoican, Grigore Stamatescu, Oana Chenaru, and Loretta Ichim. 2019. "A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring" Sensors 19, no. 21: 4690. https://doi.org/10.3390/s19214690
APA StylePopescu, D., Stoican, F., Stamatescu, G., Chenaru, O., & Ichim, L. (2019). A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring. Sensors, 19(21), 4690. https://doi.org/10.3390/s19214690