Name
Abstract | ||
|---|---|---|
In most cases, the batteries of sensor nodes in the Internet of Things (IoT) are usually constrained by size and weight, and are difficult to recharge or replace. In traditional wireless sensor networks, data is transmitted in a multi-hop manner, which may cause the high data transmission delay and unbalanced traffic load. In this paper, an Unmanned Aerial Vehicle (UAV)-assisted IoT architecture is introduced, in which UAV is utilized to achieve low-latency and seamless-coverage acquisition of the sensing data. Furthermore, based on the recent advances on deep reinforcement learning algorithms, considering both data delay requirements and network energy consumption, a real-time flight path planning scheme of the UAV in the dynamic IoT sensor networks has been proposed based on dueling deep Q-network (DQN). Besides, the grid-based method is used to handle the network state modeling, which effectively reduces the complexity of the proposed scheme. Simulation results show that the proposed scheme significantly improves the network performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8762097 | IEEE International Conference on Communications |
Keywords | Field | DocType |
UAV-assisted IoT,green mobility management,flight path,deep reinforcement learning,grid-based method | Mobility management,Data transmission,Computer science,Internet of Things,Real-time computing,Wireless sensor network,Energy consumption,Grid,Network performance,Reinforcement learning | Conference |
ISSN | Citations | PageRank |
1550-3607 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| wenqi liu | 1 | 208 | 10.74 |
| Pengbo Si | 2 | 186 | 25.23 |
| Enchang Sun | 3 | 21 | 8.33 |
| Meng Li | 4 | 36 | 6.94 |
| Chao Fang | 5 | 96 | 15.65 |
| Yanhua Zhang | 6 | 145 | 24.84 |