Abstract
By using drones as mobile IoT gateways in wireless sensor networks, data collection can be possible even in areas where IoT wireless communication is not available. A drone can move near the rendezvous node of the wireless sensor networks and receive the collected data from the node. Then, the drone moves to an area where IoT wireless communication is possible and transfers the data to the IoT server. Because drones operate as a battery, it is important to optimize the energy consumption. Existing research reduces the drone's energy consumption by minimizing flight distance using predetermined information such as the orbit, sensor location and network topology. However, if data collection fails, the additional flying distance of the drone increases dramatically and energy consumption also increases. It is important to increase the success rate of data collection. In this paper, we propose a dynamic rendezvous node estimation scheme, taking into account the average of drone speed and data collection latency without predetermined information. Simulation results show that the proposed scheme is more reliable in terms of the data collection success rate.
Highlights
As drone-related technologies are developed, many applications presently use drones [1]–[4]
Drones act as a mobile Internet of Things (IoT) gateway [7], [8], which is in the middle of sensor networks and the IoT server
In the case of IoT’s including wireless sensor networks, it is important to reduce energy consumption to prolong the lifetime of the networks
Summary
As drone-related technologies are developed, many applications presently use drones [1]–[4]. The sink node is a rendezvous point, where the drone can communicate with the sensor networks. We propose a new dynamic rendezvous node selection scheme based on an estimated proper rendezvous point considering the drone’s speed and direction as well as the data collection latency of the sensor network without any pre-acquisition information. Considering the drone speed, data collection time in the network, and the data transmission time with the drone, the proposed method selects the node that can maximize the data transmission success rate as the rendezvous node. The rendezvous node selection is optimized according to the drone’s flight path, which increases the success rate of attempting to transfer the collected data to the drone.
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