Abstract
Recently, unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. A group of UAVs with on‐board cameras usually monitors or collects information about designated areas. The UAVs can build a distributed network to share/exchange and to process collected sensing data before sending to a data processing center. A huge data transmission among them may cause latency and high‐energy consumption. This paper deploys artificial intelligent (AI) techniques to process the video data streaming among the UAVs. Thus, each distributed UAV only needs to send a certain required information to each other. Each UAV processes data utilizing AI and only sends the data that matters to the others. The UAVs, formed as a connected network, communicate within a short communication range and share their own data to each other. Convolution neural network (CNN) technique extracts feature from images automatically that the UAVs only send the moving objects instead of the whole frames. This significantly reduces redundant information for either each UAV or the whole network and saves a huge energy consumption for the network. The UAVs can also save energy for their motion in the sensing field. In addition, a flocking control algorithm is deployed to lead the group of UAVs in the working fields and to avoid obstacles if needed. Simulation and experimental results are provided to verify the proposed algorithms in either AI‐based data processing or controlling the UAVs. The results show promising points to save energy for the networks.
Highlights
Autonomous unmanned aerial vehicles (UAVs) networks have been deployed in many applications in both military and civil fields
UAV networks operate in a distributed fashion to improve robustness and Wireless Communications and Mobile Computing reduce communication burden as an UAV only needs to connect with its neighbors
The rest of this paper is organized as follows: Section 2 provides briefly the system models that describe either the UAV network deploying in the sensing field or the AIbased methods to process the surveillance data collected from the UAVs
Summary
Autonomous UAV networks have been deployed in many applications in both military and civil fields. UAV networks operate in a distributed fashion to improve robustness and Wireless Communications and Mobile Computing reduce communication burden as an UAV only needs to connect with its neighbors Besides information sharing, another consideration is control algorithms for multiple UAV formations. An AI-based method is proposed with the aim of reducing redundant data for the UAVs while performing surveillance tasks of collecting data. The rest of this paper is organized as follows: Section 2 provides briefly the system models that describe either the UAV network deploying in the sensing field or the AIbased methods to process the surveillance data collected from the UAVs. In Section 3, the whole problems are addressed.
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