Abstract
It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation under seven IoT device distribution scenarios. The swarm intelligence algorithms we used are flower pollination algorithm (FPA), salp swarm algorithm (SSA), sine cosine algorithm (SCA). FPA and SCA perform most efficiently in three and four scenarios among the seven IoT device scenarios, respectively.
Highlights
Internet of things (IoT) is a research hotspot under the trend of smart living in the future, and it is an important part of the Internet in the future
In order to verify the effect of unmanned aerial vehicle (UAV) deployment optimization based on swarm intelligence, experimental simulations are performed according to the coding scheme proposed in this paper using the algorithms salp swarm algorithm (SSA), flower pollination algorithm (FPA), and sine cosine algorithm (SCA) as basic algorithms
This paper studies the deployment optimization of UAV as the data collection platform of Internet of things devices, and proposes a coding scheme based on swarm intelligence algorithm
Summary
Internet of things (IoT) is a research hotspot under the trend of smart living in the future, and it is an important part of the Internet in the future. We take the set of location coordinates as a spatial dimension, and use a specific swarm intelligence algorithm to obtain a more optimal solution based on the synergistic evolution between individuals of the population.
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