Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

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.

[1]  Reza Ghanbari,et al.  Efficient Deployment of Small Cell Base Stations Mounted on Unmanned Aerial Vehicles for the Internet of Things Infrastructure , 2020, IEEE Sensors Journal.

[2]  Yong Wang,et al.  A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing , 2020, IEEE Transactions on Cybernetics.

[3]  Rui Zhang,et al.  Energy-Efficient Data Collection in UAV Enabled Wireless Sensor Network , 2017, IEEE Wireless Communications Letters.

[4]  Karandeep Kaur,et al.  A Survey on Internet of Things – Architecture, Applications, and Future Trends , 2018, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC).

[5]  Nei Kato,et al.  Effectively Collecting Data for the Location-Based Authentication in Internet of Things , 2017, IEEE Systems Journal.

[6]  Carlos Guedes Soares,et al.  On Connectivity of UAV-Assisted Data Acquisition for Underwater Internet of Things , 2020, IEEE Internet of Things Journal.

[7]  Yong Wang,et al.  Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.

[8]  Jeng-Shyang Pan,et al.  An Improved Flower Pollination Algorithm for Optimizing Layouts of Nodes in Wireless Sensor Network , 2019, IEEE Access.

[9]  Jun Wang,et al.  UAV-assisted wireless powered Internet of Things: Joint trajectory optimization and resource allocation , 2020, Ad Hoc Networks.

[10]  Tarik Taleb,et al.  UAV-Based IoT Platform: A Crowd Surveillance Use Case , 2017, IEEE Communications Magazine.

[11]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[12]  Yang Chen,et al.  An innovative flower pollination algorithm for continuous optimization problem , 2020 .

[13]  Qingqing Wu,et al.  Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[14]  Yang Chen,et al.  Enhanced global flower pollination algorithm for parameter identification of chaotic and hyper-chaotic system , 2019, Nonlinear Dynamics.

[15]  Haichao Wang,et al.  Energy-Constrained Completion Time Minimization in UAV-Enabled Internet of Things , 2020, IEEE Internet of Things Journal.

[16]  Yohanes Khosiawan,et al.  Task scheduling system for UAV operations in indoor environment , 2016, Neural Computing and Applications.

[17]  Qi Kang,et al.  Opposition-Based Hybrid Strategy for Particle Swarm Optimization in Noisy Environments , 2018, IEEE Access.

[18]  Susan E. Meyer,et al.  Application of UAV-Based Methodology for Census of an Endangered Plant Species in a Fragile Habitat , 2019, Remote. Sens..

[19]  Walid Saad,et al.  Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications , 2017, IEEE Transactions on Wireless Communications.

[20]  Ning Zhang,et al.  Joint Unmanned Aerial Vehicle (UAV) Deployment and Power Control for Internet of Things Networks , 2020, IEEE Transactions on Vehicular Technology.

[21]  Sara Arabi,et al.  Data Gathering and Energy Transfer Dilemma in UAV-Assisted Flying Access Network for IoT , 2018, Sensors.

[22]  Mohammad Alshinwan,et al.  Salp swarm algorithm: a comprehensive survey , 2019, Neural Computing and Applications.

[23]  Yang Chen,et al.  Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting , 2019, Connect. Sci..

[24]  Obaid Ur Rehman,et al.  A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications , 2020, IEEE Access.

[25]  Kezhi Wang,et al.  Unified Offloading Decision Making and Resource Allocation in ME-RAN , 2017, IEEE Transactions on Vehicular Technology.

[26]  Li Yang,et al.  Designing Mission Abort Strategies Based on Early-Warning Information: Application to UAV , 2020, IEEE Transactions on Industrial Informatics.

[27]  Vimal J. Savsani,et al.  Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems , 2017, Neural Computing and Applications.

[28]  Walid Saad,et al.  Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture? , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).