Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power. A corresponding system model is summarized based on the scenario and existing theoretical works. The minimum throughput-maximizing object is then formulated as an optimization problem. As swarm intelligence algorithms are utilized effectively in numerous fields, this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms. This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem, as joint time and energy optimization have two sets of variables. The proposed method performs well in terms of stability and duration. Finally, performance is evaluated through numerical experiments. Simulation results demonstrate that the proposed method performs admirably in the given scenario.

The Internet of Things has evolved and advanced over the past few years as a result of the development of wireless communication technology, specifically fifth-generation mobile communication technology (5G) and 5G beyond [

Sensors play a vital role in the Internet of Things systems. IoT systems, which are required for decision-making and execution, require data. In a typical scenario, sensors collect data and transmit it through networks to a location for further processing in real-time. In addition, the form sensors can operate independently or be integrated into a complex device. For scenarios such as environment monitoring and collecting multiple types of data, sensors with distinct functions can form a specialized unit.

In this paper, a potential scenario for a marine sensor system is presented. The sensors system consists of multiple sensor stations located in the ocean, beyond the range of terrestrial wireless communication networks. These sensor stations float at a specific location on the sea cliff. These stations lack a hardwired power and communication connection. Since solar and ocean waves generate the station’s energy, its energy resource is quite limited (it can not support satellite communication). The collected data are saved on storage and do not require real-time transmission. Then, a solution for retrieving data from this system is provided. Unmanned Aerial Vehicles (UAVs) are utilized as data retrievers in this system. The UAVs are fuel-powered and rich in resources. They retrieve data daily or weekly, depending on operational requirements. Using Wireless Power Transfer (WPT) technology, the UAVs provide power to the communication module on the sensor stations as they fly over them. The module is activated when it has sufficient power [

This paper’s contribution is summarized in the following section. First, a scenario of the UAV application is described in this paper. The model corresponds to the specification, and the optimization problem is formulated. A method that converts multi-decisions into a row vector is then implemented to qualify the problem for most swarm intelligence algorithms. Two cutting-edge swarm intelligence algorithms are then utilized to solve the optimization problem. The outcomes of the two algorithms are analyzed and compared.

Section 2 reviews some related work in IoT and UAV-assisted systems. In Section 3, two swarm intelligence algorithm is introduced, and the scheme with them is claimed. Section 4 represents the system model and the settings of the proposed method. In Section 4, the experiment is performed, and a discussion is made on the results. The conclusion of this paper is made at Section 5.

As IoT technologies advance, IoT systems cover more perspectives and can be implemented in a wider variety of scenarios. Numerous related works improve IoT. Qian et al. [

IoT technologies are applicable not only on land but also on the water. Internet of Things-based marine environment monitoring system designed by Haitao C. et al. The system is comprised of the information management subsystem, the data collection subsystem, and the monitoring terminal subsystem. It is constructed with 4G and Zigbee communication technologies. Luccio et al. introduced the Internet of Floating Things (IoFT) in [

There are numerous applications for UAVs in wireless networks described in other works. Khalifeh et al. [

Optimization issues in UAV communication networks are widely discussed. There are works concerned with optimizing force and trajectory [

In addition, additional works address resource allocation in UAV-related communications. Xu et al. [

Harris hawks optimization (HHO) is a swarm-based metaheuristic algorithm designed by Heidari et al. [

The HHO can be generally divided into three phases: the exploitation phase, the transition from exploration to exploitation, and the exploitation phase. In addition, the exploitation phase consists of four strategies: soft besiege, hard besiege, soft besiege with quick dives, and hard besiege with quick dives. The three phases are executed sequentially, and the four strategies are implemented in accordance with the various situations.

In the algorithm, the exploration phase is modeled as

The average position of hawks is obtained by

The prey’s energy decreases, so exploration transit to exploitation. Hence, the formula is given as

The algorithm considers a complex situation between hawks and the prey. A variable

When

When

When

When

Whale Optimization Algorithm (WOA) is also a metaheuristic algorithm based on a swarm [

The whale optimization algorithm consists of three phases: prey search, prey encirclement, and bubble-net attack. This follows the order in which whales hunt their prey, and the purpose of each stage is readily apparent.

The coefficient vectors

When searching for prey, the position vector of the whale can be updated by

When encircling prey, the position vector of the whale can be updated by

For the bubble-net attack, there have two approaches: shrinking encircling mechanism and spiral updating position. One of the two approaches is used each time according to

A UAV-assisted marine sensors system can have many UAVs and sensors. This paper discusses the scenario with two UAVs flying through two neighboring sensors to express the problem and show the result easily. In such a system, sensors exist as floating sensor stations.

In order to maximize efficiency, the flight paths of two unmanned aerial vehicles (UAVs) are predetermined based on the known locations of sensor stations. Each UAV possesses sufficient energy. Before sensors are detected, they will continually transmit power on a particular channel using WPT technologies. When they are within a specific range of sensor stations, they will gain enough energy to transmit a unique beacon to alert the UAV to their presence. Then, unmanned aerial vehicles can prepare to receive data from the sensors station. To ensure the integrity and validity of the data collected by UAVs from sensor stations, the sensor stations will only transmit their data to the first UAV that connects and will complete the transmission before disconnecting.

The channel power gain between Sensors Station

While transmitting data, it can receive energy from the other UAVs without the interface for the communication link. But if not only one communication link is sending data, it will cause an interface for each other. Therefore, the time used to transmit data at Sensors Station

For the case that

The energy harvested at Sensors Station

Total energy received by Sensors Station

The signal-to-interference plus noise ratio at UAV

Then, the average data rate can be calculated as

For the case that

The data rate can be calculated similarly to

The data rate for the interference part should be calculated by

As for the data rate for the whole time slot, it consisted of two parts described, and it should be given as

The cumulative energy harvested by Sensors Station

The cumulative energy consumed by Sensors Station to transmit data at the time slot

The average data rate throughput from Sensors Station

It should have enough throughput to ensure the data can be totally transmitted. The purpose is to maximize the minimum data throughput by jointly optimizing the time allocation and transfer power under limited conditions. The optimization problem is formulated as

During the optimization of the model, two sets of variables must be considered, wherein the time allocation includes a portion of the energy allocation. The number of time slots has a significant impact on the number of variables, as allocations are made accordingly. On the assumption that UAV

The allocation decision must rigorously adhere to the constraints. A portion of the constraints are ensured by the process that generates the allocation decision, but the remainder must be verified whenever the allocation changes. Therefore, a check function is implemented to ensure the constraints. If an allocation decision does not satisfy the constraint, it is immediately replaced with one generated at random.

In this section, numerical results are provided to validate the performance of the proposed solution. The algorithms are coded in MATLAB 2021a, and all tests are performed on a PC with Intel i5-8250U@3.2 GHz and 8GB of RAM.

In the simulation, the noise power that the UAV receives

Experiments are taken to evaluate the model’s performance and the proposed scheme. The solution set gets by completely random and is made as a reference. Apart from HHO and WOA, four algorithms are included in the experiments as compare, which are Particle Swarm Optimization (PSO) [

As the primary indicator of the work’s performance, the minimum data throughput of the model optimized by the aforementioned algorithms serves as the objective. In order to evaluate the efficacy of algorithms on the model, the processing time required by the algorithm is also considered. In conclusion, a swarm intelligence algorithm is a random and self-optimizing algorithm, and the outcome would vary each time. The repeatability and consistency of these results should also be investigated.

Each algorithm executes

Algorithm | Minimum | Average | Maximum |
---|---|---|---|

Random | 450.8259 | 471.1938 | 498.6472 |

WOA | 716.8550 | 733.0590 | 744.2390 |

HHO | 617.7212 | 718.9337 | 756.2619 |

GWO | 651.9074 | 685.8507 | 714.6911 |

PSO | 611.5935 | 646.1055 | 701.9458 |

GA | 452.2596 | 567.8854 | 718.7790 |

SA | 564.3699 | 670.5710 | 710.6549 |

Algorithm | Minimum | Average | Maximum |
---|---|---|---|

Random | 0.0022 | 0.0044 | 0.0110 |

WOA | 0.9846 | 1.1690 | 1.9970 |

HHO | 1.4056 | 1.6930 | 2.4602 |

GWO | 1.2664 | 1.4689 | 2.9823 |

PSO | 0.6072 | 0.8655 | 1.5062 |

GA | 3.2751 | 3.4758 | 3.7906 |

SA | 0.9292 | 0.9897 | 1.1002 |

Processing time reflects the complexity of the algorithm, and it can be observed in

Then, the results of the execution of these algorithms in the

Based on the results of the experiments, it can be concluded that WOA is the optimal solution for the optimization problem presented in this paper. HHO has both benefits and drawbacks. The instability of HHO makes it possible to find the optimal solution. HHO can be used to execute the algorithm multiple times and then select the optimal solution.

This paper describes an optimization strategy for the resource allocation of UAVs in an IoT network. Formulation of the corresponding optimization problem that maximizes the minimum uplink throughput for the sensor station. The article then selects HHO and WOA as the optimization method. Adapting the existing optimization problem to a swarm intelligence algorithm is described. Following this, numerical experiment results are obtained using the algorithms discussed. By analyzing the results, it is possible to conclude that WOA performs the best and can be used as a general optimization technique for the posed problem. In addition, HHO can be utilized in certain circumstances.

In future work, a more promising solution to the problem described in this work will be attempted. In addition, modifications to WOA and HHO would be investigated to improve the outcome, and the two algorithms could be combined.

This research was funded by the

The authors confirm contribution to the paper as follows: conceptualization, S.F. and Y.C.; methodology, S.F. and Y.C.; software, Y.C.; validation, S.F., M.H. and F.S.; formal analysis, S.F. and Y.C.; investigation, Y.C.; resources, M.H.; data curation, Y.C.; writing—original draft preparation, Y.C.; writing—review and editing, S.F. and Y.C.; visualization, Y.C.; supervision, M.H. and F.S.; project administration, M.H. and F.S.; funding acquisition, M.H. All authors have read and agreed to the published version of the manuscript.

The authors declare that they have no conflicts of interest to report regarding the present study.