Due to the fact that network space is becoming more limited, the implementation of ultra-dense networks (UDNs) has the potential to enhance not only network coverage but also network throughput. Unmanned Aerial Vehicle (UAV) communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes. A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article. In the IoT system, the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless nodes. The quality of service (QoS) offered by the cognitive node was interpreted as a price-based utility function, which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’ net utility functions. An energy-efficient non-cooperative game theory power allocation with pricing strategy abbreviated as (EE-NGPAP) is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things (IoT) wireless nodes. It has also been demonstrated, theoretically and by the use of simulations, that the Nash equilibrium does exist and that it is one of a kind. The proposed energy harvesting approach was shown, through simulations, to significantly reduce the typical amount of power that was sent. This is taken into consideration to agree with the objective of 5G networks. In order to converge to Nash Equilibrium (NE), the method that is advised only needs roughly 4 iterations, which makes it easier to utilise in the real world, where things aren’t always the same.

Rapid changes in mobile internet have also made it difficult for mobile wireless networks to be designed well, especially when it comes to delivering very high data speeds with very little time delay. This is especially the case because of the rapid changes in mobile internet. According to a recent report by the International Telecommunication Union (ITU), the amount of data that is transmitted through cellular networks is expected to increase 10,000 times over the course of the next five years in comparison to 2010. The ultra-dense network (UDN) strategy ought to have the ability to accommodate the requirements of an increasing volume of data flow [

In recent years, networking and communication between UAVs have been quite popular due to the fact that they are highly speedy and may be employed in a variety of various ways. There is the potential for a great deal of success to result from combining the usage of UAVs and UDNs [

The IoT will be a significant contributor to the development of successful wireless networks and the subsequent generation of mobile communications [

This research project investigates how the Internet of Things (IoT) system allocates its resources and how unmanned aerial vehicles (UAVs) can assist in wirelessly powering the IoT. In order to investigate how resources are distributed among UAV nodes and wireless IoT devices, a non-cooperative game theory was utilised. To collect energy for Internet of Things nodes, drones are deployed. This is the solution that has been suggested. When wireless power transmission is utilised to power wireless nodes, drones are employed as a floating power source to power the drones themselves. The resources are going to be split between the drones and the wireless nodes. The method of game theory is applied to locate the Nash equilibrium and discover a solution to this problem. Unmanned Aerial Vehicles (UAVs) utilise Nash Equilibrium to determine the most efficient way to distribute their power sources, which enables them to transmit electricity without the usage of wires.

The most significant takeaways from the research are as follows:

IoT wireless power can be generated by UAVs; even just one UAV with a large number of wireless nodes is sufficient. Drones utilise wireless power transfer technology for the purpose of harvesting wireless nodes. The data transmission process is powered by the energy that is collected by wireless nodes.

A simulation of the process by which wireless local area network (WLAN) nodes and UAVs must share resources might be carried out with the help of a competitive game of power control. In the made-up game, the drone has perfect control over its source of energy transfer, and the wireless node has perfect control over its source of information transfer.

Ground sensors that have a lot of internet of things capabilities are being looked at as a solution to the problem of non-cooperative power control game theory.

The game of power control has reached a point of Nash equilibrium at this point. In addition, it has been demonstrated that the game that has been proposed and its Nash do in fact exist and are one of a kind.

An iterative power control strategy is a good fit for the unmanned aerial vehicle situation, as well as its Sigmoid and Spiral trajectories, which are presented in this study.

The findings of the simulation indicate that the method of the proposed algorithm is beneficial.

The rest parts of the text are organised as follows: In Section 2, we discuss the overall system model as well as the construction process for games. In order to demonstrate, in a mathematical sense, that the proposed equations in Section 3 are accurate, game theory was utilised. Information pertaining to the algorithm is presented in Section 4 of the aforementioned document. New concepts for the green transmission algorithm were conceived as a direct result of the simulation results and the throughput performance, which are covered in Section 5. The most important findings of the study and a conclusion are outlined in the 6^{th} section.

As a result of their mobility, unmanned aerial vehicles (UAVs) offer advantages that cannot be matched by conventional infrastructures on the ground, which are rooted in one location. The manner in which and the timing of any changes made to UDNs will be significantly influenced by UAVs. UAVs, sometimes famous as drones, are depicted functioning as mobile energy generators in

UDNs typically consist of sensor nodes, communication nodes that are used for device-to-device (D2D) and machine-to-machine (M2M) connections, and other wireless nodes that are dispersed throughout a large region. It is difficult to reduce energy consumption while keeping networks operational for an extended period of time due to the fact that batteries continue to be the primary source of power for many networks. It is possible to spend a significant amount of time and resources in order to regularly recharge or replace the battery packs in large nodes [

In this article, we take a look at the most significant aspects of UAV-based wireless communication, which is based on UAV-drones and IoT nodes that are located on the ground. The three-dimensional (3D) position of the unmanned aerial vehicle’s (UAV) trajectory design is shown as

We determine the route of the unmanned aerial vehicle (UAV) by taking into consideration its position

Most of the time, a UAV’s path is set by the following time intervals:

Maximum speed (_{max}

In this case, ^{T} stands for where the UAV starts in the horizontal plane.

Communication between aircraft and ground stations is different because the line of sight (LoS) is most probably to spread out in a three-dimensional space [

Here, at t

Here

They are written as

When a UAV is linked to its ground nodes, the path loss expression is:

In this case, the average extra losses for LoS and non-line of sight (NLoS) are given by

Los is thought to be the most important model for free-space path loss [

Actually,

The drone’s power is used to send data from a wireless node to a drone. Using the power source of a wireless contract, data can be sent, and generate revenue. All IoT nodes in a protected zone are in the same class and use the same channel to talk to each other [

Except for the

Cognitive IoTs make it illegal to cause more interference than is allowed by the interference temperature, interference temperature limit is expressed as:

In the non-cooperative game theory power allocation approach for spectrum sharing, the amount of power each node uses are lowered to meet the predefined requirements for SIR to find the target and to give communication systems a maximum amount of interference they can handle. Using the non-cooperative game theory, IoT nodes that are connected to the system can work in a Nash game in a way that maximises their own utility. There is no doubt that the Nash equilibrium exists and is the only one of its kind. Because of what we found, we’ve made an iterative power allocation method with low processing costs and shown that fast convergence is important in games with multiple nodes. Due to a predetermined SIR need for target identification and a maximum interference tolerance limit, this work can’t reach its main goal, which is to reduce each node’s power consumption and make the best use of transmission power. This is why game theory is seen as a good mathematical tool that considers the rational and self-interested actions of the players. Nodes often compete against each other to win a reward, and they choose a strategy based on how well they can send information.

The way players interact in the non-cooperative power allocation game can be thought of in the following ways:

The integers

In cognitive networks, information is sent from transmitters to receivers using

Furthermore, the user’s transmission power and pricing function have been considered to improve efficiency even more. Also included, as shown below, are a sigmoid efficiency function as a fraction of exponential power ratio power multiplied by tuning factor (z) and total power to target SIR:

Power control that doesn’t work with other nodes hurts them and costs them money. This makes the Nash equilibrium less effective than it should be. The pricing idea was put in place to get users to use as many network resources as possible. Here’s how a power-control game based on prices works when players don’t work together:

Therefore, the proposed pricing function is detailed as:

One of our ideas for improving system performance is to change the way prices work so that CRs are rewarded for making better use of system resources. This design participates by influencing the next taken a toll for clients who are most remote from the base station and utilize more power.

So, instead of the usual linear way of pricing transmission power, we use an exponential power function. Pricing functions for the power strategy [0, 1] have probably been completed numerically, with power transferred by different users covering a range from minimum to maximum. If the transmission power is low and the network is close, it’s easier to get lower prices.

If the transmission power is high and the network is far away, it’s harder. So, the pricing system for this paper can be summed up as follows:

In this case, the price factor is shown by the c and

In this part, a mathematical illustration related to the individuality and existence is given in the next section [

The Nash equilibrium in the suggested method gives a stable and predictable results where numerous CRs with opposing activities contribute and go to a position where no contributor can request to set its own method form. To emphasize NE’s being exist, the below theorem is suggested:

The profile action approach (i.e.,

The function of utility

As every cognitive uses a strategy outline well-defined by optimal and minimal power as given in

For proving that the second criterion is also satisfied, the given utility function based on variable pricing must be shown to be concave in

To show this condition is true, the Equations:

Considering that both the criteria are given in

The approach defines of the players consists of the recommender concave and continuous application function accordingly, NE is present in EE-NGPAP. However, a question can also rise at this juncture obviously: Is the existence of the NE unique? the distinctiveness of NE may be tested as follows:

Additionally, the best approach for reaction is a set consist of exactly a single maximum point that will increase the objective function, which is mathematically formulated as follows:

Likewise, the second derivative has been demonstrated to be bigger than zero, which means that the maximum point is the optimal unique point.

To prove the uniqueness of NE, the function for the most suitable response must be a regular function and be required to have the following traits as well [

Positivity:

Monotonicity: given

Scalability: given, for all

Furthermore, it’s been proven in [

Finally, a standard function should be utilized as the most excellent reaction function. Hence, the proposed non-cooperative has as it been an only one NE solution that fulfils the prove of the uniqueness of the Nash Equilibrium.

In order to locate the point at which the model proposed in this article is in a state of equilibrium, an iterative distributed technique of power allocation is applied. To locate the Nash Equilibrium, point for the model that has been provided, the suggested algorithm approach requires that each cognitive node decide on its own what the ideal transmission power SIR value is at each time step. It is believed that EE-NGPAP is the ideal protocol for this paradigm due to the fact that each IoT node just needs to know how to send to all of the other nodes. It is not necessary for it to have any knowledge of the system. A distributed process with pseudo-code can be used to describe the iterative power allocation and pricing method. This method assumes a unique Nash equilibrium with the proposed model.

Nevertheless, we assume that each cognitive node updates it’s transmit power at time instances

_________________________________________________________________________________________________________________________________________

I. Initialize vector of transmit power

II. Initialize UAV’s Trajectory

a) Sigmoid

b) Spiral

III. Inner Iteration:

For all

a) Update

b) Update

c) Given

d) Assign the transmit power as

IV. Convergence Step:

If

Else:

V. Exit Inner Iteration (Best Response (BR) Iteration)

VI. End

_________________________________________________________________________________________________________________________________________

Actually,

To set up the simulation environment, the MATLAB software was utilised. The suggested algorithm was put to the test in an area that was

Parameter | Value |
---|---|

Total players | 400 |

Trajectory | Sigmoid & spiral |

Whole bits number per frame, |
80 |

Overall number of information bits for every frame, |
64 |

Data rate, |
10 kbps |

AWGN power at receiver, |
1e–16 Watts |

Maximum power constraint, |
2 Watts |

Target SIR, |
9 |

Pricing factors, |
1e4, 2.5 |

Tuning factor, |
0.5–0.9 |

Altitude of UAV |
100 m |

Maximum flight speed |
50 m/s |

Channel power gain at reference distance |
–30 dB |

Reduction factor |
0.3820 |

Pathloss exponent |
3 |

Flying time | (200, 300 & 400) s |

This regulation cannot be broken in any way, shape, or form. The spiral trajectory is depicted in

It has been determined that a maximum flight speed of

The discount factor influences the best policies for wireless nodes when utilizing Nash equilibrium among players, which our approach represents as an IoT node, at a height of 100 metres and with multiple average flying times as

Hence, we assumed that all nodes belonging to the same category are the same so that consumers would have an easier time comprehending the simulations we created.

The EE-NGPAP algorithm which stands for non-cooperative game theory power allocation with pricing, was proposed as a method for controlling power in cognitive UAV networks with two trajectories (Sigmoid and Spiral) that contain IoT wireless nodes by the authors of this paper. In addition to this, a new energy harvesting function was proposed and simulations as well as mathematical arguments were utilised to demonstrate that the Nash equilibrium does in fact exist and is distinct from any other equilibrium. The simulations demonstrated that the method of non-cooperative power control was superior to other ways in terms of its ability to save energy. The recommended approach to energy collecting requires only 4 iterations to achieve NE, which makes it a more attractive option for potential enhancements to IoT technology in the near future. More research needs to be done on the concept of employing an unmanned aerial vehicle (UAV) as a moveable source of energy so that it can be put to use in the not-too-distant future. The application of machine learning in unmanned aerial vehicle (UAV) communications will be the subject of one of our next research projects.