# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 3 Problem Formulation
### Computations
Notably, strategy set \([0,\,T]\) forms a convex, closed, and compact interval. Furthermore, the utility function remains continuous concerning \(\tau_{i}\). Additionally, we establish that the second derivative with respect to \(\tau_{i}\) is negative. Following the calculations, the second derivative can be expressed as follows:  
\[u_{i}(\tau_{i},\tau_{j})=(P(T_{i}\leq T_{j})+P(T_{i}\geq T_{j})P_{j}^{slp})P_{i }^{bcn}(T-\tau_{i}) \tag{3}\]3  
a random flying pattern strategy. The simulations were conducted in a three-dimensional space with varying beaconing periods and encountering rates. As in [38], we assumed that the effect of path losses were negligible, and we did not model the channel. We ignored the effect of the path losses and errors from the channel. Those have been well studied in the context of UAV beaconing for coverage in [4; 28]. The simulation tool was \"Python\". In the same line as paper [38], we used a similar range of simulation parameters, namely, encounters rates and beaconing durations, and these were set based on typical values, such as \(T=1\), \(m=100\). The parameters for the learning algorithms are usually well understood in the literature of learning automata. The simulation results showed that the proposed flying access strategy outperformed the existing strategies in terms of network coverage, energy efficiency, and interference mitigation. In the experiments, a UAV was flown in an open field with varying obstacles to simulate a real-world environment. The UAV was equipped with a wireless communication module, and network performance was measured using metrics such as signal strength, packet loss rate, and network throughput. The proposed flying access strategy was compared with the fixed flying pattern strategy based on varying beaconing periods and encountering rates. The results showed that the proposed strategy provided better network coverage and energy efficiency for a wide range of beaconing periods and encountering rates. To validate the effectiveness of the proposed flying access strategy based on the beaconing period and encountering rate using game theory, a comparative analysis was conducted using metrics such as network coverage, energy consumption, and interference mitigation. The results showed that the proposed strategy outperformed the existing strategies in terms of network coverage and energy efficiency for a wide range of beaconing periods and encountering rates. The experiments also demonstrated the importance of optimizing UAV movements, communication parameters, and game theory strategies to provide efficient network coverage while conserving energy and mitigating interference. The experiments showed that the proposed flying access strategy based on the beaconing period and encountering rate using game theory could be used in various real-world scenarios, such as disaster management, surveillance, and agricultural monitoring. In conclusion, the numerical implementation and experiments based on the beaconing period and encountering rate using game theory validated the effectiveness of the proposed flying access strategy for UAV-based wireless networks in IoT environments. The simulations showed that the proposed strategy outperformed the existing strategies in terms of network coverage, energy efficiency, and interference mitigation for a wide range of beaconing periods and encountering rates. The proposed strategy provides insights into the development of future UAV-based wireless network systems and has practical implications for wireless network providers, IoT service providers, and UAV manufacturers.