A Task-driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

Question

1. What challenges exist in UAV task execution?

2. How can the performance of task allocation results be comprehensively evaluated considering benefits and costs of task execution?

3. What is the novel sequential OCF game based on?

4. What are the two categories of sub-task types?

Accepted Answer

Challenges in UAV task execution include detailed task resource allocation, task execution schedule, and the cost of UAV coalitions to carry out tasks. Recent advances in UAV technology allow a single UAV to carry various types and quantities of resources, requiring more detailed differentiation of resource attributes for different tasks. The allocation of resources to multiple tasks and overlapping coalitions based on task schedules is a new approach to address these challenges. This paper investigates the problem of cooperative task execution in a heterogeneous multi-UAV network by forming an overlapping resource allocation coalition structure.

Accepted Answer

The performance of task allocation results can be comprehensively evaluated by considering the benefits and costs of task execution. This includes factors such as task completion time, energy consumption, and the overall efficiency of UAVs in completing tasks. By taking into account these factors, a more accurate assessment of the performance of task allocation can be achieved, leading to improved task execution and resource utilization in UAV-enabled systems.

Accepted Answer

The novel sequential OCF game is based on the sequentiality of task execution in heterogeneous and cooperative multi-UAV networks. It enables UAVs to make more flexible resource allocation decisions, improving task execution performance. This game model defines the coalition structure as the resource allocation of each UAV, leading to a large decision-making space and unguaranteed performance of frequently-used algorithms. The proposed game considers the preference relation between UAVs carrying resources and tasks required the same type of resource, aiming to improve the utility of task execution.

Accepted Answer

The two categories of sub-task types are those requiring consumable resources and those requiring non-consumable resources. Consumable resource-based sub-tasks include fire rescue and wireless charging, while non-consumable resource-based sub-tasks include information collection and video surveillance. These categories help in understanding the resource requirements for different types of tasks.

Accepted Answer

The overlapping coalition structure in UAV resource allocation allows UAVs to allocate resources to multiple task coalitions. This structure enables UAVs to contribute to the formation of coalitions by assigning resources to different tasks. Each UAV resource allocation vector, represented as A(n)m, indicates the amount of resources allocated to task m by UAV n. The z b -th type consumable resource and z c -th type non-consumable resource are also considered in the allocation. The coalition member set of UAVs allocated to task m is represented by Mem(A m ). Overall, the overlapping coalition structure enhances cooperative task performance by optimizing resource allocation among UAVs.

Accepted Answer

OCF game theory analyzes UAV cooperative task execution by considering the cooperative relationship among users and forming coalitions. Players allocate resources to different coalitions, contributing to an overlapping coalition structure. The optimization objective is to maximize the entire network utility of task execution through optimal resource allocation. The energy consumption, UAV movement, and resources consumption constraints are taken into account. Due to the combinatorial nature of the coalition formation problem, obtaining the optimal solution is NP-hard, leading to the design of a low-complexity OCF algorithm for a near-optimal solution.

Accepted Answer

The OCF game model enables overlapping coalitions by allowing a UAV to allocate its resources to multiple task coalitions. This is different from existing disjoined coalition game models. In the proposed model, each UAV obtains a share of the utility from the coalitions. The utility of UAV n can be expressed using the Shapley value, which divides the utility among UAV members based on the proportional fairness. This encourages UAVs to allocate more resources to coalitions that bring them a large utility. The model also defines a preference relation for UAVs, where a coalition structure SC Q is considered superior to SC P if UAV n prefers to allocate its resources to SC Q rather than SC P. This preference relation helps in determining the allocation of task resources among UAVs in the overlapping coalition structure.

Accepted Answer

The exchange operation in coalition structures involves leaving or joining a coalition A(p)i that leads to a new coalition structure SCQ = SCP \ A(p)i A(q)i, where A(q)i = A(p)i \ u(zc)n or A(p)i u(zc)n. This operation must satisfy the preference order, ensuring that the new coalition structure SCQ is preferred over the original coalition structure SCP. Different preference orders can result in different coalition convergence structures, influencing the entire network utility. Two major orders in the OCF game model are the Pareto order and the selfish order, which are commonly used preference relations in the model.

Accepted Answer

The proposed Bilateral Mutual Benefit Transfer (BMBT) order aims to enhance cooperation among UAVs within the same coalition. It encourages UAVs to exchange resources based on the differences in utility between original and new coalitions. By prioritizing cooperation within the same coalition, the BMBT order facilitates resource allocation changes and increases the sum of utility for the involved coalitions. This approach allows for more opportunities to optimize resource distribution and improve overall efficiency in UAV coalition structures.

Accepted Answer

The concept of o-core refers to a stable coalition structure where no UAV can make a profitable exchange operation unilaterally to improve its utility under the preference order. It is called o-stable when no UAV can make such an exchange. This concept is introduced in the OCF game model and is based on the notion of core proposed in reference [33]. The definition of stable coalition structure solution is given by SC Q n SC P = u n (SC Q ) + gMem(Aj )\{n} [u g (SC Q ) - u g (SC P )]. Theorem 1 states that in the overlapping coalition formation game, there is at least one stable coalition structure under the Pareto and selfish orders. Theorem 2 further states that there is at least one stable coalition structure under the proposed BMBT order. The stability of the coalition structure under the BMBT order is deduced based on the property of Nash equilibrium solution in exact potential game [35] and [36].

Accepted Answer

The three main stages of the OCF algorithm are task information collection, distributive coalition formation, and sequential task execution. In the task information collection stage, UAVs patrol the task area and collect task execution information such as resources required and location. In the distributive coalition formation stage, UAVs perform exchange operations based on the proposed PGG-TS algorithm, adjusting the coalition structure until convergence. In the sequential task execution stage, UAVs generate a task execution sequence based on the stable coalition structure and perform tasks sequentially, adjusting hover positions for better communication quality and forming new coalitions for new tasks.

Accepted Answer

The time complexity of coalition formation is influenced by several factors. Firstly, the complexity of each step is calculated for the worst-case scenario, as shown in Table 3. The complexity of the algorithm can be expressed as the sum of the complexities of Step 2, Step 3, and Step 4. Step 2 involves tabu list operations, Step 3 involves coalition structure exchange operations, and Step 4 involves resource allocation result notification and final coalition formation. The complexity of each step is denoted by O(C1), O(C2Ltabu), O(C3NM), and O(C4) respectively. Additionally, the size of the coalition formed by UAVs affects the complexity. Larger coalitions result in fewer exchange operations before reaching a stable structure, reducing the complexity of exchange operations. Therefore, the time complexity of coalition formation is a combination of these factors, making it a complex analysis.

Accepted Answer

The flight and hover power consumption parameters of UAVs are set according to the study [40]. This ensures that the simulation accurately reflects the energy consumption of UAVs during reconnaissance and monitoring tasks. The parameters are crucial for evaluating the efficiency and effectiveness of the proposed PGG-TS algorithm in real-world scenarios. By using these parameters, researchers can simulate the behavior of UAVs in a controlled environment, allowing them to analyze the impact of different resource allocation strategies on task execution and energy consumption. The study [40] provides a comprehensive set of parameters that can be used to replicate the conditions of UAV operations, making it a valuable reference for researchers conducting simulations in the field of UAV-based reconnaissance and monitoring tasks.

Accepted Answer

The proposed PGG-TS algorithm demonstrates advantages over other algorithms in terms of performance evaluation. It outperforms the splitmerge preferred OCF algorithm and non-overlapping CF algorithm in terms of average utility, average number of iterations, and convergence speed. The proposed algorithm achieves a close-to-optimal solution through exhaustive search and guides UAVs to avoid falling into a local optimum overlapping coalition structure. Additionally, the algorithm shows improved efficiency and effectiveness in reducing the number of iterations with approximately optimal solutions. The average utility of tasks increases by 11.9% and 42.8% compared to the splitmerge preferred OCF algorithm and non-overlapping CF algorithm, respectively. The average number of iterations decreases by 26.1% compared to the split-merge preferred algorithm and 25.1% compared to the non-overlapping CF algorithm. The convergence speed and utility under the proposed PGG-TS algorithm outperform the split-merge preferred OCF algorithm. The average utility of tasks increases by 10.1% compared to the split-merge preferred algorithm. The proposed algorithm also improves energy efficiency, with increases of 13.3% and 26.3% compared to the split-merge preferred OCF algorithm and non-overlapping CF algorithm, respectively. The average completion degree of tasks increases by 10.1% compared to the split-merge preferred algorithm. The proposed BMBT order achieves a better utility than the Pareto and selfish orders. Overall, the proposed PGG-TS algorithm demonstrates superior performance in terms of utility, iterations, convergence speed, and energy efficiency compared to other algorithms.

Accepted Answer

The proposed sequential overlapping coalition formation (OCF) game allows each UAV to decide the amount of resources allocated to each task while optimizing the tradeoff between the benefits and costs of task execution. By adopting this game, UAVs can efficiently allocate resources to tasks, ensuring that the benefits of task execution are maximized while minimizing costs. This optimization process helps in achieving a balanced and effective resource allocation strategy in task-driven heterogeneous UAV networks.

A Task-driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

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Most frequently asked questions

1. What challenges exist in UAV task execution?

2. How can the performance of task allocation results be comprehensively evaluated considering benefits and costs of task execution?

3. What is the novel sequential OCF game based on?

4. What are the two categories of sub-task types?

5. What is the role of overlapping coalition structure in UAV resource allocation?

6. How does OCF game theory analyze UAV cooperative task execution?

7. How does the OCF game model enable overlapping coalitions?

8. What is the exchange operation in coalition structures?

9. What is the purpose of the proposed BMBT order in UAV coalition structures?

10. What is the concept of o-core in stable coalition structure?

11. What are the three main stages of the OCF algorithm?

12. What factors affect the time complexity of coalition formation?

13. How are UAVs' flight and hover power consumption parameters set?

14. How does the proposed PGG-TS algorithm compare to other algorithms in terms of performance evaluation?

15. How does the proposed sequential overlapping coalition formation (OCF) game optimize resource allocation?

Citations

Distributed Active Disturbance Rejection Formation Tracking Control for Quadrotor UAVs.

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Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs

Feature Matching Data Synthesis for Non-IID Federated Learning

References

6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

Energy Minimization for Wireless Communication With Rotary-Wing UAV

6G Wireless Communications: Vision and Potential Techniques

Opportunistic Spectrum Access in Cognitive Radio Networks: Global Optimization Using Local Interaction Games

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

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