You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake. To achieve efficient and rapid networks deployment, we employ noncooperative game theory and amended binary log-linear algorithm (BLLA) seeking for the Nash equilibrium which achieves the optimal network performance. We not only take channel overlap and power control into account but also consider coverage and the complexity of interference. However, extensive UAV game theoretical models show limitations in post-disaster scenarios which require large-scale UAV network deployments. Besides, the highly dynamic post-disaster scenarios cause strategies updating constraint and strategy-deciding error on UAV ad-hoc networks. To handle these problems, we employ aggregative game which could capture and cover those characteristics. Moreover, we propose a novel synchronous payoff-based binary log-linear learning algorithm (SPBLLA) to lessen information exchange and reduce time consumption. Ultimately, the experiments indicate that, under the same strategy-deciding error rate, SPBLLA's learning rate is manifestly faster than that of the revised BLLA. Hence, the new model and algorithm are more suitable and promising for large-scale highly dynamic scenarios.

Cited Abstract(s):
[1]: Advances in wireless communications and microelectronics have spearheaded the development of unmanned aerial vehicles (UAVs), which can be used to augment a ground network composed of sensors and or vehicles in order to increase coverage, enhance the end-to-end delay, and improve data processing. While UAV-aided networks can potentially find applications in many areas, a number of issues, particularly security, have not been readily addressed. The intrusion detection system is the most commonly used technique to detect attackers. In this paper, we focus on addressing two main issues within the context of intrusion detection and attacker ejection in UAV-aided networks, namely, activation of the intrusion monitoring process and attacker ejection . In fact, when a large number of nodes activate their monitoring processes, the incurred overhead can be substantial and, as a consequence, degrades the network performance. Therefore, a tradeoff between the intrusion detection rate and overhead is considered in this work. It is not always the best strategy to eject a node immediately when it exhibits a bad sign of malicious activities since this sign could be provisional (the node may switch to a normal behavior in the future) or be simply due to noise or unreliable communications. Thus, a dilemma between detection and false positive rates is taken into account in this paper. We propose to address these two security issues by a Bayesian game model in order to accurately detect attacks (i.e., high detection and low false positive rates) with a low overhead. Simulation results have demonstrated that our proposed security game framework does achieve reliable detection.

[2]: One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

[3]: We study aggregative games in which players’ strategy sets are convex intervals of the real line and (not necessarily differentiable) payoffs depend only on a player’s own strategy and the sum of all players’ strategies. We give sufficient conditions on each player’s payoff function to ensure the existence of a unique Nash equilibrium in pure strategies, emphasizing the geometric nature of these conditions. These conditions are almost best possible in the sense that the requirements on one player can be slightly weakened, but any further weakening may lead to multiple equilibria. The same conditions also permit the analysis of comparative statics and the competitive limit. We discuss the application of these conditions in a range of examples, chosen to illustrate various aspects their use. We also show that all restrictions on payoffs in aggregative games that guarantee the existence of a unique equilibrium of which we are aware are covered by these conditions. When payoffs are sufficiently smooth, these conditions can be tested using derivatives of the marginal payoff and we illustrate these tests in the applications introduced earlier. We also investigate conditions under which the unique equilibrium is locally stable. These hold in particular in a symmetric game under the same conditions required to ensure the existence of a unique equilibrium.

[4]: Unmanned aerial vehicles (UAVs) were initially developed for military monitoring and surveillance tasks but found several interesting applications in the civilian domain. A promising application technology is to use drone small cells (DSCs) to expand wireless communication coverage on demand. Rapid deployment along with limited operating costs are key factors that boost the development of DSCs for both military and civilian utilizations. DSCs are rapidly deployable to provide connectivity for temporary users (e.g. attendees of festivals, sporting events, or seminars), or over disaster areas to replace damaged communication infrastructure. UAVs are battery-powered, which makes energy consumption optimization a critical issue for acceptable performance, high availability, and an economically viable DCS deployment. In this article we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network with its unique Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy efficiency of the strategic beaconing policy (at Nash equilibrium).

[5]: Thanks to the rapid development of information technology, the size of a wireless network is becoming larger and larger, which makes spectrum resources more precious than ever before. To improve the efficiency of spectrum utilization, game theory has been applied to study efficient spectrum sharing for a long time. However, the scale of wireless networks in existing studies is relatively small. In this paper, we introduce a novel game called aggregative game and use it to model spectrum sharing in large-scale, heterogeneous, and dynamic networks. Meanwhile, the massive usage of the spectrum leads to easier divulgence of privacy of spectrum users, which calls for privacy and truthfulness guarantees. In a large decentralized scenario, each user has no priori about other users’ channel access decisions, which forms an incomplete information game. A “weak mediator,” e.g., the base station or licensed spectrum regulator, is introduced and it turns the incomplete spectrum sharing game into a complete one. This is essential in reaching a Nash equilibrium (NE). By utilizing past channel access experience, we propose an online learning algorithm to improve the utility of each user. We show that the learning algorithm achieves an NE over time and provides no regret guarantee for each user. Specifically, our mechanism admits an approximate ex-post NE, and is joint differentially private and incentive-compatible. Efficiency of the approximate NE is evaluated, and innovative scaling law results are disclosed. We also provide simulation results to verify our analysis.

[6]: Due to the limitations of mobile devices in terms of processing power and battery lifetime, cloud based solutions offer an attractive approach to answer these shortcomings. Since offloading intensive computation tasks to an edge cloud server would achieve impressive performances, computation offloading paradigm has attracted the focus of many research groups in the last few years. This paper considers the problem of computation offloading while achieving a tradeoff between execution time and energy consumption. The proposed solution is intended for a fleet of small drones that are required to achieve highly intensive computation tasks. Drones need to detect, identify and classify objects or situations. Thus, they are brought to deal with intensive tasks such as pattern recognition and video preprocessing. The latter implement very complex calculations and typically require dedicated and powerful processors, which would definitely accentuate the dilemma between energy and delay. We adopted a game theory model where the players are all the drones in the network with three possible strategies. We defined the cost function to be minimized as a combination of energy overhead and delay. The simulation results are very promising and the achieved performances outperformed their counterparts in terms of average system wide cost and scalability.

[7]: This paper introduces quasi-aggregative games and establishes conditions under which such games admit a best-reply potential. This implies existence of a pure strategy Nash equilibrium without any convexity or quasi-concavity assumptions. It also implies convergence of best-reply dynamics under some additional assumptions. Most of the existing literature’s aggregation concepts are special cases of quasi-aggregative games, and many new situations are allowed for. An example is payoff functions that depend on own strategies as well as a linear combination of the mean and the variance of players’ strategies.

[8]: Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically seek the spectrum resource from primary users (PUs) in a competitive way. In most existing works, however, CR users are required to share the interference channel information and power strategies to conduct the game with pricing mechanisms that incur the frequent exchange of information. The requirement of significant communication overheads among CR users impedes fully distributed solutions for the deployment of CR networks, which is a challenging problem in the research communities. In this paper, a robust distributed power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning, which does not require the interference channel and power strategy information among CR users (and from CR users to PUs). To the best of our knowledge, this research provides the solution for the first time for the incomplete-information power control game in CR networks. During the repeated game, CR users can control their power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step. This procedure allows achieving high spectrum efficiency while conforming to the interference constraint of PUs. This constrained repeated stochastic game with learning automaton is proved to be asymptotically equivalence to the traditional game with complete information. The properties of existence, diagonal concavity and uniqueness for the game are studied. A Bush-Mosteller reinforcement learning procedure is designed for the power control algorithm, and the properties of convergence and learning rate of the algorithm are analyzed. The performance of the learning-based power control algorithm is thoroughly investigated with simulation results, which demonstrates the effectiveness of the proposed algorithm in solving variety of practical CR network problems for real-world applications.
Game theory provides an efficient tool for the cooperation through resource allocation and sharing [8] [5] . A computation offloading game has been designed in order to balance the UAV's tradeoff between execution time and energy consumption [6] . A sub-modular game is adopted in the scheduling of beaconing periods for the purpose of less energy consumption [4] . applied the Bayesian game-theoretic methodology in UAV's intrusion detection and attacker ejection [1] . However, most existing models focus on common scenarios with less number of UAVs, which are not compatible with large-scale scenarios with large numbers of UAVs [2] . Aggregative game is a characteristic game model which treats other agents' strategies as a whole influence, thus avoids overwhelming strategies information from every single agent [7] [3] . Inspired by this, our model is built upon the aggregative game theory which suits for large-scale scenarios.