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A fair and reasonable price mechanism is fundamental in improving the efficiency of the crowdsensing market. Being an emerging paradigm that utilizes widely distributed wireless communication network and smart sensing devices to collect sensing data, crowdsensing service offers a plausibility for collecting real-time data. However, previous studies ignored the complex competition among participants in the free data trade market and the fresh data demands of increasingly popular real-time applications. This paper introduces the age of information (AoI) as a criterion to measure the freshness of data geared towards constructing incentive models for the data provider and data requester. Subsequently, considering the opening of the market, we design a price mechanism framework based on non-cooperative games, which can help the crowdsensing platform provide each participant a stable sensing strategy to maximize profit. For the AoI-sensitive case, i.e., when the data quality deteriorates with the time. A co-evolutionary algorithm based on heuristic search is designed to solve the sensing strategy for each provider to achieve Nash equilibrium. Furthermore, for the case where AoI is insensitive. An improved relaxation algorithm is proposed to provide a stable strategy for the data provider. Numerical findings reveal that the developed algorithm can effectively identify stable sensing strategies in a Nash equilibrium. [ABSTRACT FROM AUTHOR]

It is critical to maintain significant convergence and diversity in many-objective optimization problems (MaOPs) for the performance of many-objective evolutionary algorithms (MaOEAs). However, some issues pose serious challenges to MaOEAs, such as the intensification of the conflict between convergence and diversity, and the lack of Pareto selection pressure. To address the above issues, we propose a cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithms (MaOEA-GM). The algorithm is divided into two stages, such as competition and cooperative. In competitive game stage, a strategy pool is constructed, including angle penalty distance strategy and favorable convergence strategy. In addition, a new game utility function is designed to balance convergence and diversity. This method promotes the selection of genetically superior parents for inheritance, so that the population can quickly approach the true Pareto front. In cooperative game stage, individuals choose their preferred environmental selection mechanism by voting. The final scheme is determined using the criterion of minority obedience to the majority, thereby increasing the algorithm Pareto selection pressure. Experimental results demonstrate that compared with five advanced MaOEAs, The MaOEA-GM algorithm has not only preponderance in convergence and diversity indicators, but also higher competitiveness in solving MaOPs. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of suppression of the integrated air defense system (IADS) by multiple fighters' cooperation. Considering the dynamic changing of the number of the nodes in the operational process, a profit model for the influence of the mission's cost for the whole system is developed for both offense and defensive sides. The scenario analysis is given for the process of suppressing the IADS by multiple fighters. Based on this scenario analysis, the modeling method and the specific expression for the payoff function are proposed in four cases for each node. Moreover, a distributed virtual learning algorithm is designed for the n-person and n-strategy game, and the mixed strategy Nash equilibrium (MSNE) of this game can be solved from the n×m×3-dimensional profit space. Finally, the simulation examples are provided to demonstrate the effectiveness of the proposed model and the game algorithm. [ABSTRACT FROM AUTHOR]

The traditional centralized power allocation method for cognitive tracking in the netted radar can achieve the best resource utilization efficiency and target tracking performance. However, this method has poor robustness, low fault tolerance, and a high bandwidth requirement. In order to address this challenge, we propose a decentralized distributed power allocation method based on the non-cooperative game for multi-target tracking. Firstly, we model the power allocation problem for decentralized netted radar as a non-cooperative game problem and consider each radar node in the radar network as a player in the game. Then, we employ the determinant of Fisher information matrix to evaluate target tracking accuracy and use it to construct the utility function of the non-cooperative game based on the game rule. Finally, we propose an iterative distributed computing method to solve the Nash equilibrium. Meanwhile, we theoretically prove the Nash equilibrium solution's existence and uniqueness. Numerical simulation results show that the tracking performance of the proposed method is superior to the uniform and random power allocation method. Simultaneously, the tracking performance of the proposed method is close to the centralized power allocation method with a much lower computational load. [ABSTRACT FROM AUTHOR]