A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

Cooperation-Driven Virtual Terminal Coalition Formation Games for Task Assignment in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a popular way of data collection, which forms the large-scale sensing system by smart mobile terminal users and provides multimodal sensor data. In the sensing scenario, there are various sense resource requirements of tasks released by the platform. One of the most urgent issues in MCS is how to choose corresponding users with appropriate sense resources to accomplish assigned tasks. In this article, cooperating among a host of users to perform sense tasks is considered. Firstly, the cooperation among users to accomplish the sense tasks is described as an overlapping coalition formation game (OCF game). In addition, an initial coalition method of using social networks (SN) is proposed to accelerate the formation of coalition. Finally, the cooperation degree is used to describe the cooperative relationships among users, and virtual terminal coalition formation (VTCF) algorithm is proposed to simplify the process of coalition formation. The simulated results show that the proposed approach effectively improves the system’s utility under the constraints of task cost and sense quality.

A network formation game for the emergence of hierarchies

We propose a novel network formation game that explains the emergence of various hierarchical structures in groups where self-interested or utility-maximizing individuals decide to establish or severe relationships of authority or collaboration among themselves. We consider two settings: we first consider individuals who do not seek the other party’s consent when establishing a relationship and then individuals who do. For both settings, we formally relate the emerged hierarchical structures with the novel inclusion of well-motivated hierarchy promoting terms in the individuals’ utility functions. We first analyze the game via a static analysis and characterize all the hierarchical structures that can be formed as its solutions. We then consider the game played dynamically under stochastic interactions among individuals implementing better-response dynamics and analyze the nature of the converged networks.

Human-agent coordination in a group formation game

Coordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents’ decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.