With the development of the Internet of Things (IoT) technology, many end-users participate in the smart city through their own intelligent mobile devices (such as personal wearable devices, smartphones.) or sensors. The main challenge of the device sensing layer in the edge computing system of the IoT in the smart city is to select the trusted participants. Because not all the intelligent devices of the IoT are trustworthy, some intelligent devices of the IoT may maliciously damage the network or services and affect the service quality of the system. On this basis, an intelligent device selective recommendation mechanism based on the dynamic black-and-white list was proposed to solve the problem of selecting trusted participants to improve the service quality of the edge computing system of the IoT in the smart city. We introduced the evolutionary game theory to theoretically qualitatively study the validity and stability of the trust management mechanism proposed in this paper. The Lyapunov theory was used to prove the validity and stability of the trust management mechanism. The effectiveness of the trust management mechanism was verified by the actual scenario of the personal health monitoring management system and the air-quality monitoring and analysis system in the smart city environment. Experiments showed that the trust management mechanism proposed in this paper has a significant role in promoting the cooperation of multi intelligent devices in the IoT edge computing system. It more reliably resists the malicious attacks to service providers and is suitable for the large-scale IoT edge computing system in the smart city.

Learning trajectory representations is essential in many Location Based Services (LBS) applications. Most traditional methods extract trajectory representations based on manually defined features, while deep learning-based methods can reduce part of the human effort. We propose a Deep Spatiotemporal Trajectory Clustering (DSTC) framework to tackle the Spatiotemporal Trajectory Representation Learning towards the Clustering-friendly space (STRLC) problem. Solving the STRLC problem is not a trivial task because: (1) Defining a uniform token size for datasets with an uneven density of trajectory data is challenging. (2) Measuring the similarity between trajectories spanning time zero in the time dimension is a problem to be solved. (3) It requires first learning a vector that can represent the overall characteristics of spatiotemporal trajectories and then mapping it to a more suitable space for clustering. To tackle these challenges, we first utilize the density-based clustering method to define tokens representing the trajectory points automatically. Then, we use polar coordinates to represent the temporal dimension of trajectories. Additionally, we improve the learned trajectory representations in a clustering-oriented latent space end to end. Experiments conducted on benchmark datasets demonstrate that DSTC achieves better accuracy than existing methods. Moreover, the representations learned from spatiotemporal trajectory data in the real world can be used to identify popular routes during the day.

In an autonomous system, rational agents aim to maximize their benefits. Rational agents gradually give up cooperation with others and only request services, which leads to the phenomenon of free-riding. This phenomenon generally exists in the real society and application system. Therefore, how to reduce the behavior, which is similar to free-riding, has become an important research topic. In addition, the traditional pairwise interaction network cannot truly reflect the interaction properties of cooperative events. Social norms provide an effective way to promote cooperation for autonomous systems. Axelrod's metanorm game model achieves the emergence of social norms by conducting punishment to connivance based on punishing defection. However, links in networks only allow for pairwise interactions. Therefore, we introduce hypergraphs, which are more suitable to reflect group interaction to model metanorm games. The establishment efforts of norms are examined on hypergraphs (uniform random hypergraph (URH), hyperdegree-heterogeneous random hypergraphs, and real-world hypergraphs). We show that the difference in group sizes affects norms emergence of agents and realize the establishment of social norms on URH. To a certain extent, the probability of being seen is positively correlated with agents' vengefulness and learning time and negatively correlated with boldness. However, the usage of the boldness and vengefulness learning (BV-learning) algorithm on HRHs cannot make norms emergence because leavers cannot participate in learning and the fixed learning step. Therefore, we propose the dynamic relevance BV-learning algorithm to overcome the aforementioned problems, so hub agents and leavers can jointly establish better social norms. We also verify the evolution process of agents' cooperation rate by BV-learning and the dynamic relevance BV-learning, respectively, which demonstrate that the establishment of social norms can indeed promote agents' cooperation. Finally, in order to illustrate the universality of the dynamic relevance BV-learning clearly, we study norms establishment on real-world hypergraphs by and compare it with BV-learning. It can be found that the dynamic relevance BV-learning can effectively establish social norms on real-world hypergraphs.

How to explain the emergence of cooperative behavior remains a significant problem. As players may hold diverse perceptions on a particular dilemma, the concept of multigames has been introduced. Therefore, a multigame is studied within various binary networks. Since group structures are common in human society and a person can participate in multiple groups, this paper studies an evolutionary multigame with high-order interaction properties. For this purpose, a uniform random hypergraph is adopted as the network structure, allowing players to interact with all nodes in the same hyperedge. First, we investigate the effect of the multigame payoff matrix differences on the evolution of cooperation and find that increasing the differences in the payoff matrix promotes cooperation on the hypergraph network. Second, we discover that an increase in the average hyperdegree of the hypergraph network promotes network reciprocity, wherein high-hyperdegree nodes influence surrounding nodes to form a cooperator cluster. Conversely, groups with a low hyperdegree are more susceptible to betrayal, leading to a decline in cooperation.