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.

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost. Request access from your librarian to read this article's full text.

With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two‐stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two‐stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.