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Strategic users in a service exchange application of crowdsensing are apt to exhibit malicious behaviors such as greed, free-ride, and attack, resulting in the phenomenon that no user is willing to serve others and low social utility is obtained in myopic equilibrium, which is considered as a service exchange contest dilemma. To address this issue, we propose a game-theoretic framework of multi-level two-sided rating protocol using all-pay contests to balance service request and service provision between users, in which a user is tagged with a multi-level rating to represent her social status, and she is encouraged to take the initiative to be a server and provide high-quality services to increase her rating. The two-sided rating update rule updates the ratings of both service requesters and service providers, and thus no one can always get services without providing services. By quantifying necessary and sufficient conditions for a sustainable multi-level two-sided rating protocol, we formulate the problem of selecting the optimal design parameters to maximize the social utility among all sustainable multi-level two-sided rating protocols, and design a low-complexity algorithm to select optimal design parameters via a two-stage procedure in an alternate manner. Finally, the extensive evaluation results demonstrate how intrinsic parameters impact on recommended strategies, design parameters, as well as the performance gain of the proposed rating protocol.

Immunization strategies on complex networks are effective methods to control the spreading dynamics on complex networks, which change the topology and connectivity of the underlying network, thereby affecting the dynamics process of propagation. Here, we use a non-Markovian threshold model to study the impact of immunization strategies on social contagions, in which the immune index greater than (or equal to) 0 corresponds to targeted (random) immunization, and when the immune index is less than 0, the probability of an individual being immunized is inversely related to the degree of the individual. A generalized edge-based compartmental theory is developed to analyze the dynamics of social contagions under immunization, and theoretical predictions are very consistent with simulation results. We find that increasing the immune index or increasing the immune ratio will reduce the final adoption size and increase the outbreak threshold, in other words, make the residual network after immunization not conducive to social contagions. Interestingly, enhancing the network heterogeneity is proved to help improve the immune efficiency of targeted immunization. Besides, the dependence of the outbreak threshold on the network heterogeneity is correlated with the immune ratio and immune index.

In the real Internet of Everything scenario, many large-scale information systems can be converted into interdependent sensor networks, such as smart grids, smart medical systems, and industrial Internet systems. These complex systems usually have multiple interdependent sensor networks. Small faults or failure behaviors between networks may cause serious cascading failure effects of the entire system. Therefore, in this paper, we will focus on the security of interdependent sensor networks. Firstly, by calculating the size of the largest functional component in the entire network, the impact of random attacks on the security of interdependent sensor networks is analyzed. Secondly, it compares and analyzes the impact of cascading failures between interdependent sensor networks under different switching edge strategies. Finally, the simulation results verify the effect of the security of the system under different strategies, and give a better exchange strategy to enhance the security of the system. In addition, the research work in this article can help design how to further optimize the topology of interdependent sensor networks by reducing the impact of cascading failures.

Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model.

With the rapid development of wireless mobile communication, local content sharing has become the emerging demand for users who are geographically close. Device-to-device (D2D) communication allows two users to communicate directly with each other. In order to achieve more effective content distribution and content spread by allocating better spectrum resources to users with better social networking and content diffusion capabilities, we propose an optimization scheme for resource allocation of the D2D communication by utilizing the potential social relations that are embedded in the communication devices. The degree of intimacy between users is abstracted from the call records to quantify social-relation strengths. Considering the time-varying property of social relations, the auto-regressive integrated moving average model is applied to map the call records into time sequence to predict users’ social relations. Besides, instead of individual utility or the overall network utility, each user aims to maximize its social-community utility which takes other social related D2D users into consideration. Potential game is utilized to solve the social-community utility maximization problem of resource allocation for the D2D communication due to its outstanding mapping nature and always has the Nash equilibrium. Finally, a social-aware distributed resource allocation algorithm is proposed, and the algorithm achieves convergence and stability. Numerical results show that our proposed scheme increases the overall utility over 30% compared with coalition game scheme, and over 50% compared with random selection scheme without loss of the fairness.

Recently, the increasing popularity of local area services, such as YouTube, Facebook, and Twitter, on which thousands of clients subscribe and download popular contents all the while, has drawn more and more attention on the study of content dissemination networks. Device-to-device (D2D) communication, which allows two devices to directly communicate with each other, has become an effective content dissemination method. With the goal of reducing the delay of content dissemination process with D2D communication, we propose an evolutionary game (EG)-based distributed resource allocation scheme. Moreover, we theoretically prove the existence and stability of game equilibrium and further propose a global search algorithm to achieve equilibrium. In the algorithm, all D2D links will select resource adaptively based on the predicted contact duration. Aiming at improving the prediction accuracy of contact duration, we propose social trajectory similarity (STS) to represent the overlapping of history trajectory among all the mobile users by mining user behavior patterns. Numerical results show that our proposed STS increases nearly 20% correlation with history trajectory overlap compared with jaccard index. Furthermore, our EG-based scheme reduces the delay over 15% compared with the coalition game scheme, and over 30% compared with the random selection scheme. In addition, our proposed scheme also increases throughput efficiency with nearly 20% and achieves better fairness.

With the exponential growth of traffic demand, ultra-dense networks are proposed to increase the network capacity. However, the high-density access point (AP) deployment will increase the complexity of AP coordination, and AP cluster (APC) needs to be considered in practical implementations. Due to the dynamic changes in spatiotemporal distribution of users and service demand, we propose a social-energy-based cluster management (SECM) scheme in order to reduce APC update frequency. Specifically, in social domain, we propose a congeniality-based personalized recommendation (CPR) algorithm to predict users' incoming requests. We further propose a CPR-based AP cluster algorithm to solve the matching problem among users, APs, and content. In energy domain, we propose an inter-cluster energy cooperation scheme to avoid the shutting down of members in AP cluster and reduce the update of clusters. Numerical results demonstrate that our proposed scheme can achieve a gain of 77.8% in the APC management utility averagely, without loss of fairness compared with the other state-of-the-art schemes.