Encrypted communications, implemented for the confidential information exchange, facilitate the preservation of individual privacy. Unfortunately, some criminals abuse encrypted communications to conduct illegal activities, leading to the proliferation of the Darknet. To curb malicious darknet activities, the accurate and effective classification of darknet traffic is imperative. Considerable endeavors have been devoted to identifying the darknet traffic. However, the classification of darknet applications has not yielded a satisfactory result. This deficiency arises from the limitations of current approaches, e.g., some traditional methods rely on hand-crafted features that consume labor, and other neural network-based methods disregard the graph structure of the traffic. To tackle these challenges, we propose the Darknet Traffic Graph (DTG), a graph structure that captures the interactions between local clients and remote servers in darknet traffic. Furthermore, based on DTG, we combine the GNN model and attention mechanism to create the Darknet Graph Neural Networks, i.e., DGNN, a powerful model that sufficiently exploits the benign and darknet traffic features. As a result, on the CIC-Darknet2020 dataset, the accuracy of DGNN in traffic classification and application classification is 98.52% and 99.06%, respectively, which outperforms other classifiers.

Exploiting mobile crowdsensing to conduct data collection and analysis has become an emerging trend in IoT computing models. However, in practice, mobile users in crowdsensing networks are unwilling to participate in perceptual tasks without effective stimulation due to limited resources and strategic selfishness. To tackle the problem, a new incentive mechanism is proposed by adopting game theory in this paper. Specifically, the mobile users' preference is considered comprehensively and their energy is changed dynamically through the scheme. Furthermore, the users' preference and energy changes are exploited as equation factors between subsequent users and the platform. To motivate users, we introduce the concept of virtual currency and set a price function that rewards users for participating in sensing tasks in the form of virtual currency. To fit into practical scenarios, we map task interactions to a Rubinstein-Starr bargaining game between a user and sensing platforms based on a fully informative turn-based bargaining model. Through multiple rounds of games between users and crowdsensing platforms, we prove that there is a unique Nash Equilibrium point in the sub-game process of both parties. The analysis based on these models lays a theoretical foundation on the incentive process of mobile crowdsensing. The soundness of modeling and the accuracy of analysis are verified through extensive simulation, which also guides the design of more sophisticated incentive schemes.

As a promising network architecture, Mobile Edge Computing (MEC), has been proven that can effectively reduce the end-to-end latency and the energy consumption. The Unmanned Aerial Vehicle (UAV) assisted MEC network, where the UAV can provide the computation offloading services for the mobile users, can further alleviate the huge deployment cost of static edge servers. However, it remains unsolved how multiple cooperative flying UAVs serve the ground users, especially considering that these UAVs may share the same wireless channel and can communicate with the users while flying. In this paper, we first propose the Age of Task (AoT) metric to measure the quality of experience, and then formulate the joint optimization problem to minimize the worst AoT among all the users. Based on the block coordinate descent (BCD) method, this problem is transformed into three non-convex programming sub-problems (i.e., the UAV-user association sub-problem, the UAV trajectory planning sub-problem and the transmit power optimization sub-problem). Specifically, the successive convex approximation (SCA) technique is exploited iteratively to deal with the non-convexity in the UAV trajectory and transmit power optimization. Numerical results show that the proposed scheme outperforms the benchmark offloading schemes in terms of AoT.

Recently, adopting mobile edge computing (MEC) to accommodate the compute-intensive and delay-sensitive tasks from mobile devices has gained increasing attention from the research community. In contrast to a cloud-centric scheme, deploying servers at the network edge offers the advantage of delivering faster and more efficient services. However, pioneering works primarily focus on a homogeneous server deployment strategy, which distributes the same quantity of servers among a specific number of selected locations. In this work, we aim to lay the theoretical foundation for budget constrained profits maximization (BCPM) problem, which is a coupled problem of server deployment and task scheduling. Subsequently, a two-step optimization method is proposed. Through seeking the maximum matches in the constructed bipartite graph, a task scheduling algorithm is first designed to maximize the profits under the server deployment. Then two approximation algorithms with provable approximation ratios are exploited to perform nearly optimal deployment of servers in a homogeneous and heterogeneous manner, respectively. Extensive simulations with real-world data set and system settings are conducted. The results show that the proposed algorithms can achieve at least a 10.54% increase in total profits and the average processing delay of tasks can be shortened by about 17%.