With the powerful sensing, computing capabilities of mobile devices, large-scale users with smart devices throughout the city would be the perfect carrier for the people-centric scheme, namely, mobile crowdsensing. Mobile crowdsensing has become a versatile platform for many Internet of things applications in urban scenarios. So how to select the appropriate users to complete the tasks and ensure the quality of the tasks has been a huge challenge for mobile crowdsensing. In this article, we propose a willingness-aware user recruitment strategy based on the task attributes to solve this problem. First, we divide the whole sensing region based on task attributes by a weighted Voronoi diagram and conduct the assessment about the sub-regions according to several parameters, and then categorize sub-regions as hot regions and blank regions. Moreover, we analyze the influence of user willingness on user recruitment and the task completion rate and assess the coverage ability of the users. Finally, we use the greedy method to optimize the user recruitment for each task to select the most suitable users for the tasks. Simulation results show that the willingness-aware user recruitment approach can significantly improve the task completion rate and achieve higher task coverage quality compared with other algorithms.
This paper investigates the secure rate-splitting multiple access (RSMA) cooperation for the maritime cognitive unmanned aerial vehicle (UAV) network. Specifically, we first take into account the primary privacy information and the secondary maritime UAV’s quality of service. Then, we formulate an optimization problem to maximize UAV’s transmission rate according to the RSMA decoding principle and primary information security requirements. To solve this non-convex problem, we design a CPFS algorithm to allocate the transmit power and adjust the UAV’s location. In addition, the worst case is analyzed, which is the lower-bound secondary transmission rate. Finally, simulation results indicate that the proposed scheme improves the UAV’s transmission rate compared with the traditional schemes.
Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies.
D2D communication improves the cellular network performance by using proximity-based services between adjacent devices, which considered is an effective way to solve the problem of spectrum scarcity caused by tremendous mobile data traffic. If the cache-enabled users are willing to send the cached file to the requesters, the content delivery traffic can be offloaded through the D2D link. In this paper, we strive to find the maximum energy efficiency of the D2D caching network through the joint optimization of cache policy and content transmit power. Specifically, based on stochastic geometry-aided modeling of the network, we derive the data offloading rate in closed form, which jointly considers the effects of success sensing probability and success transmission probability. According to the data offloading rate, we formulate a joint optimization problem integrating cache policy and transmit power to maximize the system energy efficiency. To solve this problem, we propose two optimization algorithms that the cache policy optimization algorithm based on gradient update and the joint optimization algorithm. The simulation results demonstrate that the joint optimization has twice the superiority in improving the energy efficiency of the D2D caching network compared with other schemes.
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