Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.

Caching content on the edge of a network can effectively localize traffic, reduce network latency and improve network throughput. In this paper, we propose an a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretic analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs) and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0-1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks.

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility.