The development of the Internet of Things (IoT) and intelligent vehicles bring a comfortable environment for users. Various emerging vehicular applications using artificial intelligence (AI) technologies are expected to enrich users’ daily life. However, how to execute computation-intensive applications on resource-constrained vehicles based on AI still faces great challenges. In this paper, we consider the vehicular computation offloading problem in mobile edge computing (MEC), in which multiple mobile vehicles select nearby MEC servers to offload their computing tasks. We propose a multi-agent deep reinforcement learning (DRL) based computation offloading scheme, in which the uncertainty of a multi-vehicle environment is considered so that the vehicles can make offloading decisions to achieve an optimal long-term reward. Firstly, we formalize a formula for the computation offloading problem. The goal of this paper is to determine the optimal offloading decision to MEC server under each observed system state, so as to minimize the total task processing delay in a long-term period. Then, we use a multi-agent DRL algorithm to learn an effective solution to the vehicular task offloading problem. To evaluate the performance of the proposed offloading scheme, a large number of simulations are carried out. The simulation results verify the effectiveness and superiority of the proposed scheme.

With the development of wireless communication and positioning technology, location-based services (LBSs) have been gaining tremendous popularity, due to its ability to greatly facilitate the people’s daily lives. Meanwhile, it also entails the risk of location privacy disclosure. To address this issue, general solutions introduce a single trusted anonymizer between the users and the location service provider (LSP). However, a single anonymizer offers limited privacy guarantees and incurs high communication overhead in continuous LBSs. Once the anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose a dual privacy preserving (DPP) scheme in continuous LBSs to protect the users’ trajectory and query privacy. Our scheme introduces multiple anonymizers between the users and LSP, and combines with Shamir threshold mechanism, dynamic pseudonym mechanism and K-anonymity technology to improve the users’ trajectory and content privacy in continuous LBSs. An anonymizer alone cannot get the users’ trajectory and query contents, and it thus can be semi-trusted. Our scheme can enhance the users’ privacy and effectively solve the single point of failure in single anonymizer structure. At the same time, the query authentication can guarantee the correctness of the query results. The analysis and simulation results demonstrate that the proposed scheme has the ability to protect users’ trajectory and content privacy effectively, and to reduce the computation and communication overhead of the single anonymizer.

Billions of sensors and devices are connecting to Internet of Thing (IoT) and generating massive data which are benefit for smart network systems. However, low-cost, secure, reliable and efficient data collection from billions of IoT devices in smart city is a huge challenge. Recruiting mobile vehicles (MVs) has been proved to be an effective data collection method. However, the previous approaches rarely considered the credibility. In this paper, a novel Baseline Data based Verifiable Trust Evaluation (BD-VTE) scheme is proposed to guarantee credibility at a low cost. BD-VTE scheme includes Verifiable Trust Evaluation (VTE) mechanism, Effectiveness-based Incentive (EI) mechanism, and Secondary Path Planning (SPP) strategy, which are respectively used for reliable trust evaluation, reasonable reward, and efficient path adjustment. Among them, an active trust verification mechanism is innovatively proposed in the VTE mechanism, which evaluates the trust of MVs by sending UAVs to perceive IoT devices data as baseline data. This is a fundamental change to the previous passive and unverifiable trust mechanism. The simulation results show that BD-VTE scheme reduces the cost by at least 25.12%~38.03%, improves the collection rate by 0.91%~9.65% and increases the accuracy by 10.28% on average compared with the previous strategies.

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

Using highly maneuverable Unmanned Aerial Vehicles (UAV) to collect data is a fast and efficient method that is widely studied. In most studies, they assume that the UAVs can obtain the location of the Cluster Head (CH) before take-off, allocate CHs, and optimize the trajectory in advance. However, in many real scenarios, many sensing devices are deployed in areas with no basic communication infrastructure or cannot communicate with the Internet due to emergencies such as disasters. In this kind of sensing network, the surviving devices often change, and the CHs cannot be known and allocated in advance, thus bringing new challenges to the efficient data collection of the networks by using UAVs. In this paper, a UAV path planning scheme for IoT networks based on reinforcement learning is proposed. It plans hover points for UAV by learning the historical location of CHs and maximizes the probability of meeting CHs and plans the shortest UAV path to visit all hover points by using the simulated annealing method. In addition, an algorithm to search for the location of CHs is proposed which is named Cluster-head Searching Algorithm with Autonomous Exploration Pattern (CHSA-AEP). By using CHSA-AEP, our scheme enables the UAV to respond to the position change of the CHs. Finally, we compare our scheme with other algorithms (area coverage algorithms and random algorithm). It is found that our proposed scheme is superior to other methods in energy efficiency and time utilization ratio.