In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help accomplish the intensive calculating tasks from the vehicular users. For the MEC networks, most existing works mainly focus on minimizing the system latency to guarantee the user’s quality of service (QoS) through designing some offloading strategies, which, however, fail to consider the pricing from the server and hence fail to take into account the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC-based IoV networks and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then, the Lagrange multiplier method is employed to allocate the calculating capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperform the conventional ones. In particular, the proposed scheme can effectively reduce the system latency by up to 56% compared to the conventional schemes.

In the VANET systems, the leakage of some sensitive data or communication information will cause heavy losses for life and property. Then, a higher security level is required in the VANET systems. Meanwhile, fast computation powers are needed by the devices with limited computing resources. Thus, a secure and lightweight privacy-preserving protocol for VANETs is urgent. In this paper, we first propose an identity-based signature that achieves unforgeability against chosen-message attack without random oracle. In order to reduce the computational cost, we design two secure and efficient outsourcing algorithms for the exponential operations, where a homomorphic mapping based on matrices conjugate operation is used to achieve the security of both exponent and base numbers. Furthermore, we construct a privacy-preserving protocol for VANETs by using outsourcing computing and the proposed IBS, where a proxy re-signature scheme is presented for authentications. In the VANET privacy-preserving protocol, TA authorizes RSU to act as an agent and RUS converts OBU’s signature into TA’s signature, which effectively hides the real identity of vehicle OBU. Meanwhile, TA has access to trace the real identity of OBU using its secret key when malicious messages are found. Then the protocol provides anonymity, traceability and privacy. In addition, with respect to the efficiency, our scheme does not need pairing operations and exponential operations. Thus, the calculation burdens for the VANET system can be significantly reduced.

To support multi-source data stream generated from Internet of Things devices, edge computing emerges as a promising computing pattern with low latency and high bandwidth compared to cloud computing. To enhance the performance of edge computing within limited communication and computation resources, we study a cloud-edge-end computing architecture, where one cloud server and multiple computational access points can collaboratively process the compute-intensive data streams that come from multiple sources. Moreover, a multi-source environment is considered, in which the wireless channel and the characteristic of the data stream are time-varying. To adapt to the dynamic network environment, we first formulate the optimization problem as a markov decision process and then decompose it into a data stream offloading ratio assignment sub-problem and a resource allocation sub-problem. Meanwhile, in order to reduce the action space, we further design a novel approach that combines the proximal policy optimization (PPO) scheme with convex optimization, where the PPO is used for the data stream offloading assignment, while the convex optimization is employed for the resource allocation. The simulated outcomes in this work can help the development of the application of the multi-source data stream.