Parking space detection is an important part of the automatic parking assistance system. How to use existing sensors to accurately and effectively detect parking spaces is the key problem that has not been solved in the automatic parking system. Advances in Artificial Intelligence and sensing technologies have motivated significant research and development in parking space detection in the automotive field. Firstly, based on extensive investigation of a lot of literature and the latest re-search results, this paper divides parking space detection methods into methods based on traditional visual features and those methods based on deep learning and introduces them separately. Secondly, the advantages and disadvantages of each parking space detection method are analyzed, compared, and summarized. And the benchmark datasets and algorithm evaluation standards commonly used in parking space detection methods are introduced. Finally, the vision-based parking space detection method is summarized, and the future development trend is prospected.

Mobile edge computing (MEC) provides edge services to users in a distributed and on-demand way. Due to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resource-constrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network (GON) model for predicting resource failure and a deep deterministic policy gradient (DDPG) model for yielding preemptive migration decisions. We show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time, and energy consumption than other existing methods.

Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively.

Skyline computation is a typical multi-objective optimization problem and has been a hot spot for current research. Most of them have concentrated on improving the efficiency of skyline computing, while the security issues associated with executing skyline computation in the cloud server are rarely taken into account. The cloud data stored in plaintext form is attacked by hackers or malicious administrators, resulting in potential leakage of users’ private information. One of the most effective approaches to solve this problem is to encrypt the private data before storing it in the cloud server. However, how to compute the skyline on the encrypted data stored on a cloud server efficiently is still challenging. In this paper, we present a novel framework called SecSky. Firstly, the improved B+ tree structure and symmetric encryption were combined to construct a secure storage structure. Then, the pruning idea was applied to reduce unnecessary calculation and to achieve efficient skyline query and dynamic update. The whole process was directly completed on the ciphertext data. Finally, both the security and the efficiency of SecSky were analyzed through theoretical proof and simulation experiments. The results showed that our scheme is of sound efficiency on the premise of ensuring private data security.