The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research.

Platoon refers to a group of vehicles traveling in a train-like strategy with a lean inter-vehicle gap, which can increase road capacity and reduce energy consumption. A platoon is composed of several member vehicles and one leader vehicle which determines the driving pattern of the platoon. Therefore, it is crucial to select a vehicle with the highest reputation value as the leader vehicle in a platoon. Reputation value is a private parameter of each vehicle, and how to preserve its privacy is also an issue worth paying attention to. Therefore, in this paper, a reputation-based and privacy-preserving platoon management (RPPM) scheme in vehicular networks is proposed. Specifically, we design a secure comparison protocol (SCP) to select a leader vehicle for each platoon. The SCP protocol not only reduces the involvement of trust authority but also preserves the privacy of vehicles’ reputation values. Furthermore, the cloud server aggregates reputation ciphertexts and feedback scores of the member vehicles based on the homomorphism characteristic of Paillier ciphertexts, and the reputation value privacy of member vehicles is preserved without affecting the aggregation results. The theoretical analysis indicates that the RPPM scheme is privacy-preserving and secure enough to resist several common attacks in vehicular networks. Simulations are conducted to demonstrate the performance of the RPPM scheme, and the results show that the RPPM scheme significantly outperforms the existing schemes in computation and communication overheads.

Incentive plays an important role in mobile crowdsensing (MCS), as it impels mobile users to participate in sensing tasks and provide high-quality sensing data. However, considering the privacy (including identity privacy, sensing data privacy, and reputation value privacy) and practicality (including reliability, quality awareness, and efficiency) issues in practice, it is a challenge to design such an effective incentive scheme for MCS applications. Existing studies either fail to provide adequate privacy-preserving capabilities or have low practicality. To address these issues, we propose a scheme called BRRV in MCS which relies on two rounds of range reliability assessment to guarantee the reliability of data while achieving privacy preservation. In addition, we also present a lightweight scheme called LRRV in MCS which relies on a single round of range reliability assessment to guarantee the reliability of data while achieving lightweight and privacy preservation. Moreover, to fairly stimulate participants, constrain participants' malicious behavior, and improve the probability of high-quality data, we design a quality-aware reputation-based reward and penalty strategy to achieve dual incentives (including money incentives and reputation incentives) for participants. Furthermore, comprehensive theoretical analysis and experimental evaluation demonstrate that our proposed schemes are significantly superior to the existing schemes in several aspects.

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%.

Mobile Edge Computing (MEC) is a forward-looking technology that provides services through resources to meet the needs of cloud-edge Internet of Things (IoT) devices. It provides computing and storage data facilities for IoT users and further renders services through resources in vicinity to fulfill the needs from IoT devices at the cloud edge. However, a major difficulty in guaranteeing reliable resource provisioning is mobility, which brings in chances of service migrations among difference distributed edge nodes and thus causes potential risks of service failures or disruptions. Existing solutions in this direction can be ineffective since they tend to consider that stability of inter-edge-node data transmission to be irrelevant to user mobility and are thus in lack of a comprehensive model for estimating effectiveness of migration paths selected. In this paper, instead, we consider that the effectiveness of migrations paths to be selected are highly dependent on user mobility as well as inter-edge-node stability propose a novel predictive and mobile track-aware approach to fault-tolerant service migration path selection in MEC (PTSM). It is capable of exploiting uses trajectories for accurate predictions of future tracks and selecting target servers as well as migration paths with guaranteed migration reliability and performance in terms of multiple metrics. We demonstrate with extensive simulations and numerical results that our proposed method outperforms its peers in terms of migration reliability and performance.

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods.