Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method.

Mobile crowdsensing systems (MCSs) are widely used in data collection due to their flexible deployment and comprehensive coverage in many IoT scenarios (e.g., road condition monitoring). Recently, the difference between workers’ perception on location privacy has drawn researchers’ attention. The only privacy trading mechanism in MCSs has been designed, however, in a single auction and single-minded way. Realizing task requesters’ competition requirement and workers’ task preference variance, in this article, we are the first to propose a double MCS auction mechanism with a personalized location privacy incentive. Specifically, this article introduces the concept of privacy budget, allowing workers to decide how much location information to disclose to the platform to realize personalized location privacy protection. Besides, considering the heterogeneity of sensing tasks and the diversity of task selection, each worker is allowed to offer several bids for interested tasks and to perform a subset of tasks in a bid if wins. In addition, our auction mechanism enables the platform to select winning requesters and workers and achieve ideal sensing service accuracy. Extensive theoretical analysis and experiment results validate that the proposed mechanism satisfies budget balance, individual rationality, and 2-D-truthfulness.

Physical layer authentication (PLA) has emerged as a promising alternative to complex cryptographic-based authentication schemes, especially for the Internet of Vehicles (IoV) scenarios with resource-limited onboard units (OBUs). However, the existing PLA schemes securing the IoV against GPS location spoofing/falsification attacks consider only insider attackers. Moreover, they cannot be used by mobile vehicles to validate GPS locations. To address these issues, this paper proposes a PLA scheme based on the Gaussian process (GP) path loss prediction, where channel state information (CSI) is used to track the variation of the channel characteristics and predict the next legitimate path loss (PL) of the signal from a transmitter for authentication. The key ideas in the proposed scheme are to first establish a mapping between the historical CSI attributes and PL features of the transmitter’s signal and use this mapping to predict the next PL, which is then used to cross-verify the transmitter’s reported location information. Extensive simulation experiments are conducted using generated radio channel characteristics from the quasideterministic radio channel generator (QuaDRiGa) to demonstrate the effectiveness of the proposed approach. The results of the experiments show that our system efficiently addressed the limitations of the existing works and improves the authentication performance in IoV environments.

Software-as-a-service (SaaS) clouds grow dramatically due to cost-effectiveness, availability, and flexibility. Quality of service (QoS) and power, which represent performance and cost, respectively, are conflicting yet critical issues in the service scheduling of SaaS clouds, and some researchers have investigated the tradeoff between them. However, existing works do not involve QoS attacks in which untrusted service providers provide fake QoS values to absorb service requests, resulting in lower user experience and system profits. In this paper, we jointly consider the QoS performance, queue congestion, and energy consumption to formulate the performance-power tradeoff while considering QoS attacks. To address this NP scheduling problem, we propose a Lyapunov-based decomposition strategy that converts the original problem into three equivalent subproblems. By aggregating the solving strategies for the three subproblems, we develop the online service selection and trustworthiness management algorithm that optimizes the performancepower tradeoff while resisting QoS attacks. In addition, a light-weighted trustworthiness management strategy is designed to update trustworthiness values without storing large amounts of past information. Mathematical analyses and simulations demonstrate that our proposed control framework realizes detection and resistance of QoS attacks and a $[O(1 / V), O(V)]$ tradeoff between performance and power with a performance-power tradeoff parameter V.

Autonomous aerial vehicle (AAV)-aided mobile edge computing (MEC) with a blockchain consensus algorithm is a promising solution for addressing mobile offloading of computation-intensive or latency-sensitive tasks with extensive service range, while ensuring the authenticity of the offloading. Existing autonomous aerial vehicle (AAV)-aided MEC studies focus on task offloading or trajectory planning of single-antenna AAVs, missing multiple-input multiple-output (MIMO) AAV systems. In addition, most blockchain-based AAV-aided edge computing studies adopt energy-consuming proof-of-work-based consensus mechanisms, which are unsuitable for energy-limited AAV scenarios. To address the above issues, in this paper, we develop a joint deadline-aware task offloading and AAV trajectory planning scheme, utilizing the effects simulated through an energy-efficient consensus protocol. In order to prevent the exhaustion of AAVs’ energy and ensure the authenticity of decision-making, we propose an energy-based Raft (E-Raft) consensus algorithm, enabling dynamic leader selection through a series of decreasing energy thresholds. Subsequently, we present a computational profit maximization problem to jointly optimize deadline-aware task offloading and the AAV swarm’s trajectory planning. To address this NP-hard problem, we develop an online priority-based task offloading and trajectory selection algorithm, which is performed by the selected leader of the E-Raft consensus in each round. Simulation results demonstrate that our proposed scheme achieves up to 40% performance improvement over other approaches.