Search

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance. [ABSTRACT FROM AUTHOR]

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network. [ABSTRACT FROM AUTHOR]

Crowdsensing has been recognized as a promising data collection paradigm, in which a platform outsources sensing tasks to a large number of users. However, requesting users to report raw data may give rise to many practical concerns, such as a significant overhead of communication and central processing, besides users’ privacy concerns. In many scenarios (e.g, advertising and recommendation), the data collector directly benefits from statistical aggregation of raw data. Thus motivated, we consider the data collection problem based on user's local histograms, which is intimately related to the fundamental trade-off between the platform's accuracy and users’ privacy. Because of users’ social relationship, their data are often correlated, indicating that users’ privacy may be leaked from others’ data. To tackle this challenge, we first utilize Gaussian Markov random fields to model the correlation structure embedded in users’ data. The data collection is modeled as a Stackelberg game where the platform decides its reward policy and users decide their noise levels while taking into account the social coupling among users. For the reward policy design, we first establish the relationship between users’ Nash equilibrium and the payment mechanism, and then optimize the platform's accuracy under a budget constraint. Further, since the noise levels are users’ private information, they may use falsified noise levels to achieve higher payoffs, which in turn impairs the crowdsensing performance. It turns out that with the insight into the correlation structure among users’ data, the information asymmetry can be overcome based on peer prediction. We revisit the payment mechanism to guarantee dominant truthfulness of each user's strategy. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

The past years have seen a rapid development of autonomous underwater vehicle-aided (AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The use of AUVs efficiently reduces energy consumption of sensor nodes. However, all AUV-aided solutions face severe problems in data collection delay, especially in a large-scale network. In this paper, to reduce data collection delay, we propose a prediction-based delay optimization data collection algorithm (PDO-DC). On the contrary to the traditional delay optimization algorithms, Kernel Ridge Regression (KRR) is utilized via cluster member nodes to obtain the corresponding prediction models. Then, the AUV can obtain all cluster data by traversing less cluster head nodes, which can effectively reduce the collection delay of the AUV. The experimental results demonstrate that the proposed method is both feasible and effective. [ABSTRACT FROM AUTHOR]

For urban crowdsourcing applications, the data sensing tasks can be achieved by vehicles traveling on the roads, which can save the expenses on constructing dedicated infrastructures. In this paper, to efficiently handle the crowdsourcing recruitment problem, we propose to recruit participants with the duration-variable principle and prove that it performs better compared to the strategy that recruits vehicles for all required time periods. The duration-variable principle enables recruitment of vehicular sensing resources across different time epochs to maximize the crowdsourcing profits. To ensure the utilization of limited budget, we formulate the duration-variable participant recruitment (DPR) problem with the consideration of indeterministic trajectories to determine which vehicle subset is chosen and how many epochs are provided for each selected vehicle. Since the formulated problem is NP-hard, we propose a two-step DPR algorithm by maximizing the available sensing resource utilization efficiency in each recruitment round, which is shown to be near-optimal and has low computational complexity. Experiments on real traces show that the proposed DPR scheme exceeds three other solutions in providing higher spatial coverage for urban crowdsourcing applications. [ABSTRACT FROM PUBLISHER]

In wireless sensor networks (WSNs), one major challenge is how to prolong the network lifetime while maintaining a certain data collection rate for resource-limited static sensors. To achieve this goal, many mobility-assisted data collection (MADC) schemes have been proposed in the literature. However, there is a lack of systematic analysis on the behaviors of the MADC models in terms of both throughput capacity, which is defined as the maximal data collection rate, and lifetime, which will be associated with a specific data collection rate. In this paper, we address this issue in a large-scale WSN with mobile sinks from a theoretical perspective, which has not yet been studied. In particular, we first propose a general MADC model that includes many important parameters such as the number of mobile sinks, the velocity of a mobile sink, and the traveling path of a mobile node. We then develop a comprehensive theoretical approach to obtain the achievable throughput capacity and lifetime. By applying the proposed approach, we investigate the behaviors of WSNs with one or more mobile sinks. Our analysis not only shows how a WSN with mobile sinks can outperform a static WSN but also provides insights on how we can adjust the MADC parameters to improve the data collection rate and to maximize the lifetime. Finally, our analysis is validated through extensive simulations. [ABSTRACT FROM PUBLISHER]