The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.

Finding fastest driving routes is significant for the intelligent transportation system. While predicting the online traffic conditions of road segments entails a variety of challenges, it contributes much to travel time prediction accuracy. In this paper, we propose O-Sense, an innovative online-traffic-prediction based route finding mechanism, which organically utilizes large scale taxi GPS traces and environmental information. O-Sense firstly exploits a deep learning approach to process spatial and temporal taxi GPS traces shown in dynamic patterns. Meanwhile, we model the traffic flow state for a given road segment using a linear-chain conditional random field (CRF), a technique that well forecasts the temporal transformation if provided with further supplementary environmental resources. O-Sense then fuses previously obtained outputs with a dynamic weighted classifier and generates a better traffic condition vector for each road segment at different prediction time. Finally, we perform online route computing to find the fastest path connecting consecutive road segments in the route based on the vectors. Experimental results show that O-Sense can estimate the travel time for driving routes more accurately.

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Localization is one of the most important subjects in Wireless Sensor Networks (WSNs). To reduce the number of beacons and adopt probabilistic methods, some particle filter-based mobile beacon-assisted localization approaches have been proposed, such as Mobile Beacon-assisted Localization (MBL), Adapting MBL (A-MBL), and the method proposed by Hang et al. Some new significant problems arise in these approaches, however. The first question is which probability distribution should be selected as the dynamic model in the prediction stage. The second is whether the unknown node adopts neighbors’ observation in the update stage. The third is how to find a self-adapting mechanism to achieve more flexibility in the adapting stage. In this paper, we give the theoretical analysis and experimental evaluations to suggest which probability distribution in the dynamic model should be adopted to improve the efficiency in the prediction stage. We also give the condition for whether the unknown node should use the observations from its neighbors to improve the accuracy. Finally, we propose a Self-Adapting Mobile Beacon-assisted Localization (SA-MBL) approach to achieve more flexibility and achieve almost the same performance with A-MBL.

As many Wireless Sensor Networks (WSNs) applications require sensor position information, localization has been an important problem in WSNs. To reduce the number of seeds, a number of mobile-assisted approaches have been proposed. Current proposed mobile-assisted approaches for localization require special hardware or face route selection problem, however. In this paper, we propose a Mobile-Assisted Monte Carlo Localization (MA-MCL) for WSNs. Our approach relies on direct arriver and leaver information from a single mobile-assisted seed. It does not require any specially designed hardware due to the range-free technique, and the single mobile-assisted seed in our approach can move uncontrollably to avoid route selection problem based on Monte Carlo method. Evaluation results show that the accuracy of MA-MCL outperforms MSL * , MSL, and ADO when all of them use only a mobile seed for localization in the static sensor networks.

Abstract. Precipitation nowcasting plays a vital role in preventing meteorological disasters, and Doppler radar data act as an important input for nowcasting models. When using the traditional extrapolation method it is difficult to model highly nonlinear echo movements. The key challenge of the nowcasting mission lies in achieving high-precision radar echo extrapolation. In recent years, machine learning has made great progress in the extrapolation of weather radar echoes. However, most of models neglect the multi-modal characteristics of radar echo data, resulting in blurred and unrealistic prediction images. This paper aims to solve this problem by utilizing the features of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling, and design the radar echo extrapolation model GAN–argcPredNet v1.0. The model is composed of an argcPredNet generator and a convolutional neural network discriminator. In the generator, a gate controlling the memory and output is designed in the rgcLSTM component, thereby reducing the loss of spatiotemporal information. In the discriminator, the model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and it thus has a more powerful discrimination ability. Through experiments on a radar dataset from Shenzhen, China, the results show that the radar echo hit rate (probability of detection; POD) and critical success index (CSI) have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet under different intensity rainfall thresholds, and the false alarm rate (FAR) has decreased by an average of 17.9 %. We also found a problem during the comparison of the result graph and the evaluation index. The recursive prediction method will produce the phenomenon that the prediction result will gradually deviate from the true value over time. In addition, the accuracy of high-intensity echo extrapolation is relatively low. This is a question worthy of further investigation. In the future, we will continue to conduct research from these two directions.

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