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Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) radar technology has attracted significant attention because it is susceptible to environmental lighting, wall shielding, and privacy concern. This article proposes a novel noninvasive human activity recognition system using a mmWave radar. The proposed framework first transforms mmWave signals into point clouds. Generally speaking, it consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine learning to lead to accurate and efficient human activity recognition. The proposed new methodology considers the spatial–temporal point clouds in physical environments through a modified voxelization approach, enriches the sparse data based on the symmetry property of radar rotations, and learns the activity using a dual-view convolutional neural network. To evaluate the performance of the proposed learning models, a dataset involving seven different activities has been established using a mmWave radar platform. The experimental results have demonstrated that the proposed system can achieve 97.61% and 98% accuracies during the tests of fall detection and activity classification, respectively. In comparison, the proposed scheme greatly outperforms four other conventional machine learning schemes in terms of the overall accuracy. [ABSTRACT FROM AUTHOR]

Industrial wireless sensor networks (IWSNs) are the fundamental components in the next-generation factories. Due to massive heterogeneous data generated from large-scale IWSNs, it is still challenging to achieve predictable, deterministic, and real-time transmission scheduling. In this article, a framework of priority-aware packet transmission scheduling (PPTS) in cluster-based IWSNs is proposed, where the PPTS strategy, the optimization theory, and the implementation design are systematically considered. In particular, the proposed PPTS strategy not only minimizes the transmission delay of high priority packets but also greatly improves the transmission delay of low priority packets. The optimization theory for end-to-end priority-aware scheduling in cluster-based IWSNs is formalized, which contributes to the optimal solution for multidimensional network resources allocation and achieves the minimum of average transmission delay. Finally, the advantages of the PPTS framework over some existing solutions are demonstrated by a case study. [ABSTRACT FROM AUTHOR]

Geospatial big data analytics are changing the way that businesses operate, and have enabled various intelligent services in smart cities. User mobility plays an important role in many context-aware applications, such as location-based advertisement, traffic planning, and urban resource management. This paper proposes CityTracker, a hidden Markov model (HMM)-based framework to predict the temporal-spatial individual trajectory and analyze the representative citywide crowd mobility. After the locations are segmented into points of interests and modeled as states, HMM can find the most likely state sequence in a maximum-likelihood sense, and thus perform trajectory prediction with different orders. In addition, CityTracker can integrate the individual trajectories, achieving representative crowd mobility visualization in the target area. The proposed mechanism is evaluated on a database provided by HyXen, which contains temporal-geospatial records from thousands of smartphones in Taipei. Experimental results confirm the effectiveness of CityTracker, which achieves prediction distance errors of approximately 1.28 km and outperforms traditional probabilistic and regression-based methods. The results also show the most representative crowd mobility behaviors in the map, which is essential for citywide applications. [ABSTRACT FROM AUTHOR]

In the mobile industrial human–machine interaction (HMI), to establish the data connection, the engineer has to manually select the target machine from a long list, which may lead to wrong connection and waste of time. We observe that the engineer should face to the machine during the interaction to ensure that the machine works accurately, and this characteristic makes the proximity estimation algorithm suitable to simplify the data connection. However, due to the densely deployed machines, the existing algorithms cannot provide sufficient accuracy with limited latency. In this paper, we implement a testbed to evaluate the performance in the mobile industrial HMI. Based on the experimental results, we propose the definition of received signal strength indicator (RSSI) difference and then use it to design the face-to-machine proximity estimation (FaceME) algorithm. The experimental results prove that FaceME can provide guaranteed estimation accuracy and low-time complexity. [ABSTRACT FROM AUTHOR]

In recent years, the importance of user information has increased rapidly for context-aware applications. This paper proposes a deep learning mechanism to identify the transportation modes of smartphone users. The proposed mechanism is evaluated on a database that contains more than 1000 h of accelerometer, magnetometer, and gyroscope measurements from five transportation modes, including still, walk, run, bike, and vehicle. Experimental results confirm the effectiveness of the proposed mechanism, which achieves approximately 95% classification accuracy and outperforms four well-known machine learning methods. Meanwhile, we investigated the model size and execution time of different algorithms to address practical issues. [ABSTRACT FROM PUBLISHER]

Location service is an efficient solution to handle actor mobility in wireless sensor and actor networks. In previous works, the information that the actor updates to location servers is its latest location. However, in most applications, the actor knows its own mobility strategy, such as its destination, trajectory and speed. If the actor can share its mobility strategy in the update packet, it will be helpful to improve the performance of location service. In this letter, we propose a mobility strategy sharing location service protocol called MLS, to show the description of mobility strategy in the update packet and its utilization for routing. The theoretical analysis and simulation results show that, sharing mobility strategy greatly reduces the update overhead and improves the scalability of MLS. [ABSTRACT FROM PUBLISHER]

Due to the actor mobility and duty cycle of sensor nodes, the data forwarding from sensor nodes to mobile actors is a challenging issue. Motivated by the research results in multi-agent systems, we consider the search packet as an agent moving in the network to search mobile actors. Then a strategy called ballooning is proposed to control the search process. The theoretical and numerical results show that the ballooning strategy can forward the search packet to mobile actors with bounded delay and efficient energy consumption. [ABSTRACT FROM PUBLISHER]