This paper investigates the power mode management problem for an IEEE 802.11-based mobile ad hoc network (MANET) that allows mobile hosts to tune to the power-saving (PS) mode. There are two major issues that need to be addressed in this problem: (a) wakeup prediction and (b) neighbor discovery. The former is to deliver buffered packets to a PS host at the right time when its radio is turned on. The latter is to monitor the environment change under a mobile environment. One costly, and not scalable, solution is to time-synchronize all hosts. Another possibility is to design asynchronous protocols as proposed by Tseng et al. in [25]. In this paper, we adopt the latter approach and correlate this problem to the quorum system concept. We identify a rotation closure property for quorum systems. It is shown that any quorum system that satisfies this property can be translated to an asynchronous power-saving protocol for MANETs. Thus, the result bridges the classical quorum system design problem in the area of distributed systems to the power mode management problem in the area of mobile ad hoc networks. We derive a lower bound for quorum sizes for any quorum system that satisfies the rotation closure property. We identify a group of quorum systems that are optimal or near optimal in terms of quorum sizes, which can be translated to efficient asynchronous power-saving protocols. We also propose a new e-torus quorum system, which can be translated to an adaptive protocol that allows designers to trade hosts' neighbor sensibility for power efficiency. Simulation experiments are conducted to evaluate and compare the proposed protocols.

Abstract-Interactive 3D content on Internet has yet become popular due to its typically large volume and the limited network bandwidth. Progressive content transmission, or 3D streaming, thus is necessary to enable real-time content interactions. However, the heavy data and processing requirements of 3D streaming challenge the scalability of client-server delivery methods. We propose the use of peer-to-peer (P2P) networks for 3D streaming, and argue that due to the non-linear access patterns of 3D content, P2P 3D streaming is a new class of applications apart from existing media streaming and requires new investigations.We also present FLoD, the first P2P 3D streaming framework that allows clients of 3D virtual globe or virtual environment (VE) applications to obtain relevant data from other clients while minimizing server resource usage. To demonstrate how FLoD applies to real-world scenarios, we build a prototype system that adapts JPEG 2000-based 3D mesh streaming for P2P delivery. Experiments show that server-side bandwidth usage can thus be reduced, while simulations indicate that P2P 3D streaming is fundamentally more scalable than client-server approaches.

In this paper, we fit RSSI values into a parabola function of the AoA between 0 ∘ and 90 ∘ by applying quadratic regression analysis. We also set up two-directional antennas with perpendicular orientations at the same position and fit the difference of the signal RSSI values of the two antennas into a linear function of the AoA between 0 ∘ and 90 ∘ by linear regression analysis. Based on the RSSI-fitting functions, we propose a novel localization scheme, called AoA Localization with RSSI Differences (ALRD), for a sensor node to quickly estimate its location with the help of two beacon nodes, each of which consists of two perpendicularly orientated directional antennas. We apply ALRD to a WSN in a 10 × 10 m indoor area with two beacon nodes installed at two corners of the area. Our experiments demonstrate that the average localization error is 124 cm. We further propose two methods, named maximum-point minimum-diameter and maximum-point minimum-rectangle, to reduce localization errors by gathering more beacon signals within 1 s for finding the set of estimated locations of maximum density. Our results demonstrate that the two methods can reduce the average localization error by a factor of about 29%, to 89 cm.

The cyber-physical system is the core concept of Industry 4.0 for building smart factories. We can rely on the ISA-95 architecture or the 5C architecture to build the cyber-physical system for smart factories. However, both architectures emphasize more on vertical integration and less on horizontal integration. This article proposes the 8C architecture by adding 3C facets into the 5C architecture. The 3C facets are coalition, customer, and content. The proposed 8C architecture is a helpful guideline to build the cyber-physical system for smart factories. We show an example of designing and developing, on the basis of the proposed 8C architecture, a smart factory cyber-physical system, including an Industrial Internet of Things gateway and a smart factory data center running in the cloud environment.

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This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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