Shaowei Lin

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

Block diagonal geometric mean decomposition (BD-GMD) for MIMO broadcast channels

In recent years, the research on multiple-input multiple-output (MIMO) broadcast channels has attracted much interest, especially since the discovery of the broadcast channel capacity achievable through the use of dirty paper coding (DPC). In this paper, we propose a new matrix decomposition, called the block diagonal geometric mean decomposition (BD-GMD), and develop transceiver designs that combine DPC with BD- GMD for MIMO broadcast channels. We also extend the BD- GMD to the block diagonal uniform channel decomposition (BD- UCD) with which the MIMO broadcast channel capacity can be achieved. Our proposed schemes decompose each users MIMO channel into parallel subchannels with identical SNRs/SINRs, thus equal-rate coding can be applied across the subchannels of each user. Numerical simulations show that the proposed schemes demonstrate superior performance over conventional schemes.

Mobile big data analytics using deep learning and apache spark

The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are used to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

A new class of B/W and color halftoning algorithms

A new class of dithering algorithms for black and white (B/W) and color images is presented. The basic idea behind the technique is to divide the image into small blocks and minimize the distortion between the original continuous tone image and its low-pass filtered halftone. This corresponds to a quadratic programming problem with linear constraints which is solved via the branch and bound algorithm. Examples of B/W and color dither images using the technique are shown and compared to halftones obtained via existing dithering algorithms.<<ETX>>

Block-Diagonal Geometric Mean Decomposition (BD-GMD) for Multiuser MIMO Broadcast Channels

Matrix decompositions play an important role in analyzing the capacity and designing the transceiver for multiple input multiple output (MIMO) channels. In the single user case, by relying on the decision feedback equalizer (DFE) at the receiver or Tomlinson-Harashima precoding (THP) at the transmitter, the geometric mean decomposition (GMD) can be used to create identical signal-to-noise ratios (SNR) for each decoupled subchannel. In this paper, we propose a new matrix decomposition, called the block-diagonal GMD (BD-GMD), for the multiuser MIMO broadcast channel. Applying THP at transmitter and linear equalization in each of the receivers, each user can achieve identical SNRs for its subchannels, thus equal-rate coding can be applied for each user. Furthermore, by using transmit power control and the BD-GMD, we design a scheme that achieves equal-rate coding for the subchannels of all users. Computer simulations have shown that the proposed schemes have better BER performances than zero-forcing THP (ZF-THP) and equal-rate ZF-THP schemes

Distributed event detection under Byzantine attack in wireless sensor networks

We present two novel distributed event detection algorithms based on a statistical approach that tolerate Byzantine attacks where malicious (compromised) sensors send false sensing data to the gateway leading to increased false alarm rate. We study the problem of Byzantine attack function optimization and the decision threshold optimization and consider two practical cases in our algorithms. In the first case, the Channel State Information (CSI) between the event generating source and sensors is unknown while CSI between the sensors and gateway is known. In the second case, the CSI between the source and sensors as well as between sensors and gateway are unknown. We develop an optimal event detection decision rule under Byzantine attacks for the first case and a novel low-complexity event detection algorithm based on Gaussian approximation and Moment Matching for the second case which considers a global decision. We evaluate our algorithms through extensive simulations. Simulation results show the Receiver Operating Characteristics (ROC) curves under different cases and scenarios, and therefore provide useful upper bounds for various centralized and distributed scheme designs. We also show that our algorithms provide superior detection performance when compared to local decision based schemes.

Hypersurfaces and Their Singularities in Partial Correlation Testing

An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations. We study these volumes using the method of real log canonical thresholds from algebraic geometry. Our analysis involves the computation of the singular loci of correlation hypersurfaces. Statistical applications include the strong-faithfulness assumption for the PC algorithm and the quantification of confounder bias in causal inference. A detailed analysis is presented for trees, bow ties, tripartite graphs, and complete graphs.

Noncoherent SIMO pre-log via resolution of singularities

We establish a lower bound on the noncoherent capacity pre-log of a temporally correlated Rayleigh block-fading single-input multiple-output (SIMO) channel. Our result holds for arbitrary rank Q of the channel correlation matrix, arbitrary block-length L > Q, and arbitrary number of receive antennas R, and includes the result in Morgenshtern et al. (2010) as a special case. It is well known that the capacity pre-log for this channel in the single-input single-output (SISO) case is given by 1−Q/L, where Q/L is the penalty incurred by channel uncertainty. Our result reveals that this penalty can be reduced to 1/L by adding only one receive antenna, provided that L ≥ 2Q − 1 and the channel correlation matrix satisfies mild technical conditions. The main technical tool used to prove our result is Hironakas celebrated theorem on resolution of singularities in algebraic geometry.

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world data sets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure and, hence, expand the service lifespan by several folds.