Qingquan Sun

A Multi-Agent-Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation

The smart-house technology aims to increase home automation and security with reduced energy consumption. A smart house consists of various intelligent sensors and actuators operating on different platforms with conflicting objectives. This paper proposes a multi-agent system (MAS) design framework to achieve smart house automation. The novelties of this work include the developments of (1) belief, desire and intention (BDI) agent behavior models; (2) a regulation policy-based multi-agent collaboration mechanism; and (3) a set of metrics for MAS performance evaluation. Simulations of case studies are performed using the Java Agent Development Environment (JADE) to demonstrate the advantages of the proposed method.

Mobile Target Scenario Recognition Via Low-Cost Pyroelectric Sensing System: Toward a Context-Enhanced Accurate Identification

Distributed binary pyroelectric sensor network (PSN) is a low-cost alternative to video systems for human monitoring applications. This paper presents a PSN-based mobile target recognition system, which aims to achieve multitarget, complex scenario recognition. In this system, a novel pseudorandom visibility mode is designed for the sensor arrays to help capture statistical information of scenarios, and a sensor array fusion scheme is adopted to facilitate discriminative feature extraction. Moreover, we propose a statistical subspace representation model called probabilistic nonnegative matrix factorization (PNMF) to seek the scenario patterns rather than the object characteristics. We also further prove that our PNMF model is a generic model for NMF based algorithms. Original NMF, sparse NMF, and smooth NMF are special cases of the PNMF model. The simulation and experimental results demonstrate the advantages of our proposed method. Our system can be further developed to function as an independent facility for intelligent monitoring applications, especially under poor illumination circumstances.

Unsupervised Multi-Level Non-Negative Matrix Factorization Model: Binary Data Case

Rank determination issue is one of the most significant issues in non-negative matrix factorization (NMF) research. However, rank determination problem has not received so much emphasis as sparseness regularization problem. Usually, the rank of base matrix needs to be assumed. In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. From machine learning point of view, the learning result depends on its prior knowledge. In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. Such a construction could apply more prior knowledge to the algorithm and obtain a better approximation of real data structure. The final bases selection is achieved through L2-norm optimization. We implement our experiment via binary datasets. The results demonstrate that our approach is able to retrieve the hidden structure of data, thus determine the correct rank of base matrix.

Human Movement Modeling and Activity Perception Based on Fiber-Optic Sensing System

This paper presents a flexible fiber-optic sensor-based pressure sensing system for human activity analysis and situation perception in indoor environments. In this system, a binary sensing technology is applied to reduce the data workload, and a bipedal movement-based space encoding scheme is designed to capture peoples geometric information. We also develop a nonrepetitive encoding scheme to eliminate the ambiguity caused by the two-foot structure of bipedal movements. Furthermore, we propose an invariant activity representation model based on trajectory segments and their statistical distributions. In addition, a mixture model is applied to represent scenarios. The number of subjects is finally determined by Bayesian information criterion. The Bayesian network and region of interests are employed to facilitate the perception of interactions and situations. The results are obtained using distribution divergence estimation, expectation-maximization, and Bayesian network inference methods. In the experiments, we simulated an office environment and tested walk, work, rest, and talk activities for both one and two person cases. The experiment results have demonstrated that the average individual activity recognition is higher than 90%, and the situation perception rate can achieve 80%.

Trustworthy Data Collection From Implantable Medical Devices Via High-Speed Security Implementation Based on IEEE 1363

Implantable medical devices (IMDs) have played an important role in many medical fields. Any failure in IMDs operations could cause serious consequences and it is important to protect the IMDs access from unauthenticated access. This study investigates secure IMD data collection within a telehealthcare [mobile health (m-health)] network. We use medical sensors carried by patients to securely access IMD data and perform secure sensor-to-sensor communications between patients to relay the IMD data to a remote doctors server. To meet the requirements on low computational complexity, we choose N-th degree truncated polynomial ring (NTRU)-based encryption/decryption to secure IMD-sensor and sensor-sensor communications. An extended matryoshkas model is developed to estimate direct/indirect trust relationship among sensors. An NTRU hardware implementation in very large integrated circuit hardware description language is studied based on industry Standard IEEE 1363 to increase the speed of key generation. The performance analysis results demonstrate the security robustness of the proposed IMD data access trust model.

Mobile targets region-of-interest via distributed pyroelectric sensor network: Towards a robust, real-time context reasoning

We have established a multi-walker recognition / tracking testbed based on low-cost pyroelectrc sensor network (PSN). In order to identify a region of interest (RoI) in the monitoring area for the detection of any interesting mobile targets, we propose to use Bayesian machine learning and binary signal projection to extract the statistical contextual features from real-time, high-dimensional PSN data. This paper describes our recent results in this area, which include two aspects: (1) we have proposed to use binary principle component analysis (B-PCA) to interpret the relationship between observed sensor data and hidden context patterns. (2) We have conducted comprehensive experiments from real PSN sensor data to verify the context detection accuracy based on B-PCA models. Our results show that B-PCA can better capture context basis than general PCA algorithm 1.

Non-informative hierarchical Bayesian inference for non-negative matrix factorization

Non-negative matrix factorization (NMF) is an intuitive, non-negative, and interpretable approximation method. Canonical NMF approach could derive some basic components to represent original data, while probabilistic NMF approaches try to introduce some reasonable constraints to optimize the canonical NMF model. However, both of them cannot handle ground-truth bases discovering and model order determination problems. In general, the model order of basis matrix needs to be pre-defined. The model order determines the capability and accuracy of data structure discovering. However, how to accurately infer the model order of basis matrix has not been well investigated. In this paper, we propose a method called non-informative hierarchical Bayesian non-negative matrix factorization (NHBNMF) to automatically determine the model order and discover the data structure. They are achieved through hierarchical Bayesian inference model, maximum a posteriori (MAP) criterion, and non-informative parameters. In NHBNMF method, we first introduce a structure with two-level parameters to enable the entire model to approach the distributions of ground-truth bases. Then we use non-informative parameter scheme to eliminate the hyper-parameter to enable automatic searching. Finally, the model order and ground-truth bases are discovered by using MAP criterion and L2-norm selection. The experiments are conducted based on both synthetic and real-world datasets to show the effectiveness of our algorithm. The results demonstrate that our algorithm can accurately estimate the model order and discover the ground-truth bases. Even for the complicated FERET facial dataset, our algorithm still obtained interpretable bases and achieved satisfactory accuracy of the model order estimation. HighlightsA non-informative hierarchical Bayesian non-negative matrix factorization (NHBNMF) algorithm is proposed.The NHBNMF algorithm can automatically find a set of bases which are close to the set of ground-truth bases.Non-informative parameter is employed to enable automatic bases determination.NHBNMF has satisfied performance on several kinds of data sets.

Primate-inspired adaptive routing in intermittently connected mobile communication systems

Abstract An intermittently connected mobile ad hoc network is a special type of wireless mobile network without fully connected path between the source and destination most of the time. In some related works on mobility models, the missing realism of mobility model has been discussed. However, very few routing protocols based on realistic mobility models have been proposed so far. In this paper, we present a primate-inspired mobility model for intermittently connected mobile networks. Such a mobility model can represent and reflect the mobile features of humans. Traditional routing schemes in intermittently connected mobile networks fail to integrate the mobility model with routing strategy to fully utilize the mobility features. To overcome such a drawback, we propose a new routing scheme called primate-inspired adaptive routing protocol (PARP), which can utilize the features of the primate mobility to assist routing. Furthermore, our proposed protocol can determine the number of message copies and the routing strategy based on the walking length of the mobility model. The predictions of the walking lengths are implemented by a particle filter based algorithm. Our results demonstrate that PARP can achieve a better performance than a few typical routing protocols for intermittently connected mobile ad hoc networks.

Apprenticeship Learning Based Spectrum Decision in Multi-Channel Wireless Mesh Networks with Multi-Beam Antennas

We propose a novel spectrum decision scheme (i.e., channel selection and handoff) for wireless mesh networks (WMN) which use multiple channels and nodes equipped with multi-beam directional antennas. Our scheme has the following features: (i) It performs spectrum decision by considering various WMN parameters, including the channel quality, beam orientation, antenna-caused deafness and capture effects, and application priority level. (ii) It uses the reinforcement learning (RL)-based spectrum decision process to achieve the optimal quality of multimedia transmission in the long term. However, a newly-joined WMN node could take a long time to make a correct spectrum decision due to the difficult choice of initial RL parameters. Therefore, our scheme uses the apprenticeship learning in conjunction with the RL model, to speed up the spectrum decision process by choosing a suitable neighboring node (called “expert”) to teach a newly-joined node (called “apprentice”). Our experiments demonstrate that the proposed spectrum decision scheme improves the network performance and multimedia transmission quality.

Cyberphysical System With Virtual Reality for Intelligent Motion Recognition and Training

In this paper, we propose to build a comprehensive cyberphysical system (CPS) with virtual reality (VR) and intelligent sensors for motion recognition and training. We use both wearable wireless sensors (such as electrocardiogram, motion sensors) and nonintrusive wireless sensors (such as gait sensors) to monitor the motion training status. We first provide our CPS architecture. Then we focus on motion training from three perspectives: 1) VR-first we introduce how we can use motion capture camera to trace the motions; 2) gait recognition-we have invented low-cost small wireless pyroelectric sensor, which can recognize different gaits through Bayesian pattern learning. It can automatically measure gait training effects; and 3) gesture recognition-to quickly tell what motions the subject is doing, we propose a low-cost, low-complexity motion recognition system with 3-axis accelerometers. We will provide hardware and software design. Our experimental results validate the efficiency and accuracy of our CPS design.