Abstract The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating...

Last. Han-Xiong Li(CityU: City University of Hong Kong)H-Index: 62

view all 5 authors...

Uncertain data clustering has been recognized as an essential task in the research of data mining. Many centralized clustering algorithms are extended by defining new distance or similarity measurements to tackle this issue. With the fast development of network applications, these centralized methods show their limitations in conducting data clustering in a large dynamic distributed peer-to-peer network due to the privacy and security concerns or the technical constraints brought by distributive...

#1Alan Wisler(ASU: Arizona State University)H-Index: 6

#2Visar Berisha(ASU: Arizona State University)H-Index: 17

Last. Alfred O. Hero(UM: University of Michigan)H-Index: 76

view all 4 authors...

A number of fundamental quantities in statistical signal processing and information theory can be expressed as integral functions of two probability density functions. Such quantities are called density functionals as they map density functions onto the real line. For example, information divergence functions measure the dissimilarity between two probability density functions and are useful in a number of applications. Typically, estimating these quantities requires complete knowledge of the und...

Jan 1, 2018 in ICRA (International Conference on Robotics and Automation)

#1Yiwei Wang(ASU: Arizona State University)H-Index: 3

#2Yixuan Sheng(ASU: Arizona State University)H-Index: 2

Last. Wenlong Zhang(ASU: Arizona State University)H-Index: 14

view all 4 authors...

In this letter, we propose that the joint motion of a human worker doing repetitive work could be predicted using a time series model. With a motion capture system, the elbow joint rotation data are collected and used to fit an autoregressive model. An online parameter adaptation algorithm is employed to update model parameters in real time. A safety index with a distance factor is defined to describe the level of safety during physical human–robot interaction. An optimization problem is formula...

This paper proposes an improvement to FastSLAM. The approach is applicable when the dynamic model describing the motion of the camera has linear sub-structure. The core novelty of the proposed algorithm is to separate the consideration of the camera's dynamic model into two sub-models without constraining the two sub-models to have independent noise processes. In contrast to commonly-used FastSLAM algorithms, which use a particle filter to consider both these sub-models, a particle filter is use...

#1Xiaofeng Li(ASU: Arizona State University)H-Index: 5

#1Xiaofeng Li(ASU: Arizona State University)H-Index: 80

Last. Habib Senol(KHU: Kadir Has University)H-Index: 6

view all 3 authors...

Training schemes for full duplex two-way relays are investigated. We propose a novel one-block training scheme with a maximum likelihood estimator to estimate the channels between the nodes as well as the residual self-interference (RSI) channel simultaneously. A quasi-Newton algorithm is used to solve the estimator. As a baseline, a multi-block training scheme is also considered. The Cramer–Rao bounds of the one-block and multi-block training schemes are derived. By using the Szego’s theorem ab...

A relay-aided cooperative underlay cognitive radio system with an interference constraint at the primary receiver (PR) is considered. When the primary user (PU) is active, those secondary users (SUs) whose interference at the PR is above a threshold will amplify-and-forward PU's signal to enhance the PU’s performance, and those SUs below this threshold will proceed with their own transmission in an underlay fashion. As the number of relay SUs increases, the PU’s performance will be improved but ...

#1Dangdang Shao(ASU: Arizona State University)H-Index: 6

#2Francis Tsow(ASU: Arizona State University)H-Index: 18

Last. Nongjian Tao(ASU: Arizona State University)H-Index: 95

view all 5 authors...

We present a noncontact method to measure ballistocardiogram (BCG) and photoplethysmogram (PPG) simultaneously using a single camera. The method tracks the motion of facial features to determine displacement BCG, and extracts the corresponding velocity and acceleration BCGs by taking first and second temporal derivatives from the displacement BCG, respectively. The measured BCG waveforms are consistent with those reported in the literature and also with those recorded with an accelerometer-based...

We present a video-based method to monitor blood pressure by analyzing pulse transit time (PTT). The PTT is determined from ballistocardiogram (BCG) and photoplethysmogram (PPG), which are recorded simultaneously using a single camera. The measured blood pressure changes are compared with those measured with a cuff-based reference blood pressure monitor. In addition to blood pressure, premature ventricular contraction (PVC) is also observed from both PPG and BCG waveforms. The method provides a ...

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identi...

#1Jiahu Qin(USTC: University of Science and Technology of China)H-Index: 33

#2Weiming Fu(USTC: University of Science and Technology of China)H-Index: 7

Last. Wei Xing Zheng(USYD: University of Sydney)H-Index: 82

view all 4 authors...

This paper is concerned with developing a distributed {k}-means algorithm and a distributed fuzzy {c}-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optim...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

view all 3 authors...

A consensus based distributed algorithm to compute the spectral radius of a network is proposed. The spectral radius of the graph is the largest eigenvalue of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. Our distributed algorithm uses a simple update rule to reach consensus on the spectral radius, usi...

Last. Andreas Spanias(ASU: Arizona State University)H-Index: 32

view all 3 authors...

A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spec...

#1Chirag Kumar(IITK: Indian Institute of Technology Kanpur)H-Index: 6

#2Ketan Rajawat(IITK: Indian Institute of Technology Kanpur)H-Index: 16

Common approaches in distributed optimization build upon the consensus framework to enforce cooperation and consistency among the nodes. In many applications however, the inter-node relationships are better modeled via antagonistic or dissensual constraints. These relationships can generally be incorporated via non-convex constraints or penalty functions, and the resulting formulations are flexible enough to subsume a wide variety of classification and discrimination problems. This work develops...