817 research outputs found
Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence
Inferring events of interest by fusing data from multiple heterogeneous sources has been an interesting and important topic in recent years. Several issues related to inference using heterogeneous data with complex and nonlinear dependence are investigated in this dissertation. We apply copula theory to characterize the dependence among heterogeneous data.
In centralized detection, where sensor observations are available at the fusion center (FC), we study copula-based fusion. We design detection algorithms based on sample-wise copula selection and mixture of copulas model in different scenarios of the true dependence. The proposed approaches are theoretically justified and perform well when applied to fuse acoustic and seismic sensor data for personnel detection. Besides traditional sensors, the access to the massive amount of social media data provides a unique opportunity for extracting information about unfolding events. We further study how sensor networks and social media complement each other in facilitating the data-to-decision making process. We propose a copula-based joint characterization of multiple dependent time series from sensors and social media. As a proof-of-concept, this model is applied to the fusion of Google Trends (GT) data and stock/flu data for prediction, where the stock/flu data serves as a surrogate for sensor data.
In energy constrained networks, local observations are compressed before they are transmitted to the FC. In these cases, conditional dependence and heterogeneity complicate the system design particularly. We consider the classification of discrete random signals in Wireless Sensor Networks (WSNs), where, for communication efficiency, only local decisions are transmitted. We derive the necessary conditions for the optimal decision rules at the sensors and the FC by introducing a hidden random variable. An iterative algorithm is designed to search for the optimal decision rules. Its convergence and asymptotical optimality are also proved. The performance of the proposed scheme is illustrated for the distributed Automatic Modulation Classification (AMC) problem. Censoring is another communication efficient strategy, in which sensors transmit only informative observations to the FC, and censor those deemed uninformative . We design the detectors that take into account the spatial dependence among observations. Fusion rules for censored data are proposed with continuous and discrete local messages, respectively. Their computationally efficient counterparts based on the key idea of injecting controlled noise at the FC before fusion are also investigated.
In this thesis, with heterogeneous and dependent sensor observations, we consider not only inference in parallel frameworks but also the problem of collaborative inference where collaboration exists among local sensors. Each sensor forms coalition with other sensors and shares information within the coalition, to maximize its inference performance. The collaboration strategy is investigated under a communication constraint. To characterize the influence of inter-sensor dependence on inference performance and thus collaboration strategy, we quantify the gain and loss in forming a coalition by introducing the copula-based definitions of diversity gain and redundancy loss for both estimation and detection problems. A coalition formation game is proposed for the distributed inference problem, through which the information contained in the inter-sensor dependence is fully explored and utilized for improved inference performance
Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks
It has been shown that cooperative localization is capable of improving both
the positioning accuracy and coverage in scenarios where the global positioning
system (GPS) has a poor performance. However, due to its potentially excessive
computational complexity, at the time of writing the application of cooperative
localization remains limited in practice. In this paper, we address the
efficient cooperative positioning problem in wireless sensor networks. A
space-time hierarchical-graph based scheme exhibiting fast convergence is
proposed for localizing the agent nodes. In contrast to conventional methods,
agent nodes are divided into different layers with the aid of the space-time
hierarchical-model and their positions are estimated gradually. In particular,
an information propagation rule is conceived upon considering the quality of
positional information. According to the rule, the information always
propagates from the upper layers to a certain lower layer and the message
passing process is further optimized at each layer. Hence, the potential error
propagation can be mitigated. Additionally, both position estimation and
position broadcasting are carried out by the sensor nodes. Furthermore, a
sensor activation mechanism is conceived, which is capable of significantly
reducing both the energy consumption and the network traffic overhead incurred
by the localization process. The analytical and numerical results provided
demonstrate the superiority of our space-time hierarchical-graph based
cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE
Transactions on Signal Processing, Sept. 201
Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks
This paper proposes an energy-efficient counting rule for distributed
detection by ordering sensor transmissions in wireless sensor networks. In the
counting rule-based detection in an sensor network, the local sensors
transmit binary decisions to the fusion center, where the number of all
local-sensor detections are counted and compared to a threshold. In the
ordering scheme, sensors transmit their unquantized statistics to the fusion
center in a sequential manner; highly informative sensors enjoy higher priority
for transmission. When sufficient evidence is collected at the fusion center
for decision making, the transmissions from the sensors are stopped. The
ordering scheme achieves the same error probability as the optimum
unconstrained energy approach (which requires observations from all the
sensors) with far fewer sensor transmissions. The scheme proposed in this paper
improves the energy efficiency of the counting rule detector by ordering the
sensor transmissions: each sensor transmits at a time inversely proportional to
a function of its observation. The resulting scheme combines the advantages
offered by the counting rule (efficient utilization of the network's
communication bandwidth, since the local decisions are transmitted in binary
form to the fusion center) and ordering sensor transmissions (bandwidth
efficiency, since the fusion center need not wait for all the sensors to
transmit their local decisions), thereby leading to significant energy savings.
As a concrete example, the problem of target detection in large-scale wireless
sensor networks is considered. Under certain conditions the ordering-based
counting rule scheme achieves the same detection performance as that of the
original counting rule detector with fewer than sensor transmissions; in
some cases, the savings in transmission approaches .Comment: 7 pages, 3 figures. Proceedings of FUSION 2018, Cambridge, U
- …