KEYWORDS: Sensors, Data modeling, Sensor networks, Systems modeling, Calibration, Reliability, Signal to noise ratio, Telecommunications, Mobile communications, Instrument modeling
Sensor networks are finding application as monitoring systems and as tools in the study of complex natural systems. In either situation, the primary goal is computation of some inference from the observations and available models. From this basic problem flows a broad set of practical and theoretical issues, among them assurance of data integrity, sufficiency of data to support the inferences made concerning models/hypotheses, deployment density, and what tools and hardware are required not just to take observations but enable a community of non-engineers to participate in and adapt a sequence of experiments as new observations are obtained. The resulting constraints for designing systems for such purposes are quite different from those commonly assumed in the infancy of wireless sensor network research, and even now in much ongoing systems research. We describe these constraints in light of experience in deploying sensor networks in support of scientific study at the Center for Embedded Networked Sensors (CENS).
This paper focuses on communications within a high-mobility context with OFDM as the modulation of choice. We first show that the Air-to-Ground (AtG) channel is the most difficult to adapt using standard channel equalization methods. We then derive a new channel model (AtGMM) for the AtG case and use it as the basis for analysis and for simulating the effects within the AtG environment. Next, we derive the optimal channel-estimation-based equalizer for the AtG channel; showing performance equivalent to a comparable time-invariant channel. We then analyze the performance of conventional OFDM-based systems using the AtGMM channel model and provide guidelines to maximizing the throughput within differing mobility contexts.
The problems of blind decorrelation and blind deconvolution have attracted considerable interest recently. These two problems traditionally have been studied as two different subjects, and a variety of algorithms have been proposed to solve them. In this paper, we consider these two problems jointly in the application of a multi-sensor network and propose a new algorithm for them. In our model, the system is a MIMO system (multiple-input multiple-output) which consists of linearly independent FIR channels. The unknown inputs are assumed to be uncorrelated and persistently excited. Furthermore, inputs can be colored sources and their distributions can be unknown. The new algorithm is capable of separating multiple input sources passing through some dispersive channels. Our algorithm is a generalization of Moulines' algorithm from single input to multiple inputs. The new algorithm is based on second order statistics which require shorter data length than the higher order statistics algorithms for the same estimation accuracy.
KEYWORDS: Deconvolution, Computer simulations, Sensors, Telecommunications, Monte Carlo methods, Quadrature amplitude modulation, Signal processing, Signal to noise ratio, Modulation, Data communications
For single-input multiple-output (SIMO) systems blind deconvolution based on second-order statistics has been shown promising given that the sources and channels meet certain assumptions. In our previous paper we extend the work to multiple-input multiple-output (MIMO) systems by introducing a blind deconvolution algorithm to remove all channel dispersion followed by a blind decorrelation algorithm to separate different sources from their instantaneous mixture. In this paper we first explore more details embedded in our algorithm. Then we present simulation results to show that our algorithm is applicable to MIMO systems excited by a broad class of signals such as speech, music and digitally modulated symbols.
Distributed microsensor networks, built from collections of nodes each having the ability to sensor their environment, process the raw sensor data in cooperation with other neighboring nodes into information and then communicate that information to end users. These systems are designed to be self-organizing in the sense of establishing and maintaining their own network without the need for specialistic operators. In most envisioned applications, wireless communications are the most practical means of interconnection, eliminating the internode cabling. Long periods of autonomous operations in remote environments will need battery or other renewable energy sources. In order to prolong battery life, all node hardware and software functions need to be designed to consume minimal power. In general, a node will expend energy on local processing of sensor data to produce compressed information in order to reduce communications. These network systems are intended to support large numbers of such nodes to cover large geographic areas.
Advances in CMOS IC and micro electrical-mechanical systems (MEMS) technologies are enabling construction of low-cost building blocks each of which incorporates sensing, signal processing, and wireless communications. Collections of these integrated microsensor nodes may be formed into sensor networks in a wide variety of ways, with characteristics that depend on the specific application--the total number of nodes, the spatial density, the geometric configuration (e.g., linear vs. areal), topographic aspects (e.g., smooth vs. rough terrain), and proximity and proportion of user/sink points. The power of these distributed sensor networks will be unleashed by means of their ability to self-organize, i.e., to bootstrap and dynamically maintain organizational structure befitting the purpose and situation that is presented, without the need for human assistance. A prototype sensor system and networking protocols are being developed under the DARPA/TTO AWAIRS Program and are described.
Wireless Integrated Network Systems (WINS) provide distributed network and Internet access to sensors, controls, and processors that are deeply embedded in equipment, facilities, and the environment. The WINS network is a new monitoring and control capability for applications in transportation, manufacturing, health care, environmental monitoring, and safety and security. WINS combine microsensor technology, low power signal processing, low power computation, and low power, low cost wireless networking capability in a compact system. WINS networks will provide sensing, local control, and embedded intelligent systems in structures, materials, and environments. This paper describes the WINS architecture and WINS technology components including sensor interface and WINS event recognition systems.
KEYWORDS: Sensors, Signal to noise ratio, Network security, Receivers, Telecommunications, Signal processing, Sensor networks, Network architectures, Information security, Fusion energy
A very important benefit of continuing advances in CMOS IC technology is the ability to construct a wide variety of micro electrical mechanical systems (MEMS), including sensors and RF components. These building blocks enable the fabrication of complete systems in a low-cost module, which include sensing, signal processing, and wireless communications. Together with innovative and focused network design techniques that will make possible simple deployment and sustained low- power operation, the small size and cost can be enabling for a very large number of law enforcement and security applications, including remote reconnaissance and security zones ranging from persons to borders. We outline how the application can be exploited in the network design to enable sustained low-power operation. In particular, extensive information processing at nodes, hierarchical decision-making, and energy conserving routing and network topology management methods will be employed in the networks under development.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.