The advancement in wireless communications and electronics has enabled the development of low-cost sensor networks. The sensor networks can be used for various application areas (e.g., health, military, home). For different application areas, there are different technical issues that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments in this field.

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A survey on sensor networks

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.

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Sensor networks: evolution, opportunities, and challenges

The origins of the research on WSNs can be traced back to the Distributed Sensor Networks (DSN) program at the Defense Advanced Research Projects Agency (DARPA) at around 1980. By this time, the ARPANET (Advanced Research Projects Agency Network) had been operational for a number of years, with about 200 hosts at universities and research institutes (Chong & Kumar, 2003). DSNs were assumed to have many spatially distributed low-cost sensing nodes that collaborated with each other but operated autonomously, with information being routed to whichever node was best able to use the information. At that time, this was actually an ambitious program. There were no personal computers and workstations; processing was mainly performed on minicomputers and the Ethernet was just becoming popular (Chong & Kumar, 2003). Technology components for a DSN were identified in a Distributed Sensor Nets workshop in 1978 (Proceedings of the Distributed Sensor Nets Workshop, 1978). These included sensors (acoustic), communication and processing modules, and distributed software. Researchers at Carnegie Mellon University (CMU) even developed a communication-oriented operating system called Accent (Rashid & Robertson, 1981), which allowed flexible, transparent accesss to distributed resources required for a fault-tolerant DSN. A demonstrative application of DSN was a helicopter tracking system (Myers et al., 1984), using a distributed array of acoustic microphones by means of signal abstractions and matching techniques, developed at the Massachusetts Institute of Technology (MIT). Even though early researchers on sensor networks had in mind the vision of a DSN, the technology was not quite ready. More specifically, the sensors were rather large (i.e. shoe box and

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Wireless Sensor Networks - An Introduction

Distributed imaging using sensor arrays is gaining popularity among various research and development communities. A common bottleneck within such an imaging sensor network is the large resulting data load. In applications for which transmission power and/or bandwidth are constrained, this can drastically decrease the sensor network lifetime. We present an algorithm that efficiently exploits inter- and intrasensor correlation for the purpose of power-constrained distributed transmission of sensor-network imagery. Gains in network lifetime up to 114% are obtained when using the suggested algorithm with lossless compression. Our results also demonstrate that when lossy compression is employed, much larger gains are achieved. For example, when a normalized root-mean-squared error of 0.78% can be tolerated in the received measurements, the network lifetime increases by a factor of 2.8, as compared to the (optimized) lossless case.

A method for coordinating the distributed transmission of imagery

The mixture of quantitative and qualitative information utilized in decomposition, which suggests that a purely numerical approach to the task would be inefficient, is discussed. The incorporation of symbolic processing methods into the decomposition system to complement the numerical aspects of the task is discussed. The overall philosophy used in the decomposition system is examined, focusing on the principles of detection used. The organization of the software used by the system is described. >

Knowledge-based signal processing in the decomposition of myoelectric signals

This paper describes a system for speech analysis and enhancement which combines signal processing and symbolic processing in a closely coupled manner. The system takes as input both a noisy speech signal and a symbolic description of the speech signal. The system attempts to reconstruct the original speech waveform using symbolic processing to help model the signal and to guide reconstruction. The system uses various signal processing algorithms for parameter estimation and reconstruction.

Knowledge based speech analysis and enhancement

Knowledge-based signal processing combines the symbolic manipulation facilities of artificial intelligence with the numerical methods of signal processing. This paper considers pitch detection as a problem for developing techniques for signal/symbol interaction. Specifically an overview of the design of the Pitch Detector's Assistant program is discussed, along with some strategies followed in the program. for the use of information derived from both signals and symbols.

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Knowledge-based pitch detection

Computer-aided algorithm design and rearrangement

Digital Signal Processing Group

This paper provides an interpretation of linear programming for spectral estimation [1,2] as building an MLM-like data-adaptive filter with separate weights on each pair of sensors rather than on each sensor individually. It is shown that this estimate becomes MLM when a positive semi-definiteness constraint is imposed on the matrix of weights and becomes linear programming when a non-negative response to sinusoids constraint is imposed on the matrix of weights. It is shown that, for some sensor array configurations, linear programming does indeed provide tighter bounds on the spectral power than MLM, linear programming can pick up lower level sources than MLM, and linear programming can reject more interfering sources than MLM.

C. Myers

Papers

Knowledge based speech analysis and enhancement

Knowledge-based pitch detection

Computer-aided algorithm design and rearrangement

Digital Signal Processing Group

On the use of linear programming for spectral estimation