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Sensor Nets workshop in 1978 (Proceedings of the Distributed Sensor Nets Workshop.
Pittsburgh 1978). The key components included acoustic sensors, a focus on devel-
oping new self-location algorithms for distributed networking, and development of
new protocols for digital communication among sensors. Since research in artificial
intelligence (AI) was also supported by DARPA, the workshop included talks on the
use of AI in signal processing. Furthermore, it focused on various distributed problem-
solving techniques. During the same time, researchers at Carnegie Mellon University
(CMU) developed an operating system kernel called Accent (Rashid and Robertson
1981). Accent provided transparent and flexible access to distributed resources, which
was a necessity for a fault-tolerant DSN. Later, Accent evolved into one of the most
popular operating systems, known as Mach (Rashid et al. 1989). A practical example
of DSN was a helicopter tracking system developed at the Massachusetts Institute of
Technology (MIT) (Myers et al. 1984). This system used a distributed array of acoustic
microphones along with knowledge-based signal-processing techniques to track heli-
copters. However, researchers soon realized that tracking multiple targets in a distrib-
uted environment was a greater challenge than a centralized architecture. Although
researchers in the 1980s and early 1990s had a vision of the future of WSN, the minia-
turization technology was not there to support their goals. Sensors at that time were
quite large, which presented an impediment to practical implementation.
The 1980s saw the emergence of multiple-hypothesis tracking algorithms that were
decomposed for distributed implementations (Chong et al. 1990). These algorithms
were basically designed to target strenuous situations involving high target density
and false alarms. Although radar and national power grid networks have existed for
decades, the term “wireless sensor network” came into vogue in the late 1990s with
the advent of microelectromechanical systems (MEMS) (Pierret 1990; Senturia 2001).
MEMS have been the driving technology in manufacturing tiny, low-cost, and low-
powered sensor nodes. This new paradigm of research in WSN has attracted a lot of
attention and has paved the way to a new era of network research in a highly dynamic
and ad-hoc environment. It has also broadened the scope of various civilian and mili-
tary WSN applications. For example, body sensor networks, vehicular sensor networks,
and many other such applications that require tiny sensors with a limited sensing range
and processing power have emanated from the introduction of MEMS. In this new era
of WSN research, DARPA initiated a new research program called SensIT, which facil-
itated a new environment for new algorithms in an ad-hoc, decentralized networking
paradigm (Kumar and Shepherd 2001). During this time, the IEEE saw the potential
of WSN in diverse fields and established the IEEE 802.1 WPAN Task Group, which
produced IEEE Standard 802.15.4-2003: Wireless Medium Access Control (MAC) and
Physical Layer (PHY) Specifications. This standard defines the characteristics of the
data-link and physical layer for low-data-rate wireless personal area networks. The
advantages of such networks include short-range communication with higher prob-
ability of reliable data transmission and extreme low cost in deployment with minimal
human supervision. Based on IEEE Std. 802.15.4, a group of companies formed an alli-
ance called the ZigBee Alliance (www.zigbee.org) to produce standards for low-power
wireless networking. This new technology is gaining widespread acceptance in many
different industrial and governmental organizations. ZigBee reuses the data-link and
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