Complex Behavior at Scale: An Experimental Study of Low-Power Wireless Sensor Networks

By Deepak Ganesan, Deborah Estrin, Alec Woo, David E. Culler, Bhaskar Krishnamachari, Stephen B. Wicker. 2002

616 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/96aa4d55a3870193b23c9c5d3b838e2be6f17c33

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Gang Zhou
2. P. Levis
3. Tian He
4. Prabal Dutta
5. J. Stankovic
6. Samuel Madden
7. Marco Zúñiga
8. Wolfgang Kiess

Citations in the corpus

• Architecture and evaluation of an unplanned 802.11b mesh network (2005, background): “One problem with the above scheme is that flooded queries often do not follow the best route [18].”
• Models and solutions for radio irregularity in wireless sensor networks (2006, methodology): “Several empirical studies [Ganesan et al. 2002][Woo et al.”
• The design of an acquisitional query processor for sensor networks (2003, background): “Real sensor network deployments show significant but not perfect correlation [17].”
• Range-free localization schemes for large scale sensor networks (2003, methodology): “For RF systems [1][15], problems such as multi-path fading, background interference, and irregular signal propagation characteristics (shown in an empirical study of this technology [10]) make range estimates inaccurate.”
• Hardware design experiences in ZebraNet (2004, methodology): “In laboratory experiments, Motes are often used to evaluate protocols and sensor network behavior [8][18][21].”
• Self-organized routing for wireless microsensor networks (2005, background): “Against this background, our goal is to maximize not just the length of time that sensors remain active, but also the active area over which the sensor network is recording data (as motivated in Section I).”
• Distributed algorithms for guiding navigation across a sensor network (2003, background): “More specifically, we build on important previous work by [16], [10], [26], [6], [27] and examine in more detail reactive sensors that can adapt to their environment by capturing a danger level map and distributing this map across the network.”
• An analysis of unreliability and asymmetry in low-power wireless links (2007, background): “It is important to observe that this negative correlation leads to some nodes being good transmitters and receivers which may create some cluster behavior, as observed in some empirical studies [1], [8].”
• Statistical model of lossy links in wireless sensor networks (2005, background): “More recently there have been many studies of significant-scale deployments in several environments [ 2 ], [8], [3], [1], [9], [10].”
• Computing aggregates for monitoring wireless sensor networks (2003, background): “Depending on those conditions, empirical studies show that heavy packet loss and link asymmetry can be quite common in wireless networks [13], [18].”
• Opportunistic routing in multi-hop wireless networks (2004, background): “11b and sensor-net test-beds show similar distributions [4, 9].”
• The Emergence of Networking Abstractions and Techniques in TinyOS (2004, background): “Traditional protocols that assume bi-directional connectivity are likely to fail in such an environment; many early multi-hop routing protocols in TinyOS correspondingly suffer from poor end-to-end packet delivery [20].”
• Coverage and connectivity issues in wireless sensor networks: A survey (2008, background): “However, empirical observations have shown that this ideal model is far from reality [20,70].”
• Understanding packet delivery performance in dense wireless sensor networks (2003, background): “[6], where packet loss is studied on a large-scale (approximately 180 motes) testbed grid on an unobstructed parking lot.”
• Analyzing the transitional region in low power wireless links (2004, background): “In [ 1 ] it is shown that the dynamics of even the simplest flooding mechanism and the topology of data gathering trees constructed in dense sensor networks can be significantly affected due to the asymmetric and occasional long-distance links caused by nodes present in the transitional region.”
• Boundary recognition in sensor networks by topological methods (2006, background): “Indeed, actual communication ranges are not circular disks and are often quite irregularly shaped [13].”