Mica: A Wireless Platform for Deeply Embedded Networks

By Jason L. Hill, David E. Culler. IEEE Micro, 2002

1,292 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d7d1ba245d7397c0cead0191c08aeb0da6fb2316

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Lessons from a Sensor Network Expedition (2004, methodology): “We used the Mica mote developed by UC Berkeley [10] running the TinyOS operating system [11].”
Fine-grained activity recognition by aggregating abstract object usage (2005, background): “A theme of much of this work is that “heavyweight” sensors such as machine vision can be replaced by large numbers of tiny, robust, easily worn sensors [7, 21].”
WiseMAC: An Ultra Low Power MAC Protocol for Multi-hop Wireless Sensor Networks (2004, background): “Relatively similar performance curves are obtained with other low power transceivers, such as the RFM TR1000 and Chipcon CC1000 used on Berkeley’s mica and mica2 motes [6].”
Beacon vector routing: scalable point-to-point routing in wireless sensornets (2005, methodology): “The first (Office-Net) consists of 42 mica2dot motes [10] in an indoor office environment of approximately 20x50m while the second (Univ-Net) is a testbed of about 74 mica2dot motes deployed across multiple student offices on a single floor of UC Berkeley’s Computer Science building.”
Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks (2005, background): “, [5,23]), this line of research of applying multi-agent system technology and constraint problem-solving techniques has a paramount importance to the overall success of embedded sensor networks by addressing the key issue of resource allocation at the application level.”
Power Sources for Wireless Sensor Networks (2004, background): “The vast reduction in size and power consumption of CMOS circuitry has led to a large research effort based around the vision of ubiquitous networks of wireless sensor and communication nodes [1-3].”
Taming the underlying challenges of reliable multihop routing in sensor networks (2003, background): “To ground the hardware and physical configuration factors, we focus on large sensor fields of Berkeley TinyOS motes [13] spread roughly as a uniform grid over a large, essentially unobstructed, indoor space.”
Geographic Routing Using Hyperbolic Space (2007, methodology): “used node, the Berkeley mote [12], has no GPS antenna.”
A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems (2019, neutral): “2 for its node [72].”
An adaptive energy-efficient MAC protocol for wireless sensor networks (2003, background): “The TinyOS approach, briefly discussed in [3], is basically an optimization at the physical layer and may be applied in combination with link-layer (MAC) solutions discussed next.”
CODA: congestion detection and avoidance in sensor networks (2003, background): “Such a multimodal congestion control scheme provides the foundation for designing efficient and low cost control that can be practically implemented in sensor networks based on the Berkeley motes series [5] [4], as discussed in Section 5.”
Temporal properties of low power wireless links: modeling and implications on multi-hop routing (2005, methodology): “A total of 55 Mica 1 mote [10] nodes, which uses the RFM TR1000 radio chip [8], were placed in a grid structure in the environment at approximately 1m distances.”
Sensor network-based countersniper system (2004, methodology): “The hardware layer is built upon the widely used Mica mote platform, developed by UC Berkeley [10].”