Communication Protocol Design for Sensor Networks
Advances in embedded system technologies motivate the deployment
of sensor networks which consist of a large number of sensor
nodes scattered over a spacious area. Each sensor node has a
processor, memory, and a short-range radio communication facility.
These distributed sensing systems enable remote monitoring and
event detection in a geographically large region or an
inhospitable area. For example, in an explosion area rescuers
equipped with handheld devices can be notified of the nearest
survivor's location detected by sensor nodes thrown over the area.
Sensor nodes are scattered in a physically spacious area and
accordingly powered by batteries instead of being tethered to
durable power sources. Generally nodes are assumed to be revoked
rather than replenished when they exhaust all the battery power.
Previous empirical studies show that the larger portion of power
is consumed by communication between nodes. Therefore, in order
to expand overall system lifetime, it is crucial to design
energy-efficient communication protocols for sensor networks.

Figure 1. Data dissemination services in sensor networks: a two-tiered
example.
Currently in this work, we focus on investigating data dissemination
in a two-tiered network which is comprised of stationary sensor nodes
and mobile data users as shown in Figure 1. For example, in an emergency rescue,
rescuers might need to monitor a specific area
that they are supposed to search, while approaching that area.
Desired data updates would be periodic to keep data fresh, and an
area of interest might overlap with another. Such data
dissemination applications suggest protocol design criteria like
the following:
- Immediate deployment: The protocol should be designed not to
require a long-term startup (e.g., network topology construction)
after sensor node placement, to get ready for the actual sensor
data dissemination.
- Adaptability: The protocol should be scalable to both the
number of data sources and the data sink populations, and allow
the diversity of user requests in terms of desired update rates
and service durations.
- Fast response to data requests: It is desirable that users
do not experience a substantial amount of delay after they
request sensor data updates.
- Energy-efficiency: Given data update demands, the protocol
should be able to satisfy them with lower energy dissipation and
ultimately extend the network lifetime.
Members
Collaborators
The members of this work have been collaborating with people
at University of Virginia including:
Publications
- Sooyeon Kim, Sang H. Son, John A. Stankovic, Shuoqi Li, and Yanghee Choi,
"SAFE: A Data Dissemination Protocol for Periodic Updates in Sensor Networks,"
accepted by Data Distribution for Real-Time Systems (DDRTS),
Providence, RI, U.S.A., May 2003.
- Sooyeon Kim, Sang H. Son, John A. Stankovic, and Yanghee Choi,
"SAFE+: An Energy-efficient Data Dissemination Protocol with Hop-by-hop data aggregation,"
in preparation, 2003.
Related links
- Real-Time Computing Laboratory, University of Virginia
- TinyOS, UC Berkeley
- Sensor Webs, UC Berkeley
- Scalable Coordination Architectures for Deeply Distributed Systems, UCLA and Information Science Institute
- CENS: Center for Embedded Networked Sensing
- Dynamic Declarative Networks, MIT Lincoln Lab
- Networks and Mobile Systems, MIT
- Amorphous Computing, MIT
- Co-Sense, Xerox Parc
- Self Organizing Sensor Networks, Auburn University
- Active Sensor Networks, Columbia University
- Cougar: Flexible Decision Support in Device-Saturated Environments, Cornell University
- Multi-resolution Data Fusion, Duke University
- Distributed Services for Microsensor Networks, Rockwell Center
- Webdust, Rutgers University
- Reactive Sensor Networks, Pennsylvania State University
- Planning Real-Time Negotiation, Honeywell
- Scalable Real-Time Negotiation Toolkit, University of Massachusetts at Amherst
- Terminodes, Swiss EPFL
- Unidirectional Link Routing, INRIA, France
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