ACM Home Page
Please provide us with feedback. Feedback
Call and response: experiments in sampling the environment
Full text PdfPdf (1.38 MB)
Source Conference On Embedded Networked Sensor Systems archive
Proceedings of the 2nd international conference on Embedded networked sensor systems table of contents
Baltimore, MD, USA
SESSION: Systems 1 table of contents
Pages: 25 - 38  
Year of Publication: 2004
ISBN:1-58113-879-2
Authors
Maxim A. Batalin  University of California, Los Angeles and University of Southern California
Mohammad Rahimi  University of California, Los Angeles
Yan Yu  University of California, Los Angeles
Duo Liu  University of California, Los Angeles
Aman Kansal  University of California, Los Angeles
Gaurav S. Sukhatme  University of California, Los Angeles and University of Southern California
William J. Kaiser  University of California, Los Angeles
Mark Hansen  University of California, Los Angeles
Gregory J. Pottie  University of California, Los Angeles
Mani Srivastava  University of California, Los Angeles
Deborah Estrin  University of California, Los Angeles
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
ACM: Association for Computing Machinery
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 13,   Downloads (12 Months): 84,   Citation Count: 21
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1031495.1031499
What is a DOI?

ABSTRACT

Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m2 over transects exceeding 1000 m2. Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. This paper describes a new approach where the deployment of a combination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling densityo ver large transects to meet a general set of environmental mapping demands.

To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variabilityin environmental phenomena discovered bythe mobile sensors and to discrete events discovered byst atic sensors. We begin byde scribing the class of important driving applications, the statistical foundations for this new approach, and task allocation. We then describe our experimental implementation of adaptive, event aware, exploration algorithms, which exploit our wireless, articulated sensors operating with deterministic motion over large areas. Results of experimental measurements and the relationship among sampling methods, event arrival rate, and sampling performance are presented.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
D. Estrin, G.J. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks," in ICASSP 2001, Salt Lake City, USA, 2001.
 
2
C. M. P. Ozanne, D. Anhuf, S. L. Boulter, M. Keller, R. L. Kitching, C. Korner, F. C. Meinzer, and A. W. Mitchell, "Biodiversityme ets the atmosphere: A global view of forest canopies," Science, vol. 301, pp. 183--186, 2003.
 
3
Radiation and Light Measurements, chapter 6, pp. 97--116, Physiological Ecology - Field Methods and Instrumentation. Chapman & Hall, London, U.K.
 
4
James San Jacinto Mountain Reserve, http://www.jamesreserve.edu.
 
5
R. Nowak, U. Mitra, and R. Willett, "Estimating inhomogeneous .elds using wireless sensor networks," IEEE Journal on Selected Areas in Communications, 2004.
 
6
J. Kiefer, "Optimal experimental design," in J. Roy. Statist. Soc., Ser. B, 21, 272--319, 1959.
 
7
V. V. Fedorov, Theory of Optimal Experiments, New York: Academic Press, 1972.
 
8
S. D. Silvey, Optimal Design, London: Chapman & Hall, 1980.
 
9
R. Pukelsheim, Optimal Design of Experiments, New York: Wiley, 1993.
 
10
H Müller, "Optimal designs for nonparametric kernel regression," in Statistics and Probability Letter, 1984.
 
11
M-Y Cheng, P. Hall, and M. Titterington, "Optimal design for curve estimation byl ocal linear smoothing," in Bernoulli 4(1), 3-14, 1998.
 
12
J. Farawayan d D. Park, "Sequential design for response curve estimation," 1998, pp. 9, 155--164.
 
13
 
14
 
15
B. Gerkey, "On multi-robot task allocation," Tech. Rep. Technical Report CRES-03-012, University of Southern California, 2003.
 
16
 
17
 
18
M. A. Batalin and G. S. Sukhatme, "Using a sensor network for distributed multi-robot task allocation," in Proc. of IEEE International Conference on Robotics and Automation (ICRA'04), New Orleans, USA, 2004, pp. 158--164.
 
19
B. Gerkeyan d M. J. Mataric, "Multi-robot task allocation: Analyzing the complexity and optimality of keyarc hitectures," in Proc. of the IEEE International Conference on Robotics an Automation (ICRA03), Taipei, Taiwan, 2003, pp. 3862--3868.
 
20
L. E. Parker, "Alliance: An architecture for fault-tolerant multi-robot cooperation.," in IEEE Transactions on Robotics and Automation, 1998, vol. 14, pp. 220--240.
 
21
S. Botelho and R. Alami, "M+: a scheme for multi-robot cooperation through negotiated task allocation and achievement.," in Proc. of IEEE International Conferenceon Robotics and Automation (ICRA), 2000, pp. 293--298.
 
22
B. P. Gerkeyan d M. J. Mataric, "Sold!: Auction methods for multi-robot coordination.," in IEEE Transactions on Robotics and Automation, 2002, vol. 18, pp. 758--768.
 
23
E. H. Ostergard, M. J. Mataric, and G. S. Sukhatme, "Distributed multi-robot task allocation for emergency handling.," in Proc. of the IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2001, pp. 821--826.
 
24
L. Girod, J. Elson, A. Cerpa, T. Stathopoulos, N. Ramanathan, and D. Estrin, "Emstar: a software environment for developing and deploying wireless sensor networks," in to appear in Proceedings of USENIX 04.
 
25
M. Rahimi, R. Pon, W. J. Kaiser, G. S. Sukhatme, D. Estrin, and M. Srivastava, "Adaptive sampling for environmental robotics," in IEEE Int. Conf. on Robotics and Automation, ICRA, New Orleans, LA, 2004.

CITED BY  21

Collaborative Colleagues:
Maxim A. Batalin: colleagues
Mohammad Rahimi: colleagues
Yan Yu: colleagues
Duo Liu: colleagues
Aman Kansal: colleagues
Gaurav S. Sukhatme: colleagues
William J. Kaiser: colleagues
Mark Hansen: colleagues
Gregory J. Pottie: colleagues
Mani Srivastava: colleagues
Deborah Estrin: colleagues