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Approximate Algorithm
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Distributed Protocol
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Linear Program
Wireless Sensor Network
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Power efficient monitoring management in sensor networks
Power efficient monitoring management in sensor networks,10.1109/WCNC.2004.1311452,P. Berman,G. Calinescu,C. Shah,A. Zelikovsky
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Power efficient monitoring management in sensor networks
(
Citations: 58
)
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P. Berman
,
G. Calinescu
,
C. Shah
,
A. Zelikovsky
Optimizing the
energy consumption
in
wireless sensor
networks has recently become the most important performance objective. We assume the
sensor network
model in which sensors can interchange idle and active modes. Given monitoring regions, battery life and
energy consumption
rate for each sensor, we formulate the problem of maximizing
sensor network
lifetime, i.e., time during which the monitored area is (partially or fully) covered. Our contributions include (1) an efficient
data structure
to represent the monitored area with at most n2 points guaranteeing the full coverage which is superior to the previously used approach based on grid points, (2) efficient provably good centralized algorithms for sensor monitoring schedule maximizing the total lifetime including (1+ln(1q)1)approximation algorithm for the case when a qportion of the monitored area is required to cover, e.g., for the 90% area coverage our schedule guarantees to be at most 3.3 times shorter than the optimum, (4) a family of efficient distributed protocols with tradeoff between communication and monitoring power consumption, (5) extensive
experimental study
of the proposed algorithms showing significant advantage in quality, scalability and flexibility.
Conference:
Wireless Communications and Networking, IEEE Conference  WCNC
, vol. 4, pp. 23292334 Vol.4, 2004
DOI:
10.1109/WCNC.2004.1311452
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Citation Context
(43)
...In [
4
], the authors have formulated the lifetime problem and suggested another Linear Programming (LP) technique to solve this problem...
Jacques M. Bahi
,
et al.
Distributed Lifetime Optimization in Wireless Sensor Networks
...Efficient monitoring algorithms have been proposed to maximize the covered area while reducing the used energy [
12
], [13]...
...Solving the problem in static conditions is similar to the minimum set covering problem, which is NP complete [
12
], [13]...
...Arguably, the most intuitive approach is to formulate the optimization problem as an ILP equation set [
12
], [13]...
...A good cost function for minimum set covering is to always select the candidate that covers most of the uncovered elements [
12
]...
...This parameter is similar to traditional priorities in minimum set covering heuristics [
12
], which choose subsets with many uncovered elements...
Codruta Istin
,
et al.
Stochastic ModelBased Heuristics for Fast Field of View Loss Recovery...
...Area monitoring was previously studied by Berman et al. [
1
,2] and a superlinear algorithm for finding efficient schedules with a logarithmic approximation ratio was presented...
...Later in [
1
], Berman et al. argue that area monitoring can be reduced to target monitoring of O(n 2 ) targets for a class of sensor models, given a network...
...In [
1
,2], Berman et al. discuss the problem of area monitoring without enforcing constraints on the node sets or battery capacities as the previous authors did...
...We refer to this problem as sensor network lifetime problem (SNLP) [
1
]...
...input: parameter δ ∈ [0,
1
], sensor nodes S ,a reaA, sensing range R output: set of covers C, set of corresponding durations t...
...Lemma 1. Let δ ∈ [0,
1
]. Algorithm 1 yields a feasible solution to problem (S, 1+ δ) with lifetime T �S , 1+ δ �≥ f · Topt�S , 1� . The running time outside algorithm A is O(S)...
...Algorithm 2. Approximation Algorithm input: parameter δ ∈ [0,
1
] ,� ∈ (0, 1], sensor nodes S ,a reaA, sensing range R output: set of covers C, set of corresponding durations t...
...Berman et al. stated in [
1
,2] that the area monitoring problem can be reduced to a target monitoring problem for convex sensing ranges...
...Since covering an area is equal to covering a set of points [
1
,2], this problem is equal to MCGDC with costs ci = wi and C =1 . �...
Peter Sanders
,
et al.
Lifetime Maximization of Monitoring Sensor Networks
...A number of researchers [
4
,5,6,11,15] have used this or related problems as an abstraction for the problem of deriving efficient sleep schedules in wireless networks...
...researchers in the wireless networks community have either used heuristics [6], the O(log n)approximation that works for general graphs [11], or have settled for a fractional solution obtained by solving the LPrelaxation of the problem [
4
]...
Saurav Pandit
,
et al.
Approximation Algorithms for Domatic Partitions of Unit Disk Graphs
...[
3
,
9
] and others showed that using nondisjoint covers allows the lifetime to be extended further and this approach has been adopted since...
Akshaye Dhawan
,
et al.
A distributed algorithmic framework for coverage problems in wireless ...
References
(8)
Power efficient organization of wireless sensor networks
(
Citations: 418
)
Sasa Slijepcevic
,
Miodrag Potkonjak
Conference:
IEEE International Conference on Communications  ICC
, 2001
Maximizing lifetime in an energy constrained wireless sensor array using team optimization of cooperating systems
(
Citations: 21
)
Robert J. Marks II
,
Arindam K. Das
,
Mohamed ElSharkawi
Conference:
International Symposium on Neural Networks  ISNN
, vol. 1, pp. 371376, 2002
Energy Efficient Routing in Ad Hoc Disaster Recovery Networks
(
Citations: 126
)
Gil Zussman
,
Adrian Segall
Journal:
Ad Hoc Networks
, vol. 1, no. 4, pp. 405421, 2003
Multicommodity maxflow mincut theorems and their use in designing approximation algorithms
(
Citations: 343
)
Tom Leighton
,
Satish Rao
Journal:
Journal of The ACM  JACM
, vol. 46, no. 6, pp. 787832, 1999
Improved Steiner tree approximation in graphs
(
Citations: 298
)
Gabriel Robins
,
Alexander Zelikovsky
Conference:
ACMSIAM Symposium on Discrete Algorithms  SODA
, pp. 770779, 2000
Sort by:
Citations
(58)
Distributed Lifetime Optimization in Wireless Sensor Networks
Jacques M. Bahi
,
Mohammed Haddad
,
Mourad Hakem
,
Hamamache Kheddouci
Conference:
High Performance Computing and Communications  HPCC
, 2011
Stochastic ModelBased Heuristics for Fast Field of View Loss Recovery in Urban Traffic Management Through Networks of Video Cameras
Codruta Istin
,
Dan Pescaru
,
Alex Doboli
Journal:
IEEE Transactions on Intelligent Transportation Systems  TITS
, vol. 12, no. 3, pp. 895907, 2011
An energyefficient algorithm for connected target coverage problem in wireless sensor networks
M. a. Jamali
,
N. Bakhshivand
,
Mohammad Easmaeilpour
,
Davood Salami
Conference:
International Conference on Computer Science and Information Technology  ICCSIT
, 2010
Lifetime Maximization of Monitoring Sensor Networks
Peter Sanders
,
Dennis Schieferdecker
Conference:
Algorithmic Aspects of Wireless Sensor Networks  ALGOSENSORS
, pp. 134147, 2010
A heterogeneous power efficient load balancing targetmonitoring protocol for sensor networks
Samayveer Singh
,
Ajay K Sharma
Conference:
International Conference on Internet Computing  ICOMP
, 2010