Katsuma, R., Murata, Y., Shibata, N., Yasumoto, K., Ito, M.: "A Decentralized Method for Maximizing k-coverage Lifetime in WSNs," Proc. of The Sixth International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2012), pp.16-23, May.23, 2012. http://ito-lab.naist.jp/mediawiki/images/1/17/Icmu2012.pdf In this paper, we propose a decentralized method for maximizing lifetime of data collection wireless sensor networks (WSNs) by making minimal number of nodes operate and putting other nodes in sleep. We divide a target field into multiple grids and make nodes in each grid locally achieve k-coverage. We can reduce energy consumption of WSN by minimizing the number of active nodes required for kcoverage. However, coverage degree is likely to go to excess beyond k near border between grids when deciding active nodes in each grid independently. To solve this problem, our method decides the minimal set of active nodes for adjoined grids at different times so that k-coverage of a grid is achieved taking into account the coverage in its neighboring grids. Through computer simulations, we confirmed that the proposed method achieved distribution of WSN processing with a small decrease of k-coverage lifetime compared to the centralized algorithm.

1. A Decentralized Method for Maximizing
k-coverage Lifetime in WSNs
Ryo Katsuma*, Yoshihiro Murata**, Naoki Shibata†,
Keiichi Yasumoto‡, Minoru Ito‡
* Osaka Prefecture University, ** Hiroshima City University,
† Shiga University, ‡ Nara Institute of Science and Technology
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2. Overview of Our Study
 Goal
 To maximize lifetime of wireless sensor networks (WSNs)
 Approach
 Sleep scheduling for each node by decentralized algorithm
 Divide the field into grids and choose leader node for each grid
 Periodically make each leader node calculate the minimal set of
active nodes in its grid
 Periodically change the leader for each grid
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3. Outline
1. Outline of our study
2. Research background
3. Related work
4. Proposed method
5. Evaluation
6. Conclusion
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4. Target WSNs
 WSNs for Data Collection
 Many small sensor nodes are deployed in the field
 Sensor nodes periodically sense environmental information
 Nodes send data to sink node by multi-hop communication
21℃ 21℃
20℃ Example of sensor node
20℃ 22℃
18℃
21℃
23℃
sink 21℃
20℃ MICA mote
Target field
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5. Two Big Problems
 Problem to maximize lifetime
 WSNs are expected to operate for a long time
 Sleep scheduling for each node is required
 Activating minimum number of nodes required for WSN
operation Sensor node
 Other nodes sleep in order to save energy
 Nodes are activated in turn
 k-coverage problem
 A sensor node covers a circular area for sensing 2-covered
 k-covering the entire field by active sensors
 Any point in the field is covered by at least k sensors
 Adjust k to monitor the area with required accuracy
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6. Challenge
 Maximizing k-coverage lifetime
 Lifetime is the time while the entire field is k-covered
 Battery energy is consumed in each node
 optimal set of active nodes changes
 According to remaining battery energy
 We need periodical reclculation
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7. Outline
1. Outline of our study
2. Research background
3. Related work
4. Proposed method
5. Evaluation
6. Conclusion
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8. Our Previous Work (1/3)
 Sleep scheduling method
 Sufficient number of nodes are deployed
 Deciding minimal set of active nodes for k-covering the field
 When battery is exhausted for some node
 Recalculate a set of active nodes
sink
activate
battery
exhausted
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9. Our Previous Work (2/3)
 Sequential activation algorithm
 For deciding minimal set of active nodes
 Activate a node with the largest contribution area one after
another until the field is k-covered
 Contribution area
 The area newly covered when the node is activated
e.g. If A is active, contribution area of D is larger than C
D B
D Active node
E A A
C C
field
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10. Our Previous Work (3/3)
 Centralized calculation by sink node
 Collects sensor node information
 Remaining energy
 Position
 Calculates the minimal set of active nodes
 Sends the result to every node
 WSNs with a large number of nodes
 Long calculation time
 High overhead for sending result
We propose a decentralized method
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11. Outline
1. Outline of our study
2. Research background
3. Related work
4. Proposed method
5. Evaluation
6. Conclusion
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12. Assumptions
 Deployed nodes
 Enough number of sensor nodes for k-coverage
 Only one sink node
rc
 Sensor nodes rs
 Limited battery energy
 Sensing range radius is rs
 Maximum communicable range radius is rc ( rs < rc )
 Sensor node can save battery by sleeping and waking
up by timer
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13. Initial Configuration
 Dividing field into grids
 k-covering entire field by k-covering each grid
 Side length of grid should be shorter than
 To guarantee that a node can communicate
every node in the surrounding 8 grids
rc
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14. Leader Node Selection
 Selecting leader node for each grid
 Calculate minimal set of active nodes for k-covering its
grid by sequential activation algorithm
 Collect all sensing data in its grid and send to sink
 Periodically selects the node with highest remaining
battery as the leader node
 A set of active nodes is calculated after the leader node
is selected
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15. Excessive Coverage Problem
 If active nodes are independently selected in each grid
 Number of coverage exceeds k near the grid border
A D G J M P
K N Q
B E H
O R
C F I L
Grid Ci Grid Cj
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16. Excessive Coverage Problem
 If active nodes are independently selected in each grid
 Number of coverage exceeds k near the grid border
A D G J M P
K N Q
B E H
O R
C F I L
Grid Ci Grid Cj
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17. Excessive Coverage Problem
 If active nodes are independently selected in each grid
 Number of coverage exceeds k near the grid border
Excessively covered
A D G J M P
K N Q
B E H
O R
C F I L
Grid Ci Grid Cj
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18. Our Idea
 Deciding the minimal set of active nodes for adjoined
grids in multiple steps
 Considering the area already covered by the nodes in the
adjoined gridsCoverage information
Q. How many steps
A D G J M P do we need to
complete the entire
N Q calculation?
K
B E H
O R
C F I L A. We need only 2
steps
Grid Ci Grid Cj
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19. Bi-coloring
 Classifying all grids into two
groups
 White and black, like a checkerboard
 Any two adjoining grids have different ② ① ② ① ②
colors
① ② ① ② ①
 Active nodes are chosen in white grids
 Computation in black grids is performed ② ② ②
① ①
after computation in neighboring white
grids ① ② ① ② ①
 Prevent excessive coverage
② ① ② ① ②
 Complete computation in 2 steps
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20. Data Collection
 Each sensor node sends sensed data to leader node
 Leader nodes send the data to the sink
 Constructing DAG
 Connecting other leader nodes that are closer to the sink
 Sending data to the node with the largest remaining energy
Leader
node
sink
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21. Outline
1. Outline of our study
2. Research background
3. Related work
4. Proposed method
5. Evaluation
6. Conclusion
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22. Simulation
 Evaluate k-coverage lifetime by the proposed method
 Compared proposed method with centralized method
 WSN parameters
 Field size: 50[m] × 50[m]
 Number of nodes: 600－1000, random deployment
 Requested coverage: k = 1 and 3
 Sensing frequency: 0.1[Hz]
 Recalculation interval: 1000[s]
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23. Result
 k-coverage lifetime
 Proposed method is only 14% less than centralized method
 Calculation time (1000 nodes)
 Proposed method: 0.1 second, centralized method: 1.2 second
1-coverage 3-coverage
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24. Conclusion
 Target problem
 Maximizing k-coverage lifetime by sleep scheduling
 Proposed method
 Dividing the field into two-colored grids like a
checkerboard
 Deciding the minimal set of active nodes taking into
account the coverage already decided by neighbor grids
 Simulation result
 k-coverage lifetime is only 14% less than centralized
method
 Shorter computation time
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