Co-Grid: an Efficient Coverage Maintenance Protocol for Distributed Sensor Networks Guoliang Xing; Chenyang Lu; Robert Pless; Joseph A. O ’ Sullivan Department of Computer Science and Engineering Washington University IPSN 2004

Outline Introduction Introduction Problem formulation Problem formulation Coverage maintenance protocol Coverage maintenance protocol Central Central Se-Grid Se-Grid Co-Grid Co-Grid Simulation Simulation Conclustion Conclustion

Introduction Coverage maintenance protocol provides required sensing coverage over a geographic region activate a subset of nodes, schedule the others to sleep, to conserve energy Coverage maintenance based on a probabilistic distributed detection model that allows efficient data fusion from multiple nodes

Introduction The need to reduce the number of active nodes and the network coverage configuration time can conflict with each other

Problem formulation Define coverage of a sensor network based on a probabilistic detection model A point p is covered by sensor network if the probability that a target, located at p, is detected by the active nodes is above threshold and the system false alarm rate is below threshold A geographic region is covered by a sensor network if all the points in this region are covered The coverage requirement of a region A :detection probability of a target located at (x, y) :system false alarm rate :detection probability threshold :detection probability threshold :system false alarm rate threshold :system false alarm rate threshold

Coverage Maintenance Protocols Centralized coverage maintenance protocol (central) Centralized coverage maintenance protocol (central) Coverage maintenance protocol based on separate grids (Se-Grid) Coverage maintenance protocol based on separate grids (Se-Grid) Coverage maintenance protocol with inter-grid coordination (Co-Grid) Coverage maintenance protocol with inter-grid coordination (Co-Grid)

Central One node is elected among all nodes in A to serve as the fusion center Fusion center decides which nodes should remain active Fusion center finds the location p in region A that has the minimal detection probability If is less than, the fusion center finds the node closest p among all sleeping nodes and marks it as active This process repeats until the minimal detection probability in region A is greater than

Re-Compute PDmin Re-Compute → PDmin > SUCCESS Find location with PDmin Example Fusion center Active member Sleeping member Location with Minimal Detection probability PDmin< Activate PDmin< Activate PDmin< Activate

Se-Grid fusion center Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Run Central algorithm Partition target area

Se-Grid -Drawback- A node cannot contribute to the decision fusion of a neighboring fusion group even if it is close to the grid's boundary The nodes close to grid boundary are more likely to be activated Se-Grid may activate redundant nodes on both sides of a grid boundary

Co-Grid G :Grid S :Sub-Grid Overlapping

Co-Grid

Example Fusion center A C B D Active node Sleeping node S1 S2 S3 S4 S5 S6 S7 S8 S9 Find Location with PDmin PDmin< Activate Broadcast active node update msg Re-compute

Example Fusion center A C B D Active node Sleeping node S1 S2 S3 S4 S5 S6 S7 S8 S9 Find Location with PDmin PDmin< Activate Broadcast active node update msg Re-compute Re-Compute → PDmin > Re-Compute → PDmin > Re-Compute → PDmin > Re-Compute → PDmin > FINISH

Degree of parallel configuration Effective computation the computation in a fusion center that will lead to the addition of a new active node Any two adjacent fusion centers cannot perform effective computation at the same time DPC (Degree of parallel configuration) DPC (Degree of parallel configuration) the total number of fusion centers that can perform effective computations simultaneously in the whole network

Degree of parallel configuration the maximal number of grids that do not overlap in the region The cardinality of the maximal independent sets

Simulation Matlab Matlab 2000 nodes randomly deployed 2000 nodes randomly deployed System detection probability : 90% System detection probability : 90% Random approach Random approach Random works similarly to Se-Grid Fusion center always randomly activates a new node in each iteration until the desired detection probability is achieved

Simulation - Simulation -Number of Active Sensors-

Simulation

Simulation - Simulation -Coverage configuration time-

Conclusion Co-Grid not only competes well against the centralized protocol in terms of the number of active nodes, but also consistently outperforms the protocol based on separate grids in terms of the configuration time