1. Energy-Aware Target Localization in Wireless Sensor Networks Yi Zou and Krishnendu Chakrabarty IEEE (PerCom’03) Speaker: Hsu-Jui Chang

2. Outline Introduction Preliminaries Target Location Procedure Simulation Conclusion

3. Introduction Energy is a critical resource in wireless sensor networks System lifetime needs to be prolonged through the use of energy-conscious sensing strategies

4. Introduction -Goal Reduces  Energy consumption  Communication bandwidth requirements Prolongs the lifetime of the wireless sensor network

5. Preliminaries -Assumption All nodes  Communicate with the cluster after initial deployment  Assumed to be homogeneous Cluster head  Knows the location of the sensors  Has more computation power

6. Preliminaries -Assumption The time for sending or receiving data is assumed to be the same The distance from the different nodes to the cluster head is ignored in the discussion on energy consumption The energy consumption for sensing is the same to each sensor node

7. Preliminaries -Sensor Detection Model Converts the physical sensing signals to probability-based values Evaluating the confidence level about the data collected by the sensor

8. Preliminaries -Sensor Detection Model r rere rere C xy (S i ) = 0 C xy (S i ) = e -λa β C xy (S i ) = 1 c xy (s i ): the coverage of the grid point P at (x,y) by sensor s i r: detection range r e : a measure of the uncertainty in sensor detection

9. Preliminaries -Energy Consumption Model Energy consumption  ψ s : sensing (J/s)  ψ t : transmitting (J/s)  ψ r : receiving (J/s)  E c : the energy for sensing activities  E c : sensor node for communication with the cluster head  E b : broadcasting data from the cluster head

10. Preliminaries -Energy Consumption Model K(t): the number of sensors involved in communication at the time instant t T s : the time duration that a sensor node is involved in sensing T: the time require for either retrieving data from a sensor node or the broadcasting of data from cluster head

11. Preliminaries -Sensor Detection Model T can be on of three values  T d : raw target data  T e : target event reporting  T q : query data T e ≦ T q ＜＜ T d

12. Target Localization Procedure -Detection Probability Table S xy : a set of sensors can cover a grid point P(x,y)  |S xy | = k xy p xy (s j,i)  s j detects a target at P(x,y); p xy (s j,i) = c xy (s j )  otherwise p xy (s j,i) = 1-c xy (s j )

13. Target Localization Procedure -Detection Probability Table

15. Target Localization Procedure –Score-based Ranking i(t):  The index of p_table xy at time t  Calculated from S xy and S rep,xy

16. Δk rep,xy (t)  Measures the degree of difference in the set of sensors that reported and those sensors that can detect point P(x,y) at time instant t S rep,xy (t):  the set of sensors Can detect a target at point P(x,y) Have reported the detection of an object at time t Target Localization Procedure –Score-based Ranking

19. Target Localization Procedure –Selection of Sensors to Query S q (t): a set of the sensors selected by the cluster head for querying at time t P MS : the set of grid points with the highest scores

20. Target Localization Procedure –Selection of Sensors to Query

21. Target Localization Procedure –Procedural Description

23. Target Localization Procedure

24. Simulation Using MatLab 30 by 30 sensor field grid 20 sensors randomly replaced r=5, r e =4,λ=0.5, β=0.5 ψ r =400nJ/s, ψ t =400nJ/s, ψ s =1000nJ/s T d =100ms, T e =2ms, T q =4ms

25. Simulation

28. Conclusion Described an energy-aware target localization procedure The approach is based on the combination of  Two-step communication protocol between the cluster head and the sensors in the cluster  Probability localization algorithm