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

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Presentation transcript:

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

Outline Introduction Preliminaries Target Location Procedure Simulation Conclusion

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

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

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

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

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

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

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

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

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

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 )

Target Localization Procedure -Detection Probability Table

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

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

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

Target Localization Procedure –Selection of Sensors to Query

Target Localization Procedure –Procedural Description

Target Localization Procedure

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

Simulation

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