Adaptive Tracking in Distributed Wireless Sensor Networks Lizhi Yang, Chuan Feng, Jerzy W. Rozenblit, Haiyan Qiao The University of Arizona Electrical and Computer Engineering Department Tucson, AZ , USA {lyang, fengc, jr, haiyanq IEEE International Symposium and Workshop on Engineering of Computer Based System ( ECBS ' 06 )

Outline  Introduction  Tracking Algorithm Design Challenges  The PaM (Predict and Mesh) Algorithm Preliminary Prediction Models The Mesh  Simulation  Conclusion

Introduction  WSN is composed of a large set of physically small, low cost, low power sensors that provide sensing and computing capabilities.  Energy consumption of the tracking algorithm has to be considered because of the inherent limitations of WSNs  Tracking algorithm is needed to adapt to real-time changes of velocities and directions of a moving target

Tracking Algorithm Design Challenges  Decentralized architecture  Random deployment  Limited power supply  Unpredictable behaviors of objects

The PaM (Predict and Mesh) Algorithm  Sensors are dropped randomly to cover a large scale region

The PaM (Predict and Mesh) Algorithm – Key idea  Using a set of prediction based activation mechanisms to extend network life time.  Using the time series trajectory of object’s path to predict the next location.  Initially, most of nodes will be in sleep status until the triggered by mobile targets.  Once the target is detected, the active nodes, not only the sensing centers but also the coordinators (SCs), will activate another set of appropriate sensors as the next SCs, previous SCs will change to the sleep status.

The PaM (Predict and Mesh) Algorithm – Preliminary Models  The Power Usage Model M= (S,N,F,K) S : WSN, N : active sensors, F : sensors in the sleep mode K : S → N ×F K be a set of events f : ∫ → I ×E ×R×T ×D f : event, ∫: single sensor, I : idle mode, E : sensing mode, T : transmitting mode, R : receiving mode, D : power down mode

The PaM (Predict and Mesh) Algorithm – Preliminary Models  p = W I ·t I +W E ·t E +B r ·W R ·t R +B t ·W T ·t T +W D ·t D W I,W E,W R,W T and W D as the power usage of a sensor under different working modes  The dynamic model of a target under tracking can be defined as x k = x k-1 + w k ∈ R n is the state vector of the target and w k is the process noise (e.g., unknown acceleration).

The PaM (Predict and Mesh) Algorithm – Preliminary Models  The power consumption matrix P can be presented by  P is a vector of which each entry represents the power usage of a single sensor  For the time interval t e – t s, the power consumed by S can be formulated as

The PaM (Predict and Mesh) Algorithm – Sensing and Communication Model An active sensor node ∫ i is able to estimate its distance D and orientation θ to an object All of the sensor nodes are equipped with GPS receivers that have knowledge of their own location

The PaM (Predict and Mesh) Algorithm – Prediction Models  V i = V o + α i × t o  t o = (r − D(M i, ∫ i ))/V i Escape period

The PaM (Predict and Mesh) Algorithm – Prediction Models  Collaborative prediction model

The PaM (Predict and Mesh) Algorithm – Prediction Models  The n-step prediction model

The PaM (Predict and Mesh) Algorithm – The Mesh The Mesh Process According to the information, it’s not possible for the target to move out of the region Mesh within the time t o + t e

Simulation  Power consumption per time unit  The quality of tracking

Simulation – The Simulation Setup  Sensing field : 1000m * 1000m  2500 sensors  Random deployment  Sensing range : 30m  Communication range : 90m

Simulation – Simulation Procedure Tracking a VehicleTracking a Person

Simulation – Simulation Procedure  Power Consumption and Tracking Quality Simulation 1 : All sensors in the idle status until an event triggers them for sensing Simulation2: PaM algorithm

Conclusions  PaM algorithm is proposed as a practical approach for the large scale applications  Using a set of two prediction models and a mesh process to monitor the movement of the objects  Making use of the PaM algorithm, the quality of tracking is preserved and the life time of the sensing system is dramatically enhanced as well  PaM algorithm is proved to be adaptive for tracking diverse kinds of objects with various movement patterns

Thank You!!