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 85721-0104 , USA {lyang , fengc , jr , haiyanq }@ece.arizona.edu 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 = WI ·tI+WE ·tE+Br ·WR·tR+Bt ·WT ·tT +WD·tD WI ,WE ,WR ,WT and WD as the power usage of a sensor under different working modes The dynamic model of a target under tracking can be defined as xk = xk-1 + wk ∈ Rn 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 te – ts, 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 objectAll of the sensor nodes are equipped with GPS receivers that have knowledge of their own location

The PaM (Predict and Mesh) Algorithm – Prediction Models Vi = Vo + αi × to

to = (r − D(Mi, ∫i))/Vi 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 to + te

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 Vehicle Tracking a Person

Simulation – Simulation Procedure Power Consumption and Tracking

Quality

Simulation 1 : All sensors in the idle status until an event triggers them for sensingSimulation2: 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!!