An Energy-Efficient Approach for Real-Time Tracking of Moving Objects in Multi-Level Sensor Networks

Vincent S. Tseng, Eric H. C. Lu, & Kawuu W. Lin Institute of Computer Science and Information Engineering National Cheng Kung University Tainan, Taiwan, R. 0. C. Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05)

Outline Introduction Assumption Proposed Method Simulation Conclusions

Introduction (motive) In Object Tracking Sensor Networks

Power consumption affects the lifetime capture a missing object back in real- time

Energy efficiency and timeliness are the two important issues No work that considers both of real-time and energy efficient issues in OTSNs simultaneously

Introduction (solution) Propose a new approach

Efficient and real-time tracking of the moving objects By mining the movement log Cluster the sensors Use the multi-level structure

Predicting the next locations of moving objects in OTSNs First proposed a data mining method to discover the temporal movement patterns

"Mining Temporal Movement patterns in Object Tracking Sensor Networks" First International Workshop on Ubiquitous Data Management, Tokyo, Japan, April, 2005

Assumption The sensors are distributed randomly The communication routing between sensors has been worked out The movement history of the moving object can be obtained from the OTSNs A server sensor in each sensor cluster

server sensor can communicate with all the sensors within the region

Proposed Method Approach consists of three phases

Clustering of sensor nodes Discovery of movement rules Prediction and recovery of moving objects

definition Sequential path

A sequence of sensors that were visited in time order by an object between its entering and leaving

movement dataset The collection of movement paths generating from moving objects

Clustering of sensor nodes clustering mechanism

K-means algorithm The goal is to divide the objects into K

clusters K sensor nodes as initial centers Each node is assigned to its closest center The center of each cluster is re-calculated Until no change for the centers.

Clustering of sensor nodes Multi-Level Clustering of Sensor Nodes

To construct the hierarchical structure Two import parameters

Fun-out To model the branch of the hierarchical structure

Height the depth of the hierarchical structure

Clustering of sensor nodes

Discovery of movement rules Mining of Movement Patterns

Two kinds of movement patterns Sensor to sensor

ex. Object moves from node a to node b Sensor to region

ex. Object moves from node a to R11 The frequency of the inference rule

Used to evaluate the confidence of the rule The highest frequent one serves as the

basis of the prediction

Prediction and recovery of moving objects The movement rules

To predict the next location for a moving object in the sensor networks

Activate the least number of sensors Recovering

To capture back the missing object Extend the scope of the region for sensor activation

An Illustrative Example Movement log of object

Mining

The movement rule

An Illustrative Example Level 0 represents the prediction of sensor-to-sensor

Level 1 and Level 2 demonstrate the frequency of two levelsLevel 3 indicates the worst case that all sensors are activated

SimulationAverage Search Time (AST)the average time required to recover the missing moving objectAverage Energy Consumption (AEC)the average energy consumption that is required to recover the missed moving objectMiss Rate (MR)the rate that the search time required to recover the missing object exceeds the predefine deadline threshold

Simulation Impact of the number of sensor nodes

Simulation Impact of deadline threshold

Simulation Impact of the number of movement log

Simulation Impact of Fan-out and Height

Conclusions

Proposed a prediction model based on multilevel architecture and clustering algorithms for tracking the objects in OTSNs

Future work Consider multiple moving objects Consider many other factors

Ex. representative of generated data