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Identification of Strong Parameters Towards Optimal Deployment of Wireless Sensor Networks

Identification of Strong Parameters Towards Optimal Deployment of Wireless Sensor Networks. David M Bell * , Rahul Ghosh + , Xiong Jie + *Nicholas School of the Environment and Earth Sciences + Dept. of Electrical & Computer Engineering. Duke University. Outline of Project. Introduction

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Identification of Strong Parameters Towards Optimal Deployment of Wireless Sensor Networks

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  1. Identification of Strong Parameters Towards Optimal Deployment of Wireless Sensor Networks David M Bell*, Rahul Ghosh+, Xiong Jie+ *Nicholas School of the Environment and Earth Sciences +Dept. of Electrical & Computer Engineering Duke University

  2. Outline of Project • Introduction • Related Works • Parameter Identification – Why? • Problem Statement • Battery Model • Proposed Algorithm • Simulation Results • Conclusions Sensor Networks for Environmental Monitoring November 27, 2007

  3. Introduction • Main goal is to gather maximum information at minimum cost • Different nodes spend their energy differently • Need for an energy efficient schedule • Place the nodes in such a manner so that they will be able to capture different events and their transitions Sensor Networks for Environmental Monitoring November 27, 2007

  4. Related Works Ganesan et. al in IPSN ’04 ~ • joint optimization of node placement and transmission structure Giridhar et. al in IPSN ’05 ~ • described the functional lifetime of a wireless sensor network Sensor Networks for Environmental Monitoring November 27, 2007

  5. Related Works Chen et. al in MILCOM ’05 ~ • tried to maximize the utilization efficiency Wu et. al in ICPADS ’06 ~ • proposed an optimal load balance solution Sensor Networks for Environmental Monitoring November 27, 2007

  6. Parameter Identification – Why? • Definition of ‘Optimal’ deployment varies, we need parameters to characterize them • Even careful ‘Optimal’ deployment may turn into sub-optimal as time increases • Identification of correct parameters for correct set of deployment can achieve high reliability in data acquisition Sensor Networks for Environmental Monitoring November 27, 2007

  7. Problem Statement • two different groups: sensing nodes and relay nodes • find a model of battery drainage which can fit such an ecological setting • deploy the nodes in such a congenial manner • it removes any possibility of generating critical node Sensor Networks for Environmental Monitoring November 27, 2007

  8. Proposed algorithms Purpose: • balance the battery life • reduce vulnerability due to critical nodes battery failure Basic algorithm: -group the sensor nodes as clusters -leader selected as the router to route all the data packets -leader is selected randomly and periodically Sensor Networks for Environmental Monitoring November 27, 2007

  9. Our improvement Improvement 1: local improvement -Different nodes have different working load -Leader period inverse proportional to the working load (This information can be obtained from the battery model) Sensor Networks for Environmental Monitoring November 27, 2007

  10. Our improvement Improvement 2: global improvement overall transmission load of clusters near the sink is heavier than those further away. Solution 1: place more nodes near the gateway Solution 2: make the cluster size bigger (more nodes) near the sink Sensor Networks for Environmental Monitoring November 27, 2007

  11. Simulator: QualNet Sensor Networks for Environmental Monitoring November 27, 2007

  12. Battery usage with static routing Number of packets transmitted Node ID Sensor Networks for Environmental Monitoring November 27, 2007

  13. usage in our algorithm Number of packets transmitted Cluster Node ID Sensor Networks for Environmental Monitoring November 27, 2007

  14. Model Formulation data process priors Sensor Networks for Environmental Monitoring November 27, 2007

  15. Parameter Estimation Sensor Networks for Environmental Monitoring November 27, 2007

  16. Parameter Estimation Sensor Networks for Environmental Monitoring November 27, 2007

  17. Individual Node Effects Sensor Networks for Environmental Monitoring November 27, 2007

  18. Prediction Accuracy Sensor Networks for Environmental Monitoring November 27, 2007

  19. Conclusions • Model of battery drainage helps us predict the decay independent of deployment settings • Predicted voltages are good, but variability is high • β0 most important, β2 & β3 for some nodes • Clustering algorithm can be easily integrated with this for practical scenario Sensor Networks for Environmental Monitoring November 27, 2007

  20. Thanks ! Sensor Networks for Environmental Monitoring November 27, 2007

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