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Smart Dust eh!!!

SNoD Sensor Networks on Defense Team Members: Kaustubh Supekar Gaurav Sharma Deepti Agarwal Aditya Barve Brijraj Vaghani Seema Joshi Debashis Haldar Gautam Kaushal. Smart Dust eh!!!. The particles of dust that could be watching you .

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Smart Dust eh!!!

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  1. SNoDSensor Networks on DefenseTeam Members:Kaustubh SupekarGaurav SharmaDeepti AgarwalAditya BarveBrijraj VaghaniSeema JoshiDebashis HaldarGautam Kaushal

  2. Smart Dust eh!!! • The particles of dust that could be watching you . • Smart Dust is the brainchild of Associate Professor Kris Pister and Professor Randy H. Katz, who are currently working at the University of California, Berkeley

  3. Techie definition Smart Dust Sensor (also known as a sensor mote) is a tiny wireless micro-electromechanical sensor (MEMS) packed into a cubic millimeter speck that can detect anything from light to vibrations.

  4. Mote Constraints • Power, size and cost • These get translated to • Slow clock cycles of the micro controller. • Less memory. • Smaller number of hardware controllers.

  5. MOTE Specifications • Two Board Sandwich • CPU/Radio board • Sensor Board: temperature, light • Size • Mote: 11 in • Pocket PC: 5.23.1 in • CPU • Mote: 4 MHz, 8 bit • Pocket PC: 133 MHz, 32 bit • Memory • Mote: 512 B RAM; 8K ROM • Pocket PC: 32 MB RAM; 16 MB ROM • Radio • 900 Hz, 19.2 kbps • Bluetooth: 433.8 kbps • Lifetime (Power) • Mote: 3-65 days • Pocket PC: 8 hrs

  6. Sensor Network A collection of sensor motes that perform autonomous sensing to form the basis of a massively integrated sensor network which sends some useful information to a base station. Some examples: Environmental sensor networks to detect and monitor environmental changes. Wireless surveillance sensor networks for providing security in a shopping mall, parking garage, etc. Military sensor networks to detect enemy movements, the presence of hazardous material.

  7. Sensor Vs Ad-Hoc Networks • The number of sensor nodes in a sensor network are much more than in an ad-hoc network. • Sensors act with other sensors in a restricted vicinity. • The topology of a sensor network changes more frequently. • Sensor nodes mainly use a broadcast paradigm, whereas most ad-hoc networks are based on a point-to-point communication • Sensors are more constrained in memory and energy compared to ad-hoc networks. • Sensors interact with physical environment, they experience Task Dynamics.

  8. Our Aim • Deploy a large number of sensor motes to form a network to monitor movements of human beings in a terrain. • The sensor motes communicate and co-ordinate efficiently to establish an approximate count of the number of human beings in that terrain.

  9. Requirements • Large number of sensors which remain stationary after random deployment. • Low energy usage (Computationally light execution and minimal amount of energy transmission) • Self-organizing network • Collaborative signal processing (Data aggregation) • Security

  10. Human detection • PIR sensors Passive Infrared Sensors detect infra-red heat energy emitted by humans. Triggering occurs when they detect a change in infrared levels, as and when a warm object moves in or out of range of a sensor. They are quite resistant to false triggering.

  11. Salient Features • A localized clustering algorithm that contributes to scalability, robustness and efficient utilization of resources. • A routing algorithm that adapts itself to the dynamics of the network and changes in the clusters. • A data aggregation algorithm to fuse data from multiple sensors within a cluster. • Security features that ensure only authorized users are granted access to the network and only those messages that weren’t altered in transit are accepted.

  12. Clustering • Allows to efficiently co-ordinate local information and thus contribute to scalability, robustness and efficient utilization of resources. • Each cluster is formed during bootstrap and all the members of a cluster elect a cluster head, which performs data aggregation and routing for the cluster.

  13. After PROMOTION TIMER expires, if a sensor hasn’t received any other sensor’s advertisement declaring itself as the Cluster Head, then the sensor promotes itself as the cluster head. All sensors start advertising presence within a predefined broadcast range and start WAIT TIMER. All non-cluster heads choose their cluster head and form clusters. Cluster heads All cluster heads send advertisements to the sensors from which they received presence advertisements. By the end of the WAIT TIME all the sensors would have received advertisements from the sensors within its broadcast range. All sensors then start their PROMOTION TIMER, which is inversely proportional to the energy level of the sensor and number of advertisements it received.

  14. Routing Base station w01 w04 Level 0 w02 w03 w11 w12 Level 1 (W01*w11 + w02*w12) / 2 Only the clusters not within Level 0 listen to those messages and again calculate their own weight based on the received messages’ signal strength. These clusters are at Level 1. All the cluster heads receiving this message set themselves as Level 0 members and assign weight to themselves based on the received RF signal strength from the base station and send back acknowledgements. Level 0 cluster heads now propagate another message within a limited range for other cluster heads. Base station sends a message within a limited range so that only nearest cluster heads receive it.

  15. Communication amongst Sensors • Since all sensors within a single cluster operate at the same frequency, provisions have to be made such that the signals do not interfere with each other .We overcome this problem by using CSMA/CA. • Similarly even the cluster heads when sending information to a lower level or to the base station use the same technology

  16. Future Work • Implementation of Security Features • Data Aggregation of information from various sensors. • Fine tuning of routing and clustering algorithms. • Parallel processing of Clustering and Routing algorithms, the current version works sequentially • Simulation of the algorithms to test their validity.

  17. Applications • Location and quantity detection in a Warehouse. • Disaster detection and recovery which today by comparison is very human intensive.

  18. Q&A

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