1. Wireless Distributed Sensor Networks Special Thanks to: Jasvinder Singh Hitesh Nama

2. What is a Sensor Network?? A sensor node is a small device complete with sensing, data processing, and communication components A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon or very close to it. Sensor nodes in a network work cooperatively, meaning that they can use their processing abilities to locally carry out simple computations, and then transmit only the required data.

3. What is a Sensor Network?? One big advantage for a sensor network is that the position of the sensor nodes can be random; it does not need to be predetermined. Sensor networks can be used for a myriad of applications, including: –Health –Surveillance / Reconnaissance –Disaster Relief

4. Information Distribution (Energy Efficient Routes)

5. PA = power available  = energy required ME = minimum energy MH = minimum hop Four possible approaches to choosing “energy efficient” route – Maximum PA, Minimum Energy, Minimum Hop, Maximum minimum PA node route

6. Information Distribution (Energy Efficient Routes Continued…) Maximum PA searches for the largest total (sum of) PA corresponding to a path w/out extending any routes (A-B-T vs. A-B-C-T) Minimum Energy looks for the route that consumes the least amt of energy. This is path A-B-T Minimum Hop selects the route that takes the fewest hops (route D-T) Maximum minimum PA node route selects the route where the minimum PA is larger than the minimum PA’s of the other routes (route D-T)

7. Information Distribution (Data Aggregation)

8. Based on Data-Centric routing, a process where sensing tasks are assigned to sensor nodes based on what the other nodes are interested in. D-C routing requires attribute-based phenomena, where users are interested in the occurrence of a specific phenomenon, rather than the a query of an individual node. Data aggregation is a technique used to solve implosion and overlap problems in data centric routing. –Implosion – duplicated messages are sent to the same node –Overlap – neighboring nodes receive duplicated messages because their overlapping “observation regions” sense the same stimuli at the same time.

9. Information Distribution (Data Aggregation Continued…) Data aggregation can be seen as a set of automated methods of combining the data that comes from many sensor nodes into a set of meaningful information.

10. Information Distribution (SPIN)

11. SPIN – Sensor Protocols for Information via Negotiation Energy Efficient combination of two packet sending methods –Flooding – each node receiving a data or management packet repeats it by broadcasting, unless a max number of hops for the packet is reached or the destination packet is the node itself. Requires little maintenance, but subject to implosion and overlap. –Gossiping – incoming packets are sent to a randomly selected neighbor. Avoids implosion, but takes a long time. SPIN contains 3 messages: ADV, REQ, DATA –ADV contain descriptor of DATA –If neighbor is interested, it sends a REQ for the DATA –DATA is sent

12. Information Distribution (Directed Diffusion)

13. Sink sends out interest to all sensors, containing a timestamp field and several gradient fields. The interest stored in the catch of each sensor node. –Interest – description of a task As the interest is propagated through the network, gradients from the source back to the sink are set up. When the source has data for the interest, the source sends the data along the interests gradient path.

14. Information Distribution (other methods) Sequential Assignment Routing – enables sensor nodes to discover their neighbors and establish transmission / reception schedules without a central management system (usually through a TDMA implementation). –SAR algorithm creates multiple trees where the root of each tree is a one-hop neighbor from the sink. Each node usually belongs to multiple trees. –Belonging to multiple trees allows a sensor node to select what path to route its data back to the sink. A similar protocol, LEACH (Low-Energy Adaptive Clustering Hierarchy), randomly selects sensor nodes as clusterheads, so the high energy dissipation in communicating with the base station is spread to all the sensor nodes in the network.

15. Power Consumption A wireless sensor node can only be equipped with a limited power source (it is a microelectronic device). It is important that the battery lifetime for a sensor node is long (on the order of several months) because the malfunction of even a few nodes can cause significant rerouting of packets in the sensor network

16. Power Consumption - DVS Energy consumption in a static CMOS-based processor has components – switching (independent of time) and leaking. –The switching energy is usually calculated as E switch = C tot * V supply 2 Dynamic Voltage Scaling (DVS) suggests that reducing V supply can result in energy savings, at the cost of additional propagation delay

17. Power Consumption – RF Hardware For short-range transmission at gigahertz carrier frequencies, the radio’s power is in large part used by the frequency synthesizer which generates the carrier frequency rather than the actual transmit power. Hence, data rate does not affect power consumption of the transmitter. However, as packets become shorter, the radio’s start-up time becomes significant. To reduce energy, the node’s radio module is duty cycled.

18. Power Consumption – System Partioning Algorithm implementations for a sensor network can take advantage of the network’s capability for parallel processing to reduce energy. Partitioning a computation among multiple sensor nodes and performing the computation in parallel will allow energy savings through frequency and voltage scaling.

19. Power Consumption – Software The overall energy efficiency of wireless sensor networks its heavily dependent upon the software that runs them. –General purpose processors and DSPs offer more flexibility for a sensor network than dedicated circuitry, but this results in the increasing importance of programmable solutions. –Improving control and application software can substantially reduce power consumption

20. Conclusion For more information: http://www.cdt.luth.se/babylon/snc/References/Akyil diz2002_SurveySensorNets_01024422.pdf http://www-mtl.mit.edu/~anantha/