Dynamic Sensor Networks Project Review of UCLA’s Activities

1. Dynamic Sensor Networks ProjectReview of UCLA’s Activities Mani Srivastava UCLA

2. Two Separate Projects at UCLA This Review • DSN (Subcontract from USC/ISI) • Sole PI: Mani Srivastava • Focus: networking • Low-power/low-latency link, MAC, and routing • GPS-less discovery & distribution of location • Capability and attribute based addressing and connectivity • Sensor network simulation and emulation • Protocols for GPS-synchronized communication subsystem • Sensorware (Subcontract from Rockwell Science Center) • Two PIs: Mani Srivastava, Miodrag Potkonjak • Focus: distributed middleware services • Network coverage service for sensor networks • Sensor control scripts: light-weight, mobile, platform independent, secure • Spatial addressing and communications, timing synchronization • Implementation on Rockwell’s nodes

3. This Review:Selected Recent Activities • Update on Sensorsim • GPS-less ad hoc localization • Low-latency packet forwarding • Dynamic assignment of MAC addresses • Low-power multihop routing

4. I. SensorSim Update • Simulation framework for modeling sensor networks built on top of ns-2 • sensing channel and sensor models • scenario generation tool (SensorViz) • light weight protocol stacks • hybrid simulation • battery/power model (further model development under PAC/C) • Alpha release at http://nesl.ee.ucla.edu/sensorsim/ • Selected features being migrated to official ns-2 through Deborah’s group • In use by external groups such as U. Maryland

5. User Node Sensor Node Sensor Node Sensor Node SensorSim Architecture Sensor Node Functional Model User Application User Node SensorWare Network Stack Power Model Network Layer Sensor App Battery Model MAC Layer Sensor Stack3 Network Stack Physical Layer Sensor Stack2 Sensor Layer Radio Network Layer Sensor Layer Sensor Stack1 Wireless Channel Physical Layer CPU Sensor Layer MAC Layer Physical Layer Wireless Channel Physical Layer ADC (Sensor) Physical Layer Sensor Channel3 Wireless Channel Sensor Channel2 Sensor Channel1 Sensor Channel Target Node Target Application Target Node Sensor Stack Sensor Layer Physical Layer Sensor Channel

6. Scenario Generation & Visualization SensorViz Features: • Diverse scenario generation • Node deployment patterns • Target trajectories • Sensor characteristics • Node attributes • Can be slaved to a running simulation (SensorSim) • Monitor real sensor nodes Planned: • XML output • Read in SITEX format scenarios

7. Known Location Unknown Location II. Dynamic Location Discovery • Discovery of absolute and relative location important • location attribute based naming and addressing of nodes • geographical routing • tracking of moving phenomena (targets) • GPS not enough • not work everywhere due to requirement of LOS to satellites (trees, indoors) • not on all nodes (costly, large, power-hungry) • No infrastructure in sensor networks precludes solutions based on trilateration with special high power beacons • also, susceptible to failure • Problem: given a network of sensor nodes where a few nodes know their location (e.g. through GPS) how do we calculate the location of the other nodes?

8. Ad-Hoc Localization System(AHLoS) Iterative Multilateration • Every node contributes to process • Small fraction of initial beacons • Distributed • Robust • Energy Efficient • Inter-node ranging uses • RSSI • Ultrasound • Integrated with routing messages • Location discovery almost free! • Adapts to channel conditions via a joint estimation of location & channel parameters Collaborative Multilateration

9. Distributed Pros More robust to node failure Less traffic => less power Better handling of local environment variations Speed of ultrasound Radio path loss Rapid updates upon topology changes No time synch. required Centralized Cons A route to a central point Time synchronization is required High latencies for location updates Central node requires preplanning More traffic => higher power consumption Centralized vs. Distributed Localization

10. 1 2 a 3 Basic Multilateration Residual of measured and estimated distance Linearize using Taylor Expansion Linear form MMSE Solution Repeat until δ becomes 0

11. Step 1 Step 3 Step 2 Iterative Multilateration • Basic multilateration can be applied iteratively across the network

12. Node vs. Initial Beacon Densities % Resolved Nodes Total Nodes % Initial Beacons Uniformly distributed deployment in a field 100x100. Node range = 10.

13. Challenges • Iterative multilateration may stall if • the network is very sparse • the percentage of beacons is very low • terrain obstacles • If the network is large, error will accumulate from iterative multilateration

14. 1 3 a b 2 4 Collaborative Multilateration Uses location information over multiple hops Linearize residuals over 2 types of edges: Both equations have the form Follow the same solution procedure as basic multilateration

15. Collaborative Multilateration (contd.) Execute Update Until

16. Collaborative Sub-trees Necessary conditions: • Each unknown node must have at least 3 participating neighbors • A participating node is either a beacon node or an unknown node connected to 3 participating nodes 18 equations 16 unknowns Over-determined!

17. Distributed Ad-Hoc Operation • Location estimation takes place at the scope of a neighborhood • Collaborative sub-trees can zoom in and out to • Form a well-determined system • Avoid degenerate cases • Avoid obstacles • Reduce Error Propagation • Error can be further reduced if computation takes place at a central point.

18. Platform Characterization Ultrasound TDoA RSSI in football field

19. Iterative Multilateration Accuracy 50 Nodes 10% beacons 20mm white gaussian ranging error

20. AtmelAVR RFMRadio Ultrasound Receiver INT Ultrasound Transmitter Implementation Status • Initial prototype competed: Medusa • Design of Medusa II(using non-SensIT resources) • Longer range ultrasound (15-20m) • Radio Power Control & RSSI circuitry • More computation (Atmel THUMB) • Goal: Hybrid Radio-acoustical localization • use radio for long-range when ultrasound is unable to find a neighbor • Medusa used standalone or as a location coprocessor to sensor nodes

21. MultihopPacket CommunicationSubsystem Rest of the Node GPS RadioModem MicroController CPU Traditional Approach Sensor DROP FORWARD Action ACCEPT 0.133 34.300 % of received packets 65.567 III. Low Latency Packet Forwarding • Problem: node often simply relays packets in multihop network • NS-2 simulation: 1000x1000 terrain, 30 nodes, DSR, CBR traffic from random SRC and DEST • Traditional approach: packets sent from radio to main CPU • long latency (serial bus), power hungry (main CPU woken up)

22. Our Packet Forwarding Architecture • Our approach: Embedded Packet Processor in the Radio • exploit programmable microcontrollers in the radios to handle common cases of packet routing • can also do operations such as combining of packets with redundant information • Packets are redirected as low in the protocol stack as possible • reduced latency (and, incidentally, also reduced power…) • Key challenge: how to do it so that every new routing protocol will not require a new radio firmware? MultihopPacket CommunicationSubsystem Rest of the Node GPS RadioModem MicroController CPU Sensor

23. Application-defined Routing Framework for Radio Firmware CommunicationSubsystem Packet Classifier GPS Application-DefinedMatching Rules& Actions RadioModem MicroController • Packet-classifier and packet-modifier driven by application defined matching rules and actions • Matching rules: and/or expressions using =, <, >, range operators on arbitrary packet fields (offset, length) • Actions: accept, forward, drop, field increment/decrement etc. • Rules and actions operate on arbitrary packet fields (any layer) • fields specified as (offset, length) • For complex cases packet sent to the main processor • only simple, common cases handled at the radio • Expressiveness: implemented the following as test cases • Node ID-based addressing and routing (DSR-like) • Geographical point-cast (send to a circular area specified as destination) Packet Modifier

24. Proof-of-concept Implementation • Rockwell nodes with a prototype radio • Prototype radio because Rockwell’s radio firmware is not open • RFM radio with FPSLIC (microcontroller with FPGA) • Mixed software/FPGA implementation • FPGA used to accelerate packet matching/modification

25. DCPUAC DSR DMCUFW DMCUAC Parameter DCPUFW 0.125 4.894 4.182 0.111 Value (ms) 36.532 Performance Analysis • Difference in packet DELAY between the traditional approach and our approach: • Serial port delay is the dominant factor Packet Distribution Serial port delay Delay Overhead for FWD Delay Overhead for ACCEPT Measurements Given the measurements the difference in delay is: When PrFW > 3% PrAC the traditional approach delay is more than our approach For our simulation traffic data Ddiff= 44ms

26. 1 5 0 4 2 3 2 1 IV. Dynamic MAC Address Allocation • Wireless spectrum is broadcast medium • MAC addresses are required • In wireless sensor networks, data size is small • Unique MAC address would present too much overhead • Employ spatial address reuse (similar to reuse in cellular systems) • Two aspects • Dynamic assignment algorithm • Address representation

27. 0 0 1 0 1 4 3 2 0 1 2 0 1 2 0 3 1 Distributed Assignment Algorithm • Network is operational (nodes have valid address) • Listen to periodicbroadcastsof neighboring nodes • In case of conflict, notify node (this node resends a broadcast) • Choose non-conflicting address and broadcast address in a periodic cycle. At this point the new node has joined the network. • Additive convergence: network remains operational during address selection • Mapping: unique ID to spatially reusable address • Algorithm also valid when unidirectional links

28. 0.5 0 0 0.3 Freq. of occurrence 10 1 110 2 0.1 111 3 0 1 2 3 Dav = 0.01 nodes/m2 Frequency Address Encoded Address Representation • Size of the address field? • Non-uniform address frequency • Huffman encoding • Robust: can represent any address • Practical address selection • All addresses with same codeword size are equivalent • Choose random address in that range to reduce conflict messages

29.  = 10 Dav = 0.01 nodes/m2  N = 125  N = 250  N = 1000 L All nodes Bulk nodes 2R Bulk nodes Frequency Address Network Density Parameter • Taking only bulk nodes eliminates edge effects • Virtually extends network size to infinity (so independent of L) • Suggests that only close proximity is critical • Characterization of node density • Connectivity is key • Average degree

30. Nodes/m2 Average address size (bits) Y (m) X(m) Y (m) X(m) Non-uniform Network Density

31. Frequency Address Convergence time (s) Pdrop Effect of Packet Losses ( = 10)

32. Average address size (bits)  =20 Unique address  =15  =10  =5 Number of nodes Scalability • Address assignment • Distributed algorithm with periodic localized communication • Address representation • Encoded addresses depend only on distribution Scales perfectly (neglecting edge effects) Assignment Representation

33. Scheme Address selection type Av. size (bits) Address size scalability Globally unique Manufacturing 128 + Network wide unique Deployment 14  Centr. / Distr. 4.7  Encoded dynamic Distributed 4.4 + Simulation Results Fixed size dynamic Our schemes

34. Rx-ID APP/NETW ARAP (~ARP) Link Layer Rx-Addr MAC Own Address PHY Dest Rx-Addr APP Data Tx-Addr Implementation Issues • Functionality • Dynamic address assignment • Address resolution (mapping) • Address Resolution & Assignment Protocol (ARAP) • Unique receiver ID is mapped into MAC address without being included into the packet • The own MAC address is modified by the ARAP

35. Dynamic Address Allocation: Summary • Spatial reuse of address • Dynamic assignmentalgorithm • Localized: scalability • Additive convergence: robustness • Encoded addressrepresentation • Independent of network size: scalability • Variable length addresses: robustness

36. V. Low-power Multihop Routing • ATHENA: Adaptive Transmission-power Heuristic and Energy-optimizing ad-hoc Network routing Algorithm • adapts transmission power to find power-optimal multi-hop paths. • uses alternate routes to maximizes lifetime of the network. • Recent work from Maryland • offers the same benefit: combine alternate routes with tx power control • but is not easy to implement (cost of algorithm vs. convergence) Principle in adapting tx power b a E(x) =energy to send a packet over distance x E(a) + E(b) < E(c) c

37. Constant Tx Power Case • Constant power case (for comparison) • On-demand algorithm • using request and reply messages • resemble DSR, AODV: source path carried to avoid loops • siblings not visited

38. Adaptable Tx Case • Increased # of requests and replies • if destination reached, algorithm not over • siblings have to be asked • Three main rules to prevent explosion of requests • one of them produces suboptimal routes, but simulations show the cost savings are worth it

39. Example 10 tx levels [10m - 250m] packet = 125bytes signal attenuation ~ 1/d3 Constant tx power case (level 8): 6 req, 8 replies 2.48*10-4Joules/packet Adaptablet tx power case: 8 req, 30 replies 6.638*10-5Joules/packet

40. 25-node Network replies requests

41. 25-node Network replies requests

42. Average Gains 25-node network signal attenuation: ~ 1/d3 ~ 1/d4 50-node network signal attenuation: ~ 1/d3 ~ 1/d4

43. Using Alternate Paths • Self_Energy, Next-Hop_Energy should affect path cost. • Each time the node’s energy changes 10%: • notify neighbors • recalculate best paths • Heuristic used: Remaining_Energyself-x1 Power_Costnext_hop + Remaining_Energynext_hop-x1 Power_Costdestination • Simulations show best x1 = 2 • 30% more packets routed than vanilla ATHENA • 96% of the packets routed in the optimal case.

44. Recent Accomplishment Summary • Sensorsim • GPS-less ad hoc localization • Low-latency packet forwarding • Dynamic assignment of MAC addresses • Low-power multihop routing