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Reconfiguration in Sensor Networks PhD Defense Karthik K Dantu Committee Gaurav Sukhatme (Chair) Ramesh Govindan Bhaskar Krishnamachari (Outside member)

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Presentation on theme: "Reconfiguration in Sensor Networks PhD Defense Karthik K Dantu Committee Gaurav Sukhatme (Chair) Ramesh Govindan Bhaskar Krishnamachari (Outside member)"— Presentation transcript:

1 Reconfiguration in Sensor Networks PhD Defense Karthik K Dantu Committee Gaurav Sukhatme (Chair) Ramesh Govindan Bhaskar Krishnamachari (Outside member)

2 2/82 Sensor Network Deployments Marine MonitoringForest Canopy Structural Health Monitoring

3 3/82 Challenges Energy Battery-operated Lack of continuous energy supply Long deployments Unpredictability Dynamic network conditions Remote deployments Sporadic events – spatially and temporally Sensor networks need to be adaptable!

4 4/82 Thesis Contribution Adaptability by reconfiguration Functional (static) Spatial (mobile) Node Network Per-process energy accounting Robust connectivity in robot networks Service reconfiguration in tiered sensor networks Route stability using cues in robot networks

5 5/82 Thesis Contribution Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network * Post-Qual * Pre-Qual Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues

6 6/82 Outline Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues

7 7/82 Accurate Per-Process Energy Accounting Energy is a vital resource Modern embedded systems are highly multi-tasking running multiple tasks concurrently and numerous sensors Most of the tasks involve I/O operations like using storage, networking and sensors Observation: Resources currently used on the system need not be used by the process currently running

8 8/82 LEAP2 Platform Representative platform for modern 32-bit embedded system Energy Management and Accounting Processor (EMAP2) ASIC continuous real-time energy monitoring across entire platform Measures energy usage in real- time for upto 16 individual subsystems McIntire, D., Ho, K., Yip, B., Singh, A., Wu, W., and Kaiser, W. J The low power energy aware processing (LEAP) embedded networked sensor system. In Proceedings of the 5th international Conference on information Processing in Sensor Networks (Nashville, Tennessee, USA, April , 2006).

9 9/82 LEAP2 Software ARM-Linux Patch Endoscope Scheduler measures energy on every scheduler tick Energy attributed to either user process or system User process energy consumption exposed to user space via the /proc/ /stat Etop reads this file to provide realtime energy consumption info Stathopoulos, T., McIntire, D., and Kaiser, W. J The Energy Endoscope: Real-Time Detailed Energy Accounting for Wireless Sensor Nodes. In Proceedings of the 7th international Conference on information Processing in Sensor Networks), April 2008.

10 10/82 Per-Process Energy Accounting: Caveats System operation dominated by I/O operations Userspace processes get switched out due to delay Latency of execution in kernel Kernel needs to track such interactions System call interface (SCI) in *nix kernel used for userspace-kernel transition Single point of entry/exit in kernel

11 11/82 Function call SCI functioning in Linux (ARM) TIME process Library Call kernel entry.S System call Flash fwrite() r7 = __NR_write switch to kernel (swi) write () vector_swi call sys_call_table [r7] sys_write() done return ret_from_syscall return numbytes write successful Process blocked

12 12/82 Modifications to SCI Implement two new system calls sys_initiate and sys_terminate Call sys_initiate on invocation of every system call Record the process id, timestamp and system call number The original system call is then invoked On completion, control returns to the kernel where we invoke sys_terminate sys_terminate calculates the energy spent for this system call and accurately allocates that to the corresponding process State for that call is then purged

13 13/82 Resource Accountant sys_initiate () (keep state) Modifications to SCI TIME kernel entry.S System call Flash done return ret_from_syscall … call sys_initiate vector_swi call sys_call_table [r7] … Resource Accountant sys_terminate () (energy accounting) call sys_terminate() write( )

14 14/82 Modifications to SCI: Discussion There are a lot of system calls invoked Eg: At bootup, about 7000 system calls before login For efficiency, we are currently only monitoring a small subset (sys_open, sys_close, sys_read, sys_write) These system calls account for majority of the system calls placed and energy consumed during the experiments Need to maintain state across system calls to keep track of file and socket identifiers Compared against our implementation of Endoscope

15 15/82 Experimental Setup LEAP2 running a copy (cp) of a large file from compact flash to compact flash Concurrently running calc1, compute intensive CPU-bound process that performs some computation and goes to sleep Multiple system services running in the background Ext2 filesystem and virtual memory tuned to minimize buffering Show energy consumption of Processor (PXA), Memory (SDRAM) and Compact Flash (CF)

16 16/82 cp and calc1: endoscope v2 Processor Speed = 104 MHz

17 17/82 cp and calc1: Resource Accountant (RA) Processor Speed = 104 MHz

18 18/82 Effect of Processor Frequency Scaling Bottleneck is read/write from and to compact flash CPU can drive reading/writing even at lowest processor frequency (104 MHz) Processor energy consumption increases at higher frequencies

19 19/82 cp and calc1: endoscope v2 Processor Speed = 312 MHz

20 20/82 cp and calc1: Resource Accountant Processor Speed = 312 MHz

21 21/82 cp and calc1: endoscope v2 Processor Speed = 624 MHz

22 22/82 cp and calc1: Resource Accountant Processor Speed = 624 MHz

23 23/82 Resource Accountant: Discussion Effective when I/O interaction takes some time Eg: GPS and other high latency sensors, flash writes etc. Intensive to monitor every system call System needs to be tailored for monitoring the significant operations only Does not account for optimizations at lower levels of kernel (eg: Delayed writes by ext2 fs, dirty page caching by VM, DMA, non-blocking drivers) Need driver support for end-end accuracy

24 24/82 Related Work AuthorsWork Banga et al. Resource containers: a new facility for resource management in server systems (OSDI 1999) Zeng et al. ECOSystem: managing energy as a first class operating system resource (ASPLOS 2002) Rajkumar et al.Resource Kernels (MMCN 1998) Fonseca et al. Quanto: Tracking energy in networked embedded systems. (OSDI 2008) Stathopoulos et al.The Energy Endoscope (IPSN 2008)

25 25/82 Outline Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues Karthik Dantu and Gaurav S. Sukhatme, "Rethinking data-fusion based services in sensor networks," In the Third IEEE Workshop on Embedded Networked Sensors (Emnets), 2006.

26 26/82 Motivation – Tiered Sensor Networks Mote-class sensor node Microserver node Base Station Transit Network Sensor node Gateway Sensor node

27 27/82 Tiered Sensor Networks Microservers: More powerful processor and radio Tradeoff true distributedness for saving on communication Data collection from motes followed by computation at microserver

28 28/82 SDP Formulation of Localization Connectivity-based constraints Range-based constraints Pratik Biswas, Yiyu Ye, “Semidefinite programming for ad hoc wireless sensor networks localization”, In Proceedings of conference on Information Processing in Sensor Networks (IPSN) 2004.

29 29/82 SDP Formulation of Localization s.t. Pratik Biswas, Yiyu Ye, “ Semidefinite programming for ad hoc wireless sensor networks localization ”, In Proceedings of conference on Information Processing in Sensor Networks (IPSN) 2004.

30 30/82 Hierarchical Overlapped Coordination Consider an optimization problem of the form - subject to Where h and g are functions and X Nestor Michelena, Hyungju Park, Panos Papalambros and Devadatta Kulkarni, “Hierarchical Overlapping coordination under non-linear constraints”, American Institute of Aeronautics and Astronautics, 1998.

31 31/82 Functional Dependence Table … Columns correspond to the variables in the problem Rows correspond to constraints Table with entries such that (i,j)th entry is 1 if constraint i involves variable j, and zero otherwise

32 32/82 Rearrange FDT

33 33/82 Problem reformulation subject to For each

34 34/82 HOC Algorithm 1.Fix linking variables and solve problem by solving independent sub problems 2.Fix linking variables to their values determined in step 1 and solve problem by solving sub problems 3.Go to step 1 with fixed values of -linking variables determined by step 2 4.Repeat until convergence is achieved Nestor Michelena, Hyungju Park, Panos Papalambros and Devadatta Kulkarni, “Hierarchical Overlapping coordination under non-linear constraints”, American Institute of Aeronautics and Astronautics, 1998.

35 35/82 Partitioning the Nodes Motes Microservers ,2

36 36/82 Partitioning: The Basic Idea Motes Microservers

37 37/82 SDP Relaxation for Localization Pratik Biswas, Yiyu Ye, “ Semidefinite programming for ad hoc wireless sensor networks localization ”, In Proceedings of conference on Information Processing in Sensor Networks (IPSN) s.t.

38 38/82 Simulation Setup Nodes distributed uniformly randomly in a square of side 100 units Radius of communication assumed to be 20 units Semi-centralized simulations had four master nodes

39 39/82 Centralized - Error vs. Iterations Error vs. Iteration Centralized Formulation (Degree = 8)

40 40/82 Distributed – Error vs. Iterations Error vs. Iteration in Semi-Centralized Formulation (Degree = 8)

41 41/82 Multiple masters Error vs. Number of Master Nodes in Semi-Centralized Formulation

42 42/82 Error – Edge Effects

43 43/82 Service Reconfiguration - Summary Framework adaptable to services that can be framed as an optimization problem Spatial distribution of sensor nodes lends itself to partitioning automatically Partitioning can be done with a simple broadcast from master nodes Formulation for lifetime-aware routing presented

44 44/82 Related Work AuthorsWork Niculescu et al.Localized Positioning in Ad hoc Networks (Adhoc Networks 2003) Pratik Biswas Semidefinite Programming Approaches to Distance Geometry Problems (Phd. thesis 2007) Savvides et al.Dynamic Fine-grained Localization (MOBICOM 2001) Aspnes et al.Theory of Network Localization (IEEE TMC 2006) Chang et al.Maximum Lifetime Routing in Wireless Sensor Networks (ToN 2004) Michelena et al.Hierarchical Overlapping coordination under non-linear constraints (AIAA 1998)

45 45/82 Functional Reconfiguration - Summary Per-Process Energy Accounting Demonstrated problem of accurate energy accounting in current embedded systems Designed resource accountant in the OS to perform energy accounting Prototype implementation on LEAP2/Linux shows accurate results Service Reconfiguration in Tiered Sensor Networks Proposed semi-centralized framework for reconfiguring services in tiered sensor networks Evaluation done on two services – Localization and Power-aware routing

46 46/82 Outline Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues

47 47/82 Robot Networks Marine observationUrban search and rescue Formation controlCollaborative tasking Target tracking Robust connectivity between the robots!

48 48/82 Robot Network Biconnectivity Problem Given a connected robot network, can they reassemble themselves with minimum movement to form a biconnected robot network? Minimum total sum distance: NP-Hard! Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May Karthik Dantu and Gaurav S. Sukhatme, Biconnected Robot Networks, In submission to Transactions in Robotics Oct 2009.

49 49/82 Definitions: Biconnectivity Biconnected Graph: Given a graph G(V, E), every vertex in V has atleast two vertex disjoint paths to every other node in V Biconnected Component: Subgraph of a given graph that is biconnected Articulation Point:Vertices in a connected graph that connects two biconnected components. This edge or vertex is the only path between the two biconnected components

50 50/82 Achieving Biconnectivity

51 51/82 Biconnectivity Algorithm: Introduction Conservative approach: Move one robot at a time Iterative: Improves connectivity of the network with iteration Each iteration potentially adds one new edge Only needs bearing measurement to neighbors Three phases in each iteration Compute biconnected components Identify potential neighbors Command movement

52 52/82 Biconnectivity Algorithm Phase I – Distributed biconnected components computation E.J.H. Chang. Echo algorithms: Depth parallel operations on general graphs. IEEE Transactions on Software Engineering, 8(4):391–401, Phase II – Identify nodes to move Articulation points compute bearing of all neighbors Identify nodes to move

53 53/82 Biconnectivity Algorithm - II Phase III - Move nodes Articulation points negotiate for least expensive move Least expensive node is chosen Command is sent based on relative angle – according to table shown Node 1 Node 2Q1Q2Q3Q4 Q1 -a l - 45  135  - a h -a l 180  - a h 45  - a l a h  a l  90  - a h Q2 45  - a l 135  - a h 90  - a l 270  - a h 135  - a l 315  - a h Q3 135  - a l 315  - a h 180  - a l 360  - a h Q4225  - a l 415  - a h

54 54/82 Biconnectivity Algorithm: Simulation Simulations done using Player/Stage simulator Studied various randomly generated topologies (connected) Results shown for 16-node and 20-node networks Results averaged over 25 simulation trials actual computed Study error in Relative Bearing Translation executed Turn angle commanded executed

55 55/82 Robustness to Bearing Error Gaussian error with zero mean, deviation shown on x-axis Algorithm robust to large bearing error (upto 30°)!

56 56/82 Robustness to Odometry Error Gaussian error with zero mean and deviation as a percentage of the command given

57 57/82 Robustness to turn angle error Gaussian error with zero mean and deviation as shown on x-axis

58 58/82 Robot Experiments iRobot Create with e-boxes Antenna raised for better signal strength readings Use radio to compute relative bearing OLSR routing protocol Neighbor list obtained from routing table

59 59/82 Robot Experiments - bearing computation Sample signal strength in the robot neighborhood according to pattern Measure 100 samples at each point Perform 3-D Principal Component Analysis Assume major axis as the direction of neighbor Experimented with wifi and zigbee radios Varied step size to study effect on bearing estimation Chosen step size 2 m Step size Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May 2009.

60 60/82 Biconnectivity: 5-node Topology Trial 1Trial 2 Edge Actual angle Measured angle(T1) Bearing error (T1) Measured angle(T2) Bearing Error (T2) 3-4 55 9.3  4.3   17.4    28.1   46.8    12.8   30.9    32.5   21.8    11.7  -4.3  25.7    22.1   25.4  Average bearing error is 24.32°

61 61/82 Related Work - Biconnectivity AuthorsWork Ahmadi et al. Keeping in Touch: Maintaining Biconnected Structure by Homogeneous Robots (AAAI 2006) Poduri et al. Using Local Geometry for Tunable Topology Control in Sensor Networks (IEEE TMC 2009) Tarjan Depth first search and Linear Graph Algorithms (SIAM J Computing 1972) Frederickson et al.On the Relationship between the Biconnectivity Augmentation and Travelling Salesman Problems (TCS 1981)

62 62/82 Thesis Contribution Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues

63 63/82 Robot Networks Marine observationUrban search and rescue Formation controlCollaborative tasking Target tracking Temporal robustness of routes using mobility cues

64 64/82 Route Stability: Problem Control algorithms expect a stable graph Routing protocol optimizes data forwarding path Path stability not a requirement for routing Frequent route switches confuse control algorithm Requirement unique to robot networks Karthik Dantu and Gaurav S. Sukhatme. Connectivity vs. control: Using directional and positional cues to stabilize routing in robot networks. In International Conference on Robot Communication and Coordination (ROBOCOMM) April 2009.

65 65/82 Route Stability: Example B A C D E F G

66 66/82 Route Stability: Example B A C D E F G

67 67/82 Route Stability: Example B A C D E F G

68 68/82 Route Stability: Example B A C D E F G

69 69/82 Route Stability: Example B A C D E F G

70 70/82 Route Stability: Our solution Robot networks have information about direction of movement and possibly position This can be used to choose routes that are more likely to be stable Modify OLSR - a popular adhoc routing protocol

71 71/82 Stability Metric: Direction Cue Direction Cue

72 72/82 Stability Metric: Location and Direction Cue Stability metric is equal to the link duration

73 73/82 Modifying OLSR: Route computation Added direction information base and position information base Modified Hello messages to incorporate direction, position and stability New routes are picked based on the best stability value instead of shortest path If a route exists, it is not modified unless the stability value falls below a threshold

74 74/82 Simulation Setup Ns-2 with OLSR implementation from U Murcia, Spain Deploy robots in 500x500 area Mobility of robots is based on random waypoint mobility model Radius of communication is approximately 50 units – propagation model is two ray ground Results are averaged over 10 trials each Percentage decrease in number of route switches is measured as the success metric

75 75/82 Effectiveness – Direction Cue Only Speed of travel vs. Route Stability

76 76/82 Effectiveness – Direction and Location Cue Speed of travel vs. Route Stability

77 77/82 Effectiveness – Direction and Location Cue Density vs. Route Stability

78 78/82 Effect of error in location cue Gaussian error with zero mean and deviation as a percentage of the radius of communication

79 79/82 Effect of error in direction cue Gaussian error with zero mean and deviation in degrees on x-axis

80 80/82 Simulation Results Directional cues improve stability by about 8%-10% Direction and position together improves route stability by about 15%-20% Error in position is not very detrimental to the route stability unless the error is high Error in direction has a greater impact on route stability

81 81/82 Related Work – Route Stability Ko and Vaidya Using Location Information to Improve Reactive Routing in MANETs by Using Directional Flooding (Wireless Networks 2000) Karp and Kung Greedy Perimeterless State Routing for Wireless Networks (Mobicom 2000) Ramachandran et al. Route Stability in Static Mesh Networks (PAM 2007)

82 82/82 Thesis Contribution Functional Reconfiguration A. Per-Process Energy Accounting B. Reconfiguration of essential services in a sensor network Spatial Reconfiguration A. Go from a connected network to a biconnected network B. Achieve route stability using directional and positional cues

83 83/82 F i g h t O n ! Trojan Cricket Club SC Badminton Armando UCLA Ascent Lab

84 84/82 Publications (Thesis-related) JOURNAL Karthik Dantu, and Gaurav S. Sukhatme. Biconnected robot networks. In Submission to Transactions on Robotics, October Karthik Dantu and Gaurav S. Sukhatme. Service Reconfiguration in Tiered Sensor Networks, In preparation. CONFERENCES AND WORKSHOPS Karthik Dantu, and Gaurav S. Sukhatme, “Per-Process Energy Accounting: Handling Asynchrony in Embedded Systems”, In Submission Dec Karthik Dantu and Gaurav S. Sukhatme. Connectivity vs. control: Using directional and positional cues to stabilize routing in robot networks. In International Conference on Robot Communication and Coordination (ROBOCOMM) April 2009 Karthik Dantu, Prakhar Goyal, and Gaurav S. Sukhatme. Relative bearing estimation from commodity radios. In International Conference of Robotics and Automation (ICRA), May Jesse Butterfield, Karthik Dantu, Brian P. Gerkey, Odest C. Jenkins, and Gaurav S. Sukhatme. Autonomous biconnected networks of mobile robots. In IEEE Workshop on Wireless Multihop Communications in Networked Robotics (WMCNR), Apr 2008.

85 85/82 Publications (Thesis-related) 5. Dustin McIntire, Timothy Chow, Karthik Dantu, Mansi Shah, Thanos Stathapoulos, Gaurav S. Sukhatme, William J. Kaiser The Low Power Energy Aware Processing (LEAP) Software Applications - Poster and Demonstration, In IPSN-SPOTS ’07: Proceedings of the 5th international conference on Information processing in sensor networks. 6.Karthik Dantu and Gaurav S. Sukhatme. Detecting and Tracking level sets of scalar fields using a robotic sensor network. In IEEE International Conference on Robotics and Automation (ICRA), pages 3665–3672, Apr Karthik Dantu and Gaurav S. Sukhatme. Rethinking data-fusion based services in tiered sensor networks. In IEEE Emnets ’06: Third Workshop on Embedded Networked Sensors,May Karthik Dantu, Mohammad H. Rahimi, Hardik Shah, Sandeep Babel, Amit Dhariwal, and Gaurav S. Sukhatme. Robomote: enabling mobility in sensor networks. In IPSN-SPOTS ’05: Proceedings of the 5th International Conference on Information Processing in Sensor Networks.

86 86/82 Publications (Other) 1.Anand Joshi, Mi Zhang, Ritesh Kadmawala, Karthik Dantu, Sameera Poduri, and Gaurav S. Sukhatme OCRdroid: A Framework to Digitize Text Using Mobile Phones In ICST International Conference on Mobile Computing, Applications and SErvices (MobiCASE),October Avinash Parnandi, Ken Le, Pradeep Vaghela, Aalaya Kolli, Karthik Dantu, Sameera Poduri and Gaurav S. Sukhatme, “Coarse In-Building Localization with Smartphones”, to appear in ICST International Workshop on Innovative Mobile User Interactivity (IMUI ’09) alongwith MobiCASE 2009, October 2009, San Diego, USA. 3.Dheeraj Kota, Neha Laumas, Urmila Shinde, Saurabh Sonalkar, Karthik Dantu, Sameera Poduri and Gaurav S. Sukhatme, “deSCribe: A Personalized Tour Guide and Navigational Assistant”, to appear in ICST International Workshop on Innovative Mobile User Interactivity (IMUI ’09) alongwith MobiCASE 2009, October 2009, San Diego, USA. 4.John Caffrey, Ramesh Govindan, Erik Johnson, Bhaskar Krishnamachari, Sami Masri, Gaurav S. Sukhatme, Krishna K. Chintalapudi, Karthik Dantu, Sumit Rangwala, Avinash Sridharan, Ning Xu, and Marco Zuniga. Networked sensing for structural health monitoring. In International Workshop of Structural Control Jun 2004.

87 87/82 Publications (Non-thesis related) 5.Krishna Chintalapudi, Jeongyeup Paek, Omprakash Gnawali, Tat S. Fu, Karthik Dantu, John Caffrey, Ramesh Govindan, Erik Johnson, and Sami Masri. Structural damage detection and localization using netshm. In IPSN-SPOTS ’06: Proceedings of the 5th international conference on Information processing in sensor networks.

88 88/82 Lifetime Aware Routing Max T system and for every iV and s.t. where

89 calc1 and scp: Resource Accountant


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