Mobility in Sensor Networks. Daniel Massaguer Feb 2005

Transcription

1 Mobility in Sensor Networks Daniel Massaguer Feb 2005

2 Mobility in Sensor Networks Mobile Code Maté: Code infection Agilla: Mobile Agents Mobile hardware Guided navigation Node mobility: Parasitic mobility Daniel Massaguer Feb 2005

3 Mobility in Sensor Networks Mobile Code Maté: Philip Levis and David Culler, "Maté: A Tiny Virtual machine for Sensor networks", ASPLOSX Agilla: Fok, C.-L., Roman, G.-C., Lu, C., "Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications" In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'05), Columbus, Ohio,

4 Mobility in Sensor Networks Mobile Code Maté [1] Pushc 1 sense pushc 7 and putled halt # Push 1 onto op. stack # Read sensor 1 (light) # Take the bottom 3 bits # Set LEDs to these 3 bits Virtual Machine Hides TinyOS programming details 1 Instr = 1 byte, 1 TinyOS task Programs are shorter [1] Philip Levis and David Culler, "Maté: A Tiny Virtual machine for Sensor networks", ASPLOSX

5 Mobility in Sensor Networks Mobile Code Maté [1] Code dissemination Code in capsules of 24 instr Each capsule has type and version Viral infection (control flooding): On reception of a new capsule, install it and broadcast it if it is a new version [1] Philip Levis and David Culler, "Maté: A Tiny Virtual machine for Sensor networks", ASPLOSX

6 Mobility in Sensor Networks Mobile Code Increase of abstraction Maté [1] IPS decrease, Energy increases Reduction of code size Energy decreases [1] Philip Levis and David Culler, "Maté: A Tiny Virtual machine for Sensor networks", ASPLOSX

7 Mobility in Sensor Networks Mobile Code Agilla [2] Middleware for Mobile Agents in Sensor Networks Maté: Viral code infection; Agilla: Application selects where to move or clone. Maté: one single application; Agilla: Multiple applications. [2] Fok, C.-L., Roman, G.-C., Lu, C., "Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications" In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'05), Columbus, Ohio,

8 Mobility in Sensor Networks Mobile Code Agilla [2] inter-agent coordination based on tuplespaces location-based addressing greedy geographic routing [2] Fok, C.-L., Roman, G.-C., Lu, C., "Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications" In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'05), Columbus, Ohio,

9 Mobility in Sensor Networks Mobile Code Agilla [2] [2] Fok, C.-L., Roman, G.-C., Lu, C., "Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications" In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'05), Columbus, Ohio,

10 Mobility in Sensor Networks Mobile Hardware Guided Navigation Li et al: Qun Li, Michael De Rosa and Daniela Rus, " Distributed Algorithms for Guiding Navigation across a Sensor Network", ACM MobiCom Batalin et al: M. A. Batalin, G. S. Sukhatme, M. Hatting, " Mobile Robot Navigation using a sensor network", IEEE ICRA Mobile Node Parasitic Mobility: Mathew Laibowitz and Joseph A. Paradiso, Parasitic Mobility in Dynamically Distributed Sensor Networks, ACM Mobisys

11 Li et al [4] Static sensor network guides a mobile device towards a target, maintaining the safest distance to the danger areas Target 9

12 Li et al [4] Artificial Potential Fields Target 9

13 Li et al [4] Artificial Potential Fields + Target _ 10

14 Pot[i]=0 Hops[j]= i=0 Pot=[0,--,--,--,--,--] Hops=[,,,,, ] 1 Pot=[--,0,--,--,--,--] Hops=[,,,,, ] 2 Pot=[--,--,0,--,--,--] Hops=[,,,,, ] 3 Pot=[--,--,--,0,--,--] Hops=[,,,,, ] 4 Pot=[--,--,--,--,0,--] Hops=[,,,,, ] 5 Pot=[--,--,--,--,--,0] Hops=[,,,,, ] 11

15 If danger, Hops[i]=0 and broadcast <src=0,hops=0> i=0 Pot=[0,--,--,--,--,--] Hops=[0,,,,, ] 1 Pot=[--,0,--,--,--,--] Hops=[,,,,, ] 2 Pot=[--,--,0,--,--,--] Hops=[,,,,, ] 3 Pot=[--,--,--,0,--,--] Hops=[,,,,, ] 4 Pot=[--,--,--,--,0,--] Hops=[,,,,, ] <src=5,hops=0> 5 Pot=[--,--,--,--,--,0] Hops=[,,,,,0] 12

16 If hops[src] > hops+1 then hops[src]=hops+1 and <src=0,hops=0> i=0 Pot=[0,--,--,--,--,--] Hops=[0,,,,, ] 1 Pot=[--,0,--,--,--,--] Hops=[1,,,,,1] 2 Pot=[--,--,0,--,--,--] Hops=[,,,,,1] 3 Pot=[--,--,--,0,--,--] Hops=[1,,,,, ] 4 Pot=[--,--,--,--,0,--] Hops=[,,,,,1] <src=5,hops=0> 5 Pot=[--,--,--,--,--,0] Hops=[,,,,,0] 13

17 Broadcast <src,hops[src]> i=0 Pot=[0,--,--,--,--,--] Hops=[0,,,,, ] <src=0,hops=1> 3 Pot=[--,--,--,0,--,--] Hops=[1,,,,, ] <src=5,hops=1> <src=0,hops=1> 1 Pot=[--,0,--,--,--,--] Hops=[1,,,,,1] <src=5,hops=1> 4 Pot=[--,--,--,--,0,--] Hops=[,,,,,1] <src=5,hops=1> 2 Pot=[--,--,0,--,--,--] Hops=[,,,,,1] 5 Pot=[--,--,--,--,--,0] Hops=[,,,,,0] 14

18 i=0 Pot=[0,--,--,--,--,--] Hops=[0,,,,,2] 1 Pot=[--,0,--,--,--,--] Hops=[1,,,,,1] 2 Pot=[--,--,0,--,--,--] Hops=[2,,,,,1] 3 Pot=[--,--,--,0,--,--] Hops=[1,,,,,2] 4 Pot=[--,--,--,--,0,--] Hops=[2,,,,,1] 5 Pot=[--,--,--,--,--,0] Hops=[3,,,,,0] 14

19 i=0 Pot=[0,--,--,--,--,--] Hops=[0,,,,,2] 1 Pot=[--,0,--,--,--,--] Hops=[1,,,,,1] 2 Pot=[--,--,0,--,--,--] Hops=[2,,,,,1] 3 Pot=[--,--,--,0,--,--] Hops=[1,,,,,2] 4 Pot=[--,--,--,--,0,--] Hops=[2,,,,,1] 5 Pot=[--,--,--,--,--,0] Hops=[3,,,,,0] 15

20 Pot[j]= 1 / Hops[j] 2 (j!=i) Pot[i] = Sum{Pot[j]} i=0 Pot=[1/4,--,--,--,--,1/4] 1 Hops=[0,,,,,2] Pot=[1/1 2,2,--,--,--,1/1 2 ] Hops=[1,,,,,1] 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] 3 Pot=[1/1 2,--,--,5/4,--,1/4] Hops=[1,,,,,2] 4 Pot=[1/4,--,--,--,5/4,1/1 2 ] 5 Hops=[2,,,,,1] Pot=[1/9,--,--,--,--,1/9] Hops=[3,,,,,0] 16

21 Safest path to goal i=0 Pot=[1/4,--,--,--,--,1/4] 1 Hops=[0,,,,,2] Pot=[1/1 2,2,--,--,--,1/1 2 ] Hops=[1,,,,,1] 4 Source 3 Pot=[1/1 2,--,--,5/4,--,1/4] Hops=[1,,,,,2] Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] Pot=[1/4,--,--,--,5/4,1/1 2 ] 5 Hops=[2,,,,,1] Pot=[1/9,--,--,--,--,1/9] Hops=[3,,,,,0] 17

22 Safest path to goal i=0 Pot=[1/4,--,--,--,--,1/4] 1 Hops=[0,,,,,2] Pot=[1/1 2,2,--,--,--,1/1 2 ] Hops=[1,,,,,1] 4 Source 3 Pot=[1/1 2,--,--,5/4,--,1/4] Hops=[1,,,,,2] Target <goal=2,id=0, hops=0, pot=0> 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] Pot=[1/4,--,--,--,5/4,1/1 2 ] 5 Hops=[2,,,,,1] Pot=[1/9,--,--,--,--,1/9] Hops=[3,,,,,0] 18

23 i=0 Pot=[1/4,--,--,--,--,1/4] 1 Hops=[0,,,,,2] Pot=[1/1 2,2,2,--,--,1/1 2 ] Hops=[1,,1,,,1] 4 Source 3 Pot=[1/1 2,--,--,5/4,--,1/4] Hops=[1,,,,,2] If Pot[goal]>pot+Pot[i] then Pot[goal]=pot+Pot[i] Hops[goal]=hops+1 Prior[goal]=id and <goal=2,id=2, hops=0, pot=0> Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 19

24 i=0 Pot=[1/4,--,--,--,--,1/4] Hops=[0,,,,,2] 1 Pot=[1/1 2,2,2,--,--,1/1 2 ] Source 3 Pot=[1/1 2,--,--,5/4,--,1/4] Hops=[1,,,,,2] Broadcast <goal,i, Hops[goal], Pot[goal] <goal=2,id=1, hops=1, pot=2> Hops=[1,,1,,,1] 4 Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] <goal=2,id=4, hops=1, pot=5/4> <goal=2,i hops=1, p Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 20

25 i=0 Pot=[1/4,--,9/4,--,--,1/4] 1 Hops=[0,,2,,,2] Prior=[--,--,1,--,--] Pot=[1/1 2,2,2,--,--,1/1 2 ] Source 3 Broadcast <goal,i, Hops[goal], Pot[goal] Pot=[1/1 2,--,13/4,5/4,--,1/4] Hops=[1,,2,,,2] Prior=[--,--,1,--,--] <goal=2,id=1, hops=1, pot=2> Hops=[1,,1,,,1] 4 Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] <goal=2,id=4, hops=1, pot=5/4> <goal=2,i hops=1, p Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 20

26 i=0 Pot=[1/4,--,9/4,--,--,1/4] 1 Hops=[0,,2,,,2] Prior=[--,--,1,--,--] Pot=[1/1 2,2,2,--,--,1/1 2 ] Hops=[1,,1,,,1] 4 Source 3 Pot=[1/1 2,--,13/4,5/4,--,1/4] Hops=[1,,2,,,2] Prior=[--,--,1,--,--] Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 21

27 Navigation i=0 Pot=[1/4,--,9/4,--,--,1/4] 1 Hops=[0,,2,,,2] Prior=[--,--,1,--,--] Pot=[1/1 2,2,2,--,--,1/1 2 ] Hops=[1,,1,,,1] <goal=2?> Source 3 Pot=[1/1 2,--,13/4,5/4,--,1/4] Hops=[1,,2,,,2] Prior=[--,--,1,--,--] 4 Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 22

28 Navigation <goal=2,id=0, hops=2, pot=9/4, prior=1> <goal=2,id=1, i=0 Pot=[1/4,--,9/4,--,--,1/4] 1 Hops=[0,,2,,,2] Prior=[--,--,1,--,--] Pot=[1/1 2,2,2,--,--,1/1 2 ] Source 3 Pot=[1/1 2,--,13/4,5/4,--,1/4] Hops=[1,,2,,,2] Prior=[--,--,1,--,--] hops=1, pot=2, prior=2> Hops=[1,,1,,,1] 4 Target 2 Pot=[1/4,--,5/4,--,--,1/1 2 ] Hops=[2,,,,,1] <goal=2,id=4, hops=1, pot=5/4, prior=2> Pot=[1/4,--,5/4,--,5/4,1/1 2 ] 5 Hops=[2,,1,,,1] Pot=[1/9,--,1/9,--,--,1/9] Hops=[3,,1,,,0] 23

29 Li et al [4] Performance optimization Profiling of neighbors: using information only from stable neighbors. -> eliminate asymmetry(?) and transient links Delaying broadcasts: waiting a preventive time to see if there is a better neighbor. (Only one packet broadcasted -> Dijkstra?). -> transmit less packets Random delays. (Depending on the granularity, it is already done by MAC layer?). -> reduce congestion Retransmissions. ->reliability Route cache flushing. ->adaptability 24

30 Li et al [4] Summary and Conclusions Conceptually, is like performing a distributed Bellman-Ford (B-F) twice: 1.- source=danger, metric=hops-to-danger 2.- source=target, metric=<sum(1/hops-to-danger 2 ), hops-to-target> Optimization -> Neighbor profiling + Moving from B-F to Dijkstra. Lessons learned on (Mica-based) WSNs: Symmetry assumption not valid Network congestion Transitory links Data loss Need GPS on all devices Approximating distance by number of hopes is ok in large networks 25

31 Parasitic Mobility [5] A node with sensing and communication capabilities attaches to mobile hosts (e.g. people, animals, vehicles, fluids, forces), and it remains attached as far as the host is bringing the node closer to a point of interest. [5] Mathew Laibowitz and Joseph A. Paradiso, Parasitic Mobility in Dynamically Distributed Sensor Networks, ACM Mobisys

32 References [1] Philip Levis and David Culler, "Maté: A Tiny Virtual machine for Sensor networks", ASPLOSX [2] Fok, C.-L., Roman, G.-C., Lu, C., "Rapid Development and Flexible Deployment of Adaptive Wireless Sensor Network Applications" In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'05), Columbus, Ohio, [3] Chenyang Lu, A Mobile Agent Middleware for Wireless Sensor Networks, (presentation), [5] Mathew Laibowitz and Joseph A. Paradiso, Parasitic Mobility in Dynamically Distributed Sensor Networks, ACM Mobisys Daniel Massaguer <dmassagu@uci.edu> Feb 2005