Infrastructureless Wireless networks

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An overview of the basic algorithmic knowledge about ad-hoc and sensor networks for engineers.

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Infrastructureless Wireless networks

  1. 1. Infrastructure-less Wireless Networks Gwendal Simon Department of Computer Science Institut Telecom 2009
  2. 2. Literature Books include: “Algorithms for sensor and ad hoc networks”, D. Wagner and R. Wattenhofer “Wireless sensor networks: an information processing approach”, F. Zhao and L. Guibas and journal/conferences include: ACM SigMobile (MobiHoc, SenSys, etc.) IEEE MASS and WCNC Elsevier Ad-Hoc Network, Wireless Networks 2 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  3. 3. Motivations Current wireless net. require an infrastructure: cellular network: interconnected base stations wifi Internet: an access point and Internet 3 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  4. 4. Motivations Current wireless net. require an infrastructure: cellular network: interconnected base stations wifi Internet: an access point and Internet Same flaws than centralized architectures: cost scalability privacy dependability 3 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  5. 5. Motivations Sometimes, there is no infrastructure transient meeting disaster areas military interventions alter-communication 4 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  6. 6. Motivations Sometimes, there is no infrastructure transient meeting disaster areas military interventions alter-communication Sometimes not every station hear every other station limited wireless transmission range large-scale area 4 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  7. 7. Multi-hop Wireless Networks Nodes: portable wireless devices transmission ranges do not cover the area density ensures network connectivity Links: wireless characteristics transmission model: local broadcasting energy consumption: transmission is costly Behavior: devices emit, receive and forward data 5 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  8. 8. A Taxonomy of Applications 6 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  9. 9. Ad-Hoc vs. Sensor Networks Ad-Hoc Networks Sensor Networks nodes powerful wifi devices tiny zigbee nodes algorithms all-to-all routing echo to sink mobility human or car motions failures performance criteria quality of service energy consumption 7 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  10. 10. Ad-Hoc Applications Delay-Tolerant Network (social media application) assumption: no connectivity, but high mobility objective: ensuring eventual message delivery 8 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  11. 11. Ad-Hoc Applications Delay-Tolerant Network (social media application) assumption: no connectivity, but high mobility objective: ensuring eventual message delivery Mesh Networks (rural wireless coverage) assumption: some nodes have Internet access objective: maintaining path to these nodes 8 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  12. 12. Ad-Hoc Applications Delay-Tolerant Network (social media application) assumption: no connectivity, but high mobility objective: ensuring eventual message delivery Mesh Networks (rural wireless coverage) assumption: some nodes have Internet access objective: maintaining path to these nodes Vehicular Ad-Hoc Networks assumption: a particular mobility model objective: mostly services related to car safety 8 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  13. 13. Sensor Network Applications Sink-Based Networks (monitoring of natural areas) assumption: one sink retrieves all sensed data objective: increasing life-time 9 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  14. 14. Sensor Network Applications Sink-Based Networks (monitoring of natural areas) assumption: one sink retrieves all sensed data objective: increasing life-time Mobile Object Tracking (area surveillance) assumption: sensors know their location objective: determining hostile position 9 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  15. 15. Sensor Network Applications Sink-Based Networks (monitoring of natural areas) assumption: one sink retrieves all sensed data objective: increasing life-time Mobile Object Tracking (area surveillance) assumption: sensors know their location objective: determining hostile position Multi-Sink Networks (intervention teams) assumptions: mobile sinks and fixed sensor objectives: increasing sink coverage 9 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  16. 16. Short Introduction to Popular Models 10 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  17. 17. Network as a Graph Unit-Disk Graph: 05 03 → node position 07 09 → circular transmission 12 11 06 10 01 → boolean connections 02 00 08 04 11 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  18. 18. Network as a Graph Unit-Disk Graph: 05 03 → node position 07 09 → circular transmission 12 11 06 10 01 → boolean connections 02 00 08 04 11 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  19. 19. Network as a Graph Unit-Disk Graph: 05 03 → node position 07 09 → circular transmission 12 11 06 10 01 → boolean connections 02 00 08 04 11 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  20. 20. Interferences Signal-to-noise-plus-interference (SINR) ratio Pu d(u,v )α Pw ≥β N+ w ∈V {u} d(w ,v )α Pu : power level of sender u d(u, v ): distance between u and v α: path-loss exponent N: noise β: minimum ratio 12 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  21. 21. A Tour of the Most Studied Issues 13 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  22. 22. Broadcasting I: Stormy Effect Broadcast: a simple basic problem : a source emits a message all nodes within the network eventually receive the message a simple and efficient solution: upon first reception of message, forward it. Limits of flooding in wireless networks: redundant messages interferences 14 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  23. 23. Broadcasting II: Proposals Probabilistic flooding: idea: forward the message with some probability p drawbacks: no guarantee of delivering refinements: adjust p to node density 15 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  24. 24. Broadcasting II: Proposals Probabilistic flooding: idea: forward the message with some probability p drawbacks: no guarantee of delivering refinements: adjust p to node density Constrained flooding: idea: only some nodes forward the message implementation: build the Minimum Connected Dominating Set drawbacks: maintaining cost in dynamic systems 15 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  25. 25. Mobility Models I Few theoretical proof, few real implementations ⇒ generate realistic node motions for simulations 16 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  26. 26. Mobility Models I Few theoretical proof, few real implementations ⇒ generate realistic node motions for simulations The simplest model: Random Waypoint 1. each node picks a random position uniformly 2. it travels toward this destination with a speed v 3. once it reaches it, it stops during few seconds 4. back to 1 16 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  27. 27. Mobility Models II: Improvements Basic Structural Flaws: non-uniform distribution of node location: higher node distribution in the center average speed decay: low speed nodes spend more time to travel Realistic Mobility Models: group movement area popularity urban models community-based 17 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  28. 28. Mobility Models II: Improvements Basic Structural Flaws: non-uniform distribution of node location: higher node distribution in the center average speed decay: low speed nodes spend more time to travel Realistic Mobility Models: group movement area popularity urban models community-based the most realistic one : using real traces! 17 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  29. 29. Localized Data Gathering Basic idea: query data from sensors within an area two rounds: query diffusion retrieve data from sensors main objectives: minimize energy consumption minimize the delay A problem related with broadcasting except: only sensors from the queried area are reached: complex queries are possible (average, max, etc.) 18 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  30. 30. Time Synchronization I Different time on nodes: different oscillator frequency ⇒ frequency error absolute difference between clocks ⇒ phase error The need of a common clock localization protocols some MAC protocols data fusion in sensor network 19 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  31. 31. Time Synchronization II Broadcasting standard time via GPS system: √ precision, simple implementation × expensive devices × limited usage (outdoor environment) Achieve a common time distributively: √ (almost) no special devices required √ more tolerant to the environment × special protocols × message overhead, multi-hop delays 20 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  32. 32. A Focus on Routing Protocols 21 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  33. 33. Routing Protocols Objective: select a path between a source and a destination Main design challenges: unstable network topology low-cost devices (energy, computing. . . ) Main routing mechanisms: neighbor discovering route setup route maintenance 22 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  34. 34. Proactive routing vs. On demand routing Proactive Reactive Setup all-to-all on demand Maintenance regularly during utilization Advantages no setup delay no unused routes Disadvantages fixed overhead long setup delay Main examples OLSR AODV 23 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  35. 35. AODV Route Discovery G F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  36. 36. AODV Route Discovery Broadcasting RREQ Mes- G sage. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  37. 37. AODV Route Discovery Setting up G reverse path. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  38. 38. AODV Route Discovery Setting up G reverse path. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  39. 39. AODV Route Discovery Setting up G reverse path. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  40. 40. AODV Route Discovery Setting up G reverse path. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  41. 41. AODV Route Discovery Setting up G reverse path. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  42. 42. AODV Route Discovery Replying RREP to G source. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  43. 43. AODV Route Discovery Forward path G setup. F H S B E D A 24 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  44. 44. AODV Route Maintenance Link breaks between B G and D. F H S B E D A 25 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  45. 45. AODV Route Maintenance Sending RERR mes- G sage. F H S B E D A 25 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  46. 46. AODV Route Maintenance Restarting route discov- G ery. F H S B E D A 25 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  47. 47. AODV Route Maintenance New route G discovered. F H S B E D A 25 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  48. 48. Some Tricks Intelligent flooding (detect close destination) idea: init TTL at 1, then 2, then 3. . . idea: flood slowly and send message to stop it 26 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  49. 49. Some Tricks Intelligent flooding (detect close destination) idea: init TTL at 1, then 2, then 3. . . idea: flood slowly and send message to stop it Route caching (use past flooding) idea: during flood, answer for a distant node drawback: contradict reactive routing philosophy 26 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  50. 50. Some Tricks Intelligent flooding (detect close destination) idea: init TTL at 1, then 2, then 3. . . idea: flood slowly and send message to stop it Route caching (use past flooding) idea: during flood, answer for a distant node drawback: contradict reactive routing philosophy Local maintenance (almost unchanged route) idea: instead of NAK s, look for d by yourself drawback: sometimes it does not work 26 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  51. 51. A Proactive Routing Protocol: OLSR Objective: make use of Multi-Point Relay (MPR) acting as super-peers easing topology discovery handling most of the traffic OLSR message types: HELLO: discover 1-hop and 2-hop neighbors topology discovery through MPR 27 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  52. 52. Neighbor sensing F G H S B D E A 28 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  53. 53. Neighbor sensing F G Nb:{S}, H 2hopNb:{} Broadcasting S B HELLO Message. Nb:{S}, 2hopNb:{} D E A Nb:{S}, 2hopNb:{} 28 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  54. 54. Neighbor sensing F G H S B Nb:{E}, Nb:{S,E}, 2hopNb:{} 2hopNb:{} D E A Nb:{E}, 2hopNb:{S} 28 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  55. 55. Neighbor sensing F G Nb:{F}, H 2hop Nb:{S} S B Nb:{E,F}, Nb:{S,E,F}, 2hop Nb:{} 2hop Nb:{} D E A 28 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  56. 56. Neighbor sensing F G Nb:{S,B,G}, Nb:{F,B,H}, H 2hopNb:{E,A,H} 2hopNb:{S,E,A,D} Nb:{G,D}, 2hopNb:{B,F,A} S B Nb:{E,F,B}, Nb:{S,E,F,A,G}, 2hopNb:{G,A} 2hopNb:{D,H} D E A Nb:{A,H}, Nb:{S,A,B}, Nb:{E,B,D}, 2hopNb:{B,E,G} 2hopNb:{F,G,D} 2hopNb:{S,F,G,H} 28 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  57. 57. MPR selection F G Nb:{S,B,G}, H 2hopNb:{E,A,H} S B Nb:{E,F,B}, Nb:{S,E,F,A,G}, 2hopNb:{G,A} 2hopNb:{D,H} D E A Nb:{S,A,B}, 2hopNb:{F,G,D} 29 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  58. 58. MPR selection F G HELLO message H indicating B as S B MPR of S and B note S as its MPR selector. D E A 29 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  59. 59. MPR selection F G MPR Selector: MPR Selector: H {} {B,F,H} MPR Selector: {G,D} S B MPR Selector: MPR Selector: {} {S,G,E,F,A} D E A MPR Selector: MPR Selector: MPR Selector: {A,H} {} {B,E,D} 29 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  60. 60. Topology Table Each node maintains a Topology Table containing all possible destinations notifying a MPR to reach them Structure of Topology Table (on S for example): Dest Addr Last Hop Seq Holding Time G B 1 10 A B 4 20 D A 6 10 H G 5 15 ... ... ... ... 30 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  61. 61. Building the Topology Table F G MPR Selector: H {B,F,H} S B D E A 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  62. 62. Building the Topology Table F G H S B Topology Table Des Lhop Seq Htime D F E G 2 30 A H G 2 30 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  63. 63. Building the Topology Table F G H MPR Selector: {G,D} S B MPR Selector: {S,G,E,F,A} D E A 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  64. 64. Building the Topology Table F G H Topology Table Des Lhop Seq Htime S F G B2 30 H G 2 30 B G 2 30 D E A 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  65. 65. Building the Topology Table F G H Broadcasting contin- S B ues. . . D E A MPR Selector: MPR Selector: {A,H} {B,E,D} 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  66. 66. Building the Topology Table F G MPR Selector: MPR Selector: H {} {B,F,H} MPR Selector: {G,D} S B MPR Selector: MPR Selector: {} {S,G,E,F,A} D E A MPR Selector: MPR Selector: MPR Selector: {A,H} {} {B,E,D} 31 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  67. 67. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 E A 6 30 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  68. 68. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 E A 6 30 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  69. 69. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 E A 6 30 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  70. 70. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 A B 2 E A 6 30 G B 2 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  71. 71. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 A B 2 E A 6 30 G B 2 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  72. 72. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 A B 2 E A 6 30 G B 2 D A 6 30 A D 7 30 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  73. 73. Building the Routing Table Topology Table on S Neighbor Table on S Des Lhop Seq Htime Nb:{E,F,B}, F G 2 30 2hopNb:{G,A} H G 2 30 B G 2 30 Routing Table on S F B 3 30 Des Nhop Hops A B 3 30 E E 1 E B 3 30 F F 1 G B 3 30 B B 1 B A 6 30 A B 2 E A 6 30 G B 2 D A 6 30 H B 3 A D 7 30 D B 3 H D 7 30 D H 8 30 32 / 41 G Gwendal Simon8 H 30 Infrastructure-less Wireless Networks
  74. 74. Any Hybrid Approach ? Merging advantages from both approaches: build a routing table at 4 ∼ 5 hops launch a reactive process if d is not Applicative concerns: OLSR is attractive because networks often small AODV scales well but no all-to-all routing 33 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  75. 75. Research Activity: Multi-Sinks Query Range 34 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  76. 76. Multi-sink Multi-hop WSN 200 Target application: “fireman application” Many sensors (small, blue) and some firemen (large, green) 150 Firemen talk directly with the sensors Gather only local information On demand, fixed rate data gathering 100 Hop based query, constrained flooding Simple to deploy and scalable 50 0 50 100 150 200 250 300 35 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  77. 77. Multi-sink Multi-hop WSN 200 Target application: “fireman application” Many sensors (small, blue) and some firemen (large, green) 150 Firemen talk directly with the sensors Gather only local information On demand, fixed rate data gathering 100 Hop based query, constrained flooding Simple to deploy and scalable 50 0 50 100 150 200 250 300 35 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  78. 78. Multi-sink Multi-hop WSN 200 Target application: “fireman application” Many sensors (small, blue) and some firemen (large, green) 150 Firemen talk directly with the sensors Gather only local information On demand, fixed rate data gathering 100 Hop based query, constrained flooding Simple to deploy and scalable 50 0 50 100 150 200 250 300 35 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  79. 79. Multi-sink Multi-hop WSN 200 Target application: “fireman application” Many sensors (small, blue) and some firemen (large, green) 150 Firemen talk directly with the sensors Gather only local information On demand, fixed rate data gathering 100 Hop based query, constrained flooding Simple to deploy and scalable 50 0 50 100 150 200 250 300 35 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  80. 80. Multi-sink Multi-hop WSN 200 Target application: “fireman application” Many sensors (small, blue) and some firemen (large, green) 150 Firemen talk directly with the sensors Gather only local information On demand, fixed rate data gathering 100 Hop based query, constrained flooding Simple to deploy and scalable Networking assumptions: 50 IEEE 802.15.4 MAC layer, ZigBee tree routing No in-network data aggregation, compression 0 Static sensors100 50 and sinks 150 (may extend to mobile 250 200 sinks) 300 35 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  81. 81. Network Sharing Without Congestions 200 Capacity of sensors c = 5 Each flow consumes r = 1 Nodes within u hops generate traffic 150 Configurations Feasible? (5, 1) yes S1 100 u1 = 5 S2 50 u2 = 1 0 50 100 150 200 250 300 0 36 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  82. 82. Network Sharing Without Congestions 200 Capacity of sensors c = 5 Each flow consumes r = 1 Nodes within u hops generate traffic 150 Configurations Feasible? (5, 1) yes S1 100 (4, 2) no u1 = 4 S2 50 u2 = 2 0 50 100 150 200 250 300 0 36 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  83. 83. Network Sharing Without Congestions 200 Capacity of sensors c = 5 Each flow consumes r = 1 Nodes within u hops generate traffic 150 Configurations Feasible? (5, 1) yes S1 100 (4, 2) no (3, 2) yes u1 = 3 S2 50 u2 = 2 0 50 100 150 200 250 300 0 36 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  84. 84. Network Sharing Without Congestions 200 Capacity of sensors c = 5 Each flow consumes r = 1 Nodes within u hops generate traffic 150 Configurations Feasible? (5, 1) yes S1 100 (4, 2) no (3, 2) yes u1 = 2 (2, 3) yes S2 50 u2 = 3 0 50 100 150 200 250 300 0 36 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  85. 85. Network Sharing Without Congestions 200 Capacity of sensors c = 5 Each flow consumes r = 1 Nodes within u hops generate traffic 150 Configurations Feasible? (5, 1) yes S1 100 (4, 2) no (3, 2) yes u1 = 2 (2, 3) yes S2 50 u2 = 3 62 configurations 0 50 100 150 200 250 300 0 36 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  86. 86. Which Configuration is Better? 200 Basic considerations: (4, 2): not feasible, (1, 1): inefficient 150 100 50 0 50 100 150 200 250 300 37 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  87. 87. Which Configuration is Better? 200 Basic considerations: (4, 2): not feasible, (1, 1): inefficient 150 Optimality criteria: 1 Maximum Impact Range 100 (5, 1): Sum up to 6 Max-Min Fairness 4 (2, 3) = (3, 2) (5, 1) 3 2 50 (?, ?, ?, ?) 0 50 100 150 200 250 300 37 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  88. 88. Problem Formulation Multi-Dimensional Multiple Choice Knapsack Problem a NP-complete problem 38 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  89. 89. Problem Formulation Multi-Dimensional Multiple Choice Knapsack Problem a NP-complete problem Toward a distributed heuristic algorithm only local views of the network only local optimal solutions 38 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  90. 90. Protocol 200 At each sink: enlarge requirement periodically receive notification from sensors 150 adjust requirement if it is smaller At each sensor: 3 100 measure the traffic detect congestion A 50 4 2 1 solve the local problem notify related sinks 0 50 100 150 200 250 300 39 / 0 41 Gwendal Simon Infrastructure-less Wireless Networks
  91. 91. Conclusion 40 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  92. 92. Personal Thoughts Great theoretical importance: a lot of new and scientifically exciting problems a multi-disciplinary field (network, algorithms, computational geometry, probabilities) 41 / 41 Gwendal Simon Infrastructure-less Wireless Networks
  93. 93. Personal Thoughts Great theoretical importance: a lot of new and scientifically exciting problems a multi-disciplinary field (network, algorithms, computational geometry, probabilities) Unsure applicative importance: no killer application yet cellular networks just do what we want 41 / 41 Gwendal Simon Infrastructure-less Wireless Networks

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