Large wireless autonomic networks Sensor networks Philippe Jacquet.

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Presentation transcript:

Large wireless autonomic networks Sensor networks Philippe Jacquet

Future of internet A galaxy of wireless mobile nodes

Toward massively dense networks Captors sensor networks Micro or nano-drones –Static or mobile –Several thousands nodes per hectar

Nano drones and droids –Very small RF devices.

5. A simple wireless model Physical model –An infinite plan –Emitters have same nominal power Q –Signal attenuation at distance r from emitter : x y

5. Physical model –S is emitter set at time t Received signal at point z and time t

A wireless model Emitters are distributed as a point Poisson process in the plan –Signals sum

A wireless model Signal distribution

Signal power Laplace transform Partition of the plan

Wireless Space capacity With signal over noise ratio K requirement Average area of correct reception

Wireless Space capacity Reception probability vs distance Optimal routing radius

Wireless Space capacity Average number of hops Average per hop transmission number Net traffic density A

Wireless space capacity Net traffic density –Increases when increases. –Is there a limit on ?

Density limit Network must remain dense Gupta Kumar rule for non isolation Density limit

Density limits Brut per node traffic limit : Net per node traffic limit :

Space capacity result (Gupta- Kumar 2000) The capacity increases with the density Massively dense wireless networks N capacity

Protocol on wireless network Every node sends hellos at frequency h –Hellos are not routed Traffic density due to hellos –No other traffic: –Limit network size due to hellos

Manageable neighborhood Average neighborhood size Maximum network size

6. Interprétation multi sauts voisinage moyen M voisinage unique réseau déconnecté when N<M: Single hop

Neighbor vontrol: remaining capacity Traffic density positive since moralement libre gestion du voisinage

Time capacity paradox Mobility can create capacity in disconnected networks Delay Tolerant Networks X X path disruption! S D End-to-end path X X path disruption! node link

Information propagation speed Unit disk graph model Random walk mobility model

Time capacity paradox Mobility creates capacity capacity time capacity time Permanently disconnected Permanently connected Information propagation time