GS3: Scalable Self-configuration and Self-healing in Wireless Networks

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GS3 Scalable Self-configuration and
Self-healing in Wireless Networks

• Hongwei Zhang Anish Arora

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Introduction

• Sensor networks are not deployed manually
• ? self-configuration (into interconnected
  clusters)
• Sensor nodes and wireless links are subject to a
  rich class of faults
• ? self-healing (of clusters and interconnections)
• Sensor networks need to scale well in time,
  space, and resources
• ? scalability in self-configuration and
  self-healing

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Scalability via locality

• An ideal goal for locality
• self-healing should be a function of the
  size of perturbation (in time, space, and energy)
  
• Example problem of dining philosophers
• for correctness dining philosophers need
  information only from philosophers at distance
  2 hops
• for fault-tolerance (Nesterenko and Arora02)
• if state corruptions occur within a 2-hop
  neighborhood, they can be corrected within the
  neighborhood itself
• any number of Byzantine philosophers can be
  tolerated as long as they are 2 hops away

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Locality via choice of model

• Locality for some graph problems is hard
• e.g. self-configuration and self-healing of
  routing tree

• Our approach to simplifying design of locality
• choose a proper model for specific problems

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System model

• System
• multiple small nodes and one big node, on a
  plane
• node distribution
• density (? Rt s.t. with high probability,
• there are multiple nodes in
  any circular area of radius Rt)
• localization relative location between nodes can
  be estimated
• Perturbations
• dynamic nodes
• joins, leaves (deaths), state corruptions
• mobile nodes

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Geography-aware self-configuration

• Geographic radius of clusters is crucial
• for communication quality, energy dissipation,
  data aggregations applications
• Problem statement
• Given
• R ideal cell radius (R gt Rt)
• Construct a set of cells , connected via a head
  node in each cell s.t.
• radius of each cell is in R-c , Rc , where c
  f (Rt)
• each node belongs to only one cell
• cells and the connectivity graph over head nodes
  self-heal locally

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Outline

• Static networks
• Dynamic networks
• Mobile dynamic networks
• Related work
• Conclusions

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Static networks

• An ideal case
• In reality no node may exist at some geometric
  centers (ILs), but, with high probability there
  are nodes no more than Rt away from any IL

(IL Ideal Location)
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How to find the set of cell heads

• Bottom-up ?
• hard to guarantee the placement and size of
  clusters
• Top-down w.r.t. big node
• use diffusing computation
• but, accumulation in deviation of head location
  from IL is a problem

i
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Organizing neighboring clusters heads

• Deviation problem is handled locally
• instead of using real locations, node i uses its
  and its parents ILs
• i calculates the ILs of next band cells in its
  search region lt LD , RD gt
• big node lt0o , 360ogt
• other nodes lt-60o-a , 60oagt , where
  a ? Sin-1(Rt / R)
• for each IL, i ranks nodes within Rt radius of
  the IL (by ltD, Agt), and selects the highest
  ranked node as the corresponding cluster head

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Summary static networks

• Cell structure is hexagonal
• cell radius
• Time taken to form the structure is ?(Db), where
  Db the maximum distance between the big node
  and the small nodes
• Scalability in self-configuration
• local coordination only with nodes within range
• local knowledge each node maintains info about a
  constant number of nearby nodes

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Outline

• Static networks
• Dynamic networks
• Mobile dynamic networks
• Related work
• Conclusions

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Dynamic networks

• Dynamics include
• node join, leave (death), state corruption
• Common vs. rare
• common perturbations node density is preserved
• rare perturbations node density is destroyed
• Scalable self-healing is achieved via locality
  in
• intra-cell healing
• inter-cell healing
• sanity checking of state (invariants)

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Local intra-cell healing

• Head shift
• upon head leaving (death)
• local in a radius of Rt
• Cell shift
• upon the death of all the nodes in an area of
  radius Rt
• local in a radius of R
• independent but consistent shift at individual
  cells ? sliding of the global head level
  structure

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Local inter-cell healing sanity checking

• Local inter-cell healing
• upon failure of intra-cell healing at head j,
• first, the parent of j tries to find a new head
  j
• if that fails, the children of j find new parents
• Local sanity checking of state invariants
• upon detecting violation of the hexagonality
  property,
• node corrects itself after checking with its
  neighbors
• when state perturbation includes several nodes,
  the perturbed region corrects itself from the
  outside going in, and all nodes are corrected
  within time proportional to size of perturbed
  region

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Summary dynamic networks

• Cell radius
• for cells not adjoining any gap
• for cells adjoining a gap
• Head tree is now minimum distance tree rooted at
  the big node
• Stabilization time from perturbed state ?(Dp),
  where Dp diameter of the continuously perturbed
  area

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Summary dynamic networks (contd.)

• Scalability in self-healing
• local fault-containment and healing
• local knowledge
• Local healing and fault-containment enables
• stable cell structure
• lengthened lifetime ?(nc) , where nc the
  number of nodes in a cell

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Outline

• Static networks
• Dynamic networks
• Mobile dynamic networks
• Related work
• Conclusions

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Mobile dynamic networks
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Outline

• Static networks
• Dynamic networks
• Mobile dynamic networks
• Related work
• Conclusions

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Related work

• Cellular hexagon structure (Mac Donald 79)
• Preconfigured not considering self-healing
• LEACH (Heinzelman et al 00)
• No guarantee about the placement and size of
  clusters
• Perturbations dealt with by globally repeating
  the whole clustering process

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Related work (contd.)

• Logical-radius based clustering (in Banerjee 01)
• non-local cluster maintenance, and no
  consideration of state corruption
• only logical radius ? long links and link
  asymmetry are possible
• multiple rounds of diffusion
• Self-stabilization
• tree maintenance (in Arora Gouda 90)
• not fault containing
• local mending (in Kutten Peleg 95)
• local in time, not in space

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Outline

• Static networks
• Dynamic networks
• Mobile dynamic networks
• Related work
• Conclusions

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Conclusions

• GS3 is scalable
• self-configuration
• self-healing
• And this is achieved by exploiting the model
  properties in wireless sensor networks
• Density
• Localization
• (Note we have also designed an algorithm for
  local containment of faults in general spanning
  trees for dynamic networks)