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GS 3 : Scalable Self-configuration and Self-healing in Wireless Networks. Hongwei Zhang & Anish Arora. 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
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GS3: Scalable Self-configuration and Self-healing in Wireless Networks Hongwei Zhang & Anish Arora
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
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 Arora’02) • 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
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
System model • System • multiple “small” nodes and one “big” node, on a plane • node distribution • density: ( Rts.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
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 > Rt) • Construct a set of cells , connected via a “head” node in each cell s.t. • radius of each cell is in [ R-c , R+c ] , where c = f (Rt) • each node belongs to only one cell • cells and the connectivity graph over head nodes self-heal locally
Outline • Static networks • Dynamic networks • Mobile dynamic networks • Related work • Conclusions
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)
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
Organizing neighboring clusters & heads Deviation problem is handled locally • instead of using real locations, node i uses its and its parent’s ILs • i calculates the ILs of next band cells in its search region < LD , RD > • big node: <0o , 360o> • other nodes: <-60o-a , 60o+a> , where a Sin-1(Rt / R) • for each IL, i ranks nodes within Rt radius of the IL (by <D, A>), and selects the highest ranked node as the corresponding cluster head
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
Outline • Static networks • Dynamic networks • Mobile dynamic networks • Related work • Conclusions
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)
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
H0 H0 H0 H0
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
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
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
Outline • Static networks • Dynamic networks • Mobile dynamic networks • Related work • Conclusions
Mobile dynamic networks H0 d H0
Outline • Static networks • Dynamic networks • Mobile dynamic networks • Related work • Conclusions
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
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
Outline • Static networks • Dynamic networks • Mobile dynamic networks • Related work • Conclusions
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)