Lecture 5: Topology Control

1
Lecture 5 Topology Control

• Anish Arora
• CIS788.11J
• Introduction to Wireless Sensor Networks
• Material uses slides from Paolo Santi and Alberto
  Cerpa

2
Problems affected by link quality

• Topology Control
• Neighborhood Management
• Routing
• Time Synchronization
• Aggregation
• Application Management

3
References

• Topology Control tutorial, Mobihoc04, Paolo
  Santi
• SPAN, Benjie Chen, Kyle Jamieson, Robert
  Morris, Hari Balakrishnan, MIT
• GAF/CEC, Y. Xu, S. Bien, Y. Mori, J . Heidemann
  D. Estrin, USC/ISI UCLA
• ASCENT, Alberto Cerpa and Deborah Estrin, UCLA
• GS3 Scalable Self-configuration and
  Self-healing in Wireless Networks, PODC 2002,
  Hongwei Zhang, Anish Arora
• M. Demirbas, A.Arora, V.Mittal, FLOC A Fast
  Local Clustering Service for Wireless Sensor
  Networks DIWANS 2004

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Why Control Communications Topology

• High density deployment is common
• Even with minimal sensor coverage, we get a high
  density communication network (radio range gt
  typical sensor range)
• Energy constraints
• When not easily replenished
• Power usage
• Observation radios consume about the same power
  in idle state than Tx and Rx state
• Chicken egg problem to save energy, radios
  must be turned off (not simply reduce packet
  transmissions) but if radios are turned off,
  nodes cannot receive messages

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Problem Statement(s)

• Find an MCDS, i.e. a minimum subset of nodes that
  is both
• Set cover
• Connected
• Choose a transmit-power level whereby network is
  connected
• per node or same for all nodes
• with per node there is the issue of avoiding
  asymmetric links
• cone-based algorithm
• node u transmits with the minimum power ?u s.t.
  there is at least one neighbor in every cone of
  angle x centered at u
• k-neighbors algorithm
• each node chooses nearest k neighbors for its
  subgraph
• k is chosen s.t. the graph generated is connected
  w.h.p.

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Problem Statement(s)

• Find a minimum subset of nodes that provides some
  density
• in each geographic region ? connectivity
• well look at the examples of GAF, SPAN, GS3,
  ASCENT
• Given a connected graph G, find a subgraph G
  which can route messages between nodes in
  energy-efficient way
• both unicast and broadcast spanners
• reduces interference as well
• Sub-problems
• Prune asymmetric links
• Tolerate node perturbations
• Load balance

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Where should TC be positioned in the protocol
stack?

• No clear answer in the literature
• One view
• Routing Layer
• TC Layer
• MAC Layer
• Routing protocol may trigger TC execution (to get
  better routes)
• Routing (structure) involves only active nodes
• MAC protocol may trigger TC execution (if
  neighborhood changes)
• TC controls coarse-grain duty-cycling, MAC
  controls fine-grain
• Mode changes need to be coordinated to avoid
  conflicts

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Assumptions Radio/MAC

• Circular or Isotropic Models GS3
• Grid-based connectivity GAF, GS3
• Radio/MAC dependencies
• 802.11 Power Saving mode Span
• Promiscuous mode ASCENT, CEC

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Assumptions Neighbor Information

• Locality
• 1-hop neighbor GS3, ASCENT
• n-hop neighbor (with various n gt 1) GAF, CEC,
  Span
• Dependency on routing GS3, Span
• Measurement-based ASCENT, CEC

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Properties Reactivity to dynamics load
balancing

• Local re-calculation of state GS3
• Global re-calculation of state Span
• Local recovery GS3, GAF, CEC, ASCENT
• Explicit load balancing mechanisms GS3, Span,
  GAF, CEC

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SPAN

• Goal preserve fairness and capacity still
  provide energy savings
• SPAN elects coordinators from all nodes to
  create backbone topology
• Other nodes remain in power-saving mode and
  periodically check if they should become
  coordinators
• Tries to minimize of coordinators while
  preserving network capacity
• Depends on an ad-hoc routing protocol to get list
  of neighbors the connectivity matrix between
  them
• Runs above the MAC layer and alongside routing

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Coordinator Election Announcement

• Rule if 2 neighbors of a non-coordinator node
  cannot reach each other (either directly or via 1
  or 2 coordinators), node becomes coordinator
• Announcement contention is resolved by delaying
  coordinator announcements with a randomized
  backoff delay
• delay ((1 Er/Em) (1 Ci/(Ni pairs))
  R)NiT
• Er/Em energy remaining/max energy
• Ni number of neighbors for node i
• Ci number of connected nodes through node i
• R Random0,1
• T RTT for small packet over wireless link

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Coordinator Withdrawal

• Each coordinator periodically checks if it should
  withdraw as a coordinator
• A node withdraws as coordinator if each pair of
  its neighbors can reach each other directly of
  via some other coordinators
• To ensure fairness, after a node has been a
  coordinator for some period of time, it withdraws
  if every pair of nodes can reach each other
  through other neighbors (even if they are not
  coordinators)
• After sending a withdraw message, the old
  coordinator remains active for a grace period
  to avoid routing loses until the new coordinator
  is elected

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Performance Results
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GAF/CEC Geographical Adaptive Fidelity

• Each node uses location information (provided by
  some orthogonal mechanism) to associate itself to
  a virtual grid
• All nodes in a virtual grid must be able to
  communicate to all nodes in an adjacent grid
• Assumes a deterministic radio range, a global
  coordinate system and global starting point for
  grid layout
• GAF is independent of the underlying ad-hoc
  routing protocol

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Virtual Grid Size Determination

• r grid size, R deterministic radio range
• r2 (2r)2 ? R2
• r ? R/sqrt(5)

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Parameters settings

• enat estimated node active time
• enlt estimated node lifetime
• Td,Ta, Ts discovery, active,
• and sleep timers
• Ta enlt/2
• Ts enat/2, enat
• Node ranking
• Active gt discovery (only one node active per
  grid)
• Same state, higher enlt --gt higher rank (longer
  expected time first)
• Node ids to break ties

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Performance Results
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CEC

• Cluster-based Energy Conservation
• Nodes are organized into overlapping clusters
• A cluster is defined as a subset of nodes that
  are mutually reachable in at most 2 hops

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Cluster Formation

• Cluster-head Selection longest lifetime of all
  its neighbors (breaking ties by node id)
• Gateway Node Selection
• primary gateways have higher priority
• gateways with more cluster-head neighbors have
  higher priority
• gateways with longer lifetime have higher
  priority

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Network Lifetime
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Challenges for local healing of solid-disc
clustering

• Equi-radius solid-disc clustering with bounded
  overlaps is not achievable in a distributed and
  local manner

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FLOC protocol

• Solid-disc clustering with bounded overlaps is
  achievable in a distributed and local manner for
  approximately equal radius
• Stretch factor, m2, produces partitioning that
  respects solid-disc
• Each clusterhead has all the nodes within unit
  radius of itself as members, and is allowed to
  have nodes up to m away of itself
• FLOC is locally self-healing, for m2
• Faults and changes are contained within the
  respective cluster or within the immediate
  neighboring clusters

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FLOC program

• By taking unit distance to be reliable comm.
  radius m be maximum comm. radius, FLOC
• exploits the double-band nature of wireless
  radio-model
• achieves communication- and energy-efficient
  clustering
• FLOC achieves clustering in O(1) time regardless
  of the size of the network
• Time, T, depends only on the density of nodes
  is constant
• Through simulations and implementations, we
  suggest a suitable value for T for achieving fast
  clustering without compromising the quality of
  resulting clusters

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Model

• Geometric network, e.g., 2-D coordinate plane
• Radio model is double-band
• Reliable communication within unit distance
  in-band
• Unreliable communication within 1 lt d lt m
  out-band
• Nodes have i-band/ o-band estimation capability
• RSSI-based using signal-strength as indicator of
  distance
• Statistics-based using average link quality as an
  indicator
• Fault model
• Fail-stop and crash
• New nodes can join the network

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Problem statement

• A distributed, local, scalable, and
  self-stabilizing clustering program, FLOC, to
  construct network partitions such that
• a unique node is designated as a leader of each
  cluster
• all nodes in the i-band of each leader belong to
  that cluster
• maximum distance of a node from its leader is m
• each node belongs to a cluster
• no node belongs to multiple clusters

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Justification for stretch factor gt 2

• For m2 local healing is achieved a new node is
• either subsumed by one of the existing clusters,
• or allowed to form its own cluster without
  disturbing neighboring clusters

)
(
(
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)
)
(
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)
new cluster
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Basic FLOC program

• Status variable at each node j
• idle j is not part of any cluster and j is not
  a candidate
• cand j wants to be a clusterhead, j is a
  candidate
• c_head j is a clusterhead, j.cluster_idj
• i_band j is an inner-band member of a
  clusterhead j.cluster_id a clusterhead itself is
  an i_band member
• o_band j is an outer-band member of j.cluster_id
• The effects of the 6 actions on the status
  variable

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FLOC actions

1. idle ? random wait time from 0T
   expired ? become a cand and bcast cand msg
2. receiver of cand msg is within in-band ? its
   status is i_band ? receiver sends a conflict msg
   to the cand
3. candidate hears a conflict msg ?
   candidate becomes o_band for respective cluster
4. candidacy period ? expires ? cand
   becomes c_head, and bcasts c_head message
5. idle ? c_head message is heard ?
   become i_band or o_band resp.
6. receiver of c_head msg is within in-band ? is
   o_band ? receiver joins cluster as i_band

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FLOC is fast

• Assumption atomicity condition of candidacy is
  observed by T
• Theorem Regardless of the network size FLOC
  produces the partitioning in T? time
• Proof
• An action is enabled at every node within at most
  T time
• Once an action is enabled at a node, the node is
  assigned a clusterhead within ? time
• Once a node is assigned to a clusterhead, this
  property cannot be violated
• action 6 makes a node change its clusterhead to
  become an i-band member, action 2 does not cause
  clusterhead to change

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FLOC is locally-healing

• Node failures
• inherently robust to failure of non-clusterhead
  members
• clusterhead failure detected via lease
  mechanism, the orphaned nodes execute clustering
  ---see node additions
• Node additions
• either join existing cluster, or
• form a new cluster without disturbing immediate
  neighboring clusters

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Extensions to basic FLOC algorithm

• Extended FLOC algorithm ensures that solid-disc
  property is satisfied even when atomicity of
  candidacy is violated occasionally
• Insight Bcast is an atomic operation
• Candidate that bcasts first locks the nodes in
  the vicinity for ? time
• Later candidates become idle again by dropping
  their candidacy when they find some of the nodes
  are locked
• 4 additional actions to implement this idea

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Simulation for determining T

• Prowler, realistic wireless sensor network
  simulator
• MAC delay 25ms
• Tradeoffs in selection of T
• Short T leads to network contention, and hence,
  message losses
• Tradeoff between faster completion time and
  quality of clustering
• Scalability wrt network size
• T depends only on the node density
• In our experiments, the degree of each node is
  between 4-12
• a constant T is applicable for arbitrarily
  large-scale networks

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Tradeoff in selection of T
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Constant T regardless of network size
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Implementation

• Mica2 mote platform, 5-by-5 grid
• Confirms simulation

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Sample clustering with FLOC
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GS3 Scalability via locality

• Locality is hard for some graph problems
• e.g., self-configuration and self-healing of
  routing tree
• An ideal goal for locality
  self-healing should be a function of the size of
  perturbation (in time, space, and energy)
• Locality depends on model

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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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Problem 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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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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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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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
• 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

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ASCENT

• Adaptive Self-Configuring sEnsor Networks
  Topologies
• Observation different applications may require
  the underlying topology to have different
  characteristics. For example
• Minimal
• Homogeneous with a certain degree of connectivity
• Heterogeneous with different degrees of
  connectivity in different regions
• Examples of these different regions may be
• Along a data flow path
• Avoiding a data flow path
• In the border of an event of interest
• Input application tolerance specified in terms
  of acceptable loss rate at any node

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Model

• Adapt to empirical measurements of link quality
  each node assesses its connectivity adapts its
  participation in the multi-hop topology based on
  the measured operating region
• Assumptions ASCENT needs to
• turn off the radio (sleep state)
• turn the NIC/MAC in promiscuous mode (passive
  state)
• ASCENT runs on top of MAC and below routing does
  not uses any information gathered by routing

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ASCENT Basics

• Node state active or passive
• Active nodes are in topology forward data
  packets (using orthogonal routing mechanism that
  runs on topology)
• Passive nodes can sleep or collect network
  measurements
• Each node measures of neighbors and packet loss
  locally
• Each node then decides to join the network
  topology or to adapt (e.g. reducing its duty
  cycle to save energy)

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State Transitions
NT neighbor threshold LT loss threshold Tx
state timer values (x p passive, s sleep, t
test)
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Details

• Each node adds a sequence number per packet (for
  loss detection)
• Neighbor estimator based on a neighbor loss
  threshold (NLT) 1 1/N (N number of neighbors
  in the previous cycle)
• Neighbor threshold value (NT) determines the
  average degree of connectivity in the network
• Loss threshold determines the maximum data loss
  application can tolerate
• Relation between Tp/Ts (passive sleep timers)
  determines amount of energy savings and
  convergence time

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Performance Results
Energy Savings (normalized to the Active case,
all nodes turn on) as a function of density.
ASCENT provides energy savings up to 5.5 times
for high density scenarios
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ASCENT Energy Savings Analysis
NT neighbor threshold Tp passive state
timer Ts sleep state timer Sleep power radio
off Idle power radio on ? Tp/Ts ? Sleep/Idle
0.004
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Adaptive Timers

• For any given probability target Pk and given
  the of passive nodes in the area, nodes can
  calculate the optimal relation between the
  passive and sleep timers (?)
• Larger the Pk target, the larger the alpha for
  any given density
• Larger the k, the larger the alpha (although it
  grows slowly)

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Adaptive vs Fixed Timers
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Interaction with routing

• SPAN gets the connectivity matrix and the
  neighbor list from a routing protocol. In
  addition, it needs to modify the route lookup
  process such that routing uses only coordinators
  to route packets
• GAF/CEC and ASCENT do not depend on the routing
  mechanisms, nor they need to modify them

61
Other issues

• None of the previous schemes required any
  particular MAC
• It would be interesting to study the synergy
  effect that these schemes may have with energy
  savings MACs (S-MAC, UCB MAC, etc)
• Also interesting to study the synergy effect with
  high-level energy savings mechanisms (STEM)

62
Energy Savings

• SPAN (2), GAF(3), CEC(3.5), and ASCENT (5.5)
  achieve comparable energy savings, albeit their
  goals are different
• SPAN and ASCENT have sublinear energy savings as
  a function of density (because they periodically
  check the status of the network). GAF/CEC and
  ASCENT (w/adaptive timers) have linear energy
  savings as a function of density
• Further studies are required to make more
  conclusive statements

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Topology Control from a Sensing Perspective

• So far we have considered only the communications
  perspective
• Sensing coverage model
• typically unit disk sensing
• note depends on object being sensed
• Node deployment model
• deterministic with (no failures or with isolated
  failures)
• approximated by a pdf or is random (as a result
  of rampant errors)
• Coverage requirements
• Point coverage (deterministic or probabilistic
  guarantee)
• Barrier coverage (deterministic or probabilistic
  guarantee)
• Worst-case coverage least exposed path
• Tracking coverage any uncovered path has length
  at most l

64
Sensing Coverage References

• Survey
• Coverage in Wireless Sensor Network, Mihaela
  Cardei, Jie Wu
• For 1-coverage
• Pater Hall, "An Introduction to the Theory of
  Coverage Processes, 1988
• For k-coverage
• Santosh Kumar and Balogh, Mobisys 2004
• For k-coverage poisson deployment
• Honghai Zhang and Jennifer Hou, Mobihoc 2004

65
Coverage Results

• 1-point coverage with deterministic placement
• hexagonal layout is optimal
• k-point coverage with deterministic placement
• question of optimal placement is open
• k-point probabilistic coverage
•  
• almost always k-coverage for poisson deployment
• n?r2 ln(n) k ln(ln(n)) (error term)
• where n is sensors and r is sensing radius
• almost always k-coverage for random uniform
  deployment
• has essentially same result

66
Coverage Algorithms

• Checking whether network is not suitably covered
• point coverage violation check is possible
  locally
• Maintaining coverage via sleep-wakeup
• optimal scheme is NP-Complete, if deployment
  unknown
• (so heuristics used)
• random independent scheduling, if deployment
  uniformly random
• sentry rotation between redundant nodes in each
  cluster/region

67
Both Communication and Sensing Topology Control

• Relation between sensing radius and communication
  radius
• If Comm radius 2 x Sensing radius
• then (k-coverage ? k-connectivity)