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Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks


Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks Alec Woo, Terence Tong, David Culler SenSys 2003 – PowerPoint PPT presentation

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Title: Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks

Taming the Underlying Challenges of Reliable
Multihop Routing in Sensor Networks
  • Alec Woo, Terence Tong, David Culler
  • SenSys 2003

Key Observations
  • Many wireless links are lossy
  • Loss rate may change dynamically
  • Environmental factors
  • Highly correlated behavior of an application
  • Routing should consider these underlying factors
  • A lot of existing work on routing are based on
    abstract MAC physical layer model
  • Simply assume 802.11 takes care of MAC layer

  • Empirical link quality observation
  • Connectivity analysis
  • Likelihood of the success of a communication
  • Distance, residual energy, congestion, channel
  • Link quality estimation
  • Neighborhood management
  • Routing for periodic data collection applications

Empirical Observation of Link Characteristics
  • Measure loss rates between many different pairs
    of nodes at different distances
  • Starting point for development of a practical
    topology formation and routing
  • A sequence of linearly arranged sensor nodes with
    a spacing of 2 feet
  • One transmitter sends packets 200 packets at the
    rate of 8 packets/sec
  • Remaining nodes counts the number of successfully
    received packets
  • Environment? Indoor? Outdoor? Obstacles??

Empirical Results
  • A simple probabilistic means can be used to
    capture the link behavior in simulations
  • Connected region
  • Transitional region link probability with mean
    variance from the empirical data
  • Disconnected region

  • Spherical radio range assumption in current
  • Localization, Sensing Coverage, Topology Control
  • Radio Irregularity
  • Deepak Ganesan, etc., Complex Behavior at Scale
    An Experimental Study of Low-Power Wireless
    Sensor Networks , UCLA/CSD-TR 02-0013, 2002
  • Alberto Cerpa, etc., SCALE A Tool for Simple
    Connectivity Assessment in Lossy Environments,
    CENS-TR 03-0021, 2003
  • Jerry Y. Zhao, etc., Understanding Packet
    Delivery Performance in Dense Wireless Sensor
    Network, ACM SenSys, 2003
  • Alec Woo, etc., Taming the Underlying Challenges
    of Reliable Multihop Routing in Sensor Networks,
    ACM SenSys, 2003
  • DOI Concept
  • Tian He, etc., Range-Free Localization Schemes
    in Large Scale Sensor Networks, MobiCom, 2003

Link Estimation
  • Individual nodes estimate link quality by
    observing packet success and loss events
  • Use the estimated link quality as the cost metric
    for routing
  • Good estimator should
  • React quickly to potentially large changes in
    link quality
  • Stable
  • Small memory footprint
  • Simple, lightweight computation

  • Snooping
  • Track the sequence numbers of the packets from
    each source to infer losses
  • Window mean with EWMA
  • WMEWMA(t, a) (packets received in t) /
    max(packets expected in t, packets received in
  • t, a tuning parameters
  • t message opportunities
  • Take average in a window
  • Take EWMA of the average

WMEWA (t 30, a 0.6)
Neighborhood Management
  • Neighborhood table
  • Record information about nodes from which it
    receives packets
  • MAC address, routing cost, parent address, child
    flag, reception (inbound) link quality, send
    (outbound) link quality, link estimator data
  • Propagate back to the neighbors as the outbound
    rather than inbound link quality is needed for
    cost-based routing
  • The receiving node may update its own table based
    on the received information possibly indicating
    topology changes ? Distance-vector based routing
  • How does a node determine which nodes it should
    keep in the table?
  • Keep a sufficient number of good neighbors in the
  • Similar to cache management

Background Distance vector routing
  • Link state routing algorithm
  • Assume knowledge of the network topology and all
    link costs
  • Apply Dijkstra algorithm to find the shortest
    path from one source to all the other nodes
  • Implemented via link state broadcast Perlmann
  • Distance vector routing
  • Iterative, distributed, asynchronous algorithm
  • Receive from immediate neighbors
  • Perform a calculation and broadcast the result
    back to the neighbors
  • Also called Bellman-Ford algorithm
  • For example, look up http//en.wikipedia.org/wiki/

Management Policies
  • Insertion
  • Heard from a non-resident source
  • Adaptive down-sampling technique
  • Probability of insertion N/T neighbor table
    size / distinct neighbors
  • At most N messages can be inserted for every T
  • Eviction
  • FIFO, Least-Recently Heard, CLOCK, Frequency

Good neighbors maintainable (table size 40)
  • Frequency Algorithm
  • Keep a frequency count for each entry in the
  • Reinforce a node by incrementing its count
  • A new node will be inserted if there is an entry
    with a zero count
  • Otherwise, decrement the count of all entries and
    drop the new candidate

Cost-based routing
  • Key ideas
  • Minimize the cost that is abstract measure of
  • Could be hops, retransmissions, etc.
  • Minimize retransmissions A longer path with
    fewer retransmission could be better than a
    shorter path with more retransmissions!
  • Distance-vector based approach implemented by the
    parent selection component
  • Periodically run parent selection to identify one
    of the neighbors for routing
  • May also locally broadcast a route message
    including parent address, estimated routing cost
    to the sink, and a list of reception link
    estimations of neighbors
  • A receiving node may update the neighbor table
    based on the received info or drop it
  • Flag a child in the table to avoid a cycle
  • When a cycle is detected trigger parent selection
    without the current parent

Routing Framework
Underlying Issues
  • Parent selection
  • If connectivity to the current parent is lost, a
    node disjoins from the tree, and sets its routing
    cost to infinity ? Reselect a parent
  • Rate of parent change
  • Periodic Parent selection for every route update
    msg from neighbors incurs a domino effect of
    route changes
  • Parent snooping
  • For example, quickly learn routing info
  • Cycles
  • Monitor forwarding traffic and snoop on the
    parent address in each neighbors msg -gt Identify
    child nodes and dont consider them as potential

Underlying Issues
  • Duplicate packet elimination
  • Use sender address sequence number
  • Queue management
  • Give priority to originating traffic assuming
    originating data rate is lower than forwarding
  • General fair queuing is not considered in this
  • Relation to link estimation
  • Link failure detection based on a fixed number of
    consecutive xmission failures can be ineffective
    over semi-lossy links
  • Link quality estimation can be a better judgment
    of link failure
  • Bidirectional link estimations can avoid routing
    over asymmetric links
  • Stability and agility of link estimators can
    significantly affect routing
  • Final tuning must be done while observing its
    effect on routing performance

Cost metric
  • MT (Minimum Transmission) metric
  • Expected number of transmissions along the path
  • For each link, MT cost is estimated by 1/(Forward
    link quality) 1/(Backward link quality)
  • Inherently non-linear
  • For MT, a substantial noise margin should be used
    in parent select to enhance stability
  • Reliability
  • Another cost metric
  • Product of link qualities along the path
  • Not explored in this paper

Performance Evaluation Tested Routing Algorithms
  • Shortest Path
  • Conventional distance-vector approach
  • Each node picks a minimum hop-count neighbor as
    the parent and set its own hop-count to one
    greater than its parent
  • Two variations for performance analysis
  • SP A node is a neighbor if a packet is received
    from it
  • SP(t) A node is a neighbor if its link quality
    exceeds the threshold t
  • t 70 only consider the links in the effective
  • t 40 also consider good links in the
    transitional region

Performance Evaluation Tested Routing Algorithms
  • Minimum Transmission (MT)
  • Use the expected transmissions as the cost
  • Use a new path if the new cost is lesser by a
    noise margin
  • MTTM
  • Assume a neighbor table can maintain only 20
  • Broadcast
  • Root periodically floods the network
  • A node chooses a parent that forwards the flooded
    msg to itself first in each epoch
  • Use the reverse path to reach the root

Performance Evaluation Tested Routing Algorithms
  • Destination Sequence Distance Vector (DSDV)
  • Choose a parent based on the freshest sequence
    number from the root to avoid a cycle
  • Maintain a minimum hop count when possible
  • Ignore link quality Consider a node a neighbor
    once heard from it
  • Periodically reevaluate

Packet level simulations
  • Built a discrete time, event-driven simulator in
  • Network of 400 nodes 20 20 grid with 8 feet
  • Sink is placed at a corner to maximize the
    network depth

Packet level simulation
Hop Distribution
Path reliability over distance
Packet level simulation
Empirical study of a sensor field
  • Evaluate SP(40), SP(70), MT
  • 50 Berkeley motes inside a building
  • 5 10 grid w/ 8 foot spacing
  • 90 link quality in 8 feet
  • 3 inches above the ground

  • Hop Distribution
  • SP(70) failed to
  • construct a routing
  • tree
  • - MT congested Triple the data origination and
    route update rate
  • Link Quality of MT
  • Vary around 70
  • SP(70) may suffer

E2E success rate
Irregular Indoor Network
  • 30 nodes scattered around an indoor office of

Link Estimation of a node to its neighbors over
E2E Success Rate
  • Link quality estimation and neighborhood
    management are essential to reliable routing
  • WMEWMA is a simple, memory efficient estimator
    that reacts quickly yet relatively stable
  • MT (Minimum Transmissions) is an effective metric
    for cost-based routing
  • The combinations of these techniques can yield
    high E2E success rates

Beacon Vector Routing Scalable Point-to-Point
Routing in Wireless Sensornets
  • R. Fonseca et al.
  • NSDI 05

  • Most existing protocols only support basic
    many-to-one or one-to-many routing primitives
    (e.g., Directed diffusion, TAG, )
  • More point-to-point routing protocols have
    recently been proposed
  • Applications Pursuer-evader game, spatial
    queries, reactive tasking, multi-dimensional
    range queries, data centric storage,
  • No practical and broadly applicable
    implementation of point-to-point routing in WSNs

Design Goals
  • Develop implement a point-point routing
  • Simple with minimal complexity
  • Make minimal assumptions about radio quality,
    presence of GPS,
  • Use TinyOS tree construction prtocol

Key Ideas
  • Randomly select a few beacon nodes
  • Construct trees from the beacons to every other
  • Every node knows its distance (hops) to every
    beacon by using the standard reverse path tree
  • These beacon vectors serve as coordinates
  • Apply simple greedy, geographic forwarding

  • Nodes periodically send a local broadcast to
    announce their coordinates
  • A node qs position P(q) ltq1, q2, , qrgt where
    qi is hops from node q to beacon i
  • Distance function d(p, d) to measure how good p
    would be as a next hop to reach the destination d
  • Choose a node whose coordinates are more to the
  • Move towards a beacon when the destination is
    closer to the beacon than the current node
  • Move away from a beacon when the destination is
    further from the beacon than the current node

Fallback mode
  • If a node cannot make a progress towards the
    destination itself, it forwards the packet to the
    parent in the corresponding beacon tree
  • A parent does the same thing
  • First try to apply greed forwarding
  • If it doesnt work, rely on the fallback mode
  • If the closest beacon still cannot find the
    destination, it does scoped flooding

Beacon maintenance
  • Route based on the beacons the source and
    destination have in common
  • Does not require perfect beacon info.
  • Each entry in the beacon vector has a sequence
  • Periodically updated by the corresponding beacon
  • Timeout
  • If the beacons lt r, non-beacon nodes nominate
    themselves as beacons

Location directory
  • Depending on the application, a source may first
    have to look up the destination coordinates by
  • Use beacons as storage
  • Hashing H nodeid ? beaconid 14
  • Each node k that wants to be a destination
    periodically publishes its coordinates to its
    corresponding beacon bk H(k)
  • When a node wants to route to node k, it sends a
    lookup request to bk
  • Cache the coordinates

Simulation Results
  • Assumptions for high level simulation
  • Fixed circular radio range
  • Ignore the network capacity and congestion
  • Ignore packet losses
  • Place nodes uniformly at random in a square
    planner region
  • 3200 nodes uniformly distributed in a 200 200
    unit area
  • Radio range is 8 units
  • Average node degree is 16
  • Vary total beacons and routing beacons

Greedy success rate
Success ratio given 10 routing beacons
On-demand two hop neighbor acquisition
  • At lower densities, each node has fewer immediate
  • The performance of greedy routing drops
  • Add a neighbors neighbors to the routing table,
    if greedy forwarding is impossible

beacons required to achieve less than 5 scoped
  • On-demand two hop neighbor acquistion
  • Start with one hop neighbors
  • Fetch neigbors neigbors when theres a void

Performance under obstacles
  • Place horizontal vertical walls with lengths of
    10 or 20 units when the radio range is 8 units

Prototype evaluation
  • Office-Net 42 mica2dot motes in a 20m 50m
  • Univ-Net 74 mica2dot motes deployed across
    multiple student offices on a single floor in a
    UC Berkeley building

Link quality vs. distance
  • Orthogonal! (in Office-Net)
  • Contradicts to circular radio assumptions made by
    geographic routing protocols
  • BVR is connectivity based

Routing performance in Office-Net
  • - Success rate gt 98
  • 1.2 of the reqeusts resulted in scoped flooding
  • average scope of 2 hops
  • - Contention drops lt 0.1

Routing performance in Univ-Net
  • - Success rate gt 98
  • 5.5 of the reqeusts resulted in scoped flooding
  • average scope of 2 hops
  • - Contention drops lt 0.1

Office-Net success rate
Beacon failure
  • TOSSIM TinyOS simulator
  • 100 motes with 8 beacons
  • Expected node degree of 12
  • TOSSIMs lossy link generator
  • Based on empirical data to simulate lossy and
    asymmetric connectivity

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Related Work
  • DSDV computes the shortest path between all
    possible pair of source and destination
  • Scalibility problem
  • On-demand route discovery
  • Poor performance when many source-destination
    pair want to communicate
  • Landmark routing
  • Hierarchical set of landmark nodes periodically
    send scoped route discovery messages
  • ? Each node self-configures its address
    concatenation of the closest landmark at each
    level of the hierarchy
  • ? Landmark maintenance
  • ? How to tune the landmark scope?

  • Geographic routing
  • GPSR
  • ? Highly scalable
  • O(1) route discovery
  • O(1) routing table
  • Local planarization
  • Path lengths are close to the shortest path
  • ? Unit graph assumption
  • ? Each node should node its geographic
  • ? Greedy forwarding can be suboptimal because it
    does not use real connectivity info.

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