Computing Aggregates for Monitoring Wireless Sensor Networks

1
Computing Aggregates for Monitoring Wireless
Sensor Networks

• Jerry Zhao, Ramesh Govindan and Deborah Estrin
• 2003. 10. 16
• Presented by Yu Seung Jeong
• (Dept. of Computer Science, KAIST)

2
Outline

• Introduction
• Architecture for Monitoring Wireless Sensor
  Networks
• Definitions, Assumptions, and Models
• Computing Digests and Digest Diffusion
• Impact of Packet Loss and Solution
• Experimental Evaluation and Simulation Results
• Conclusions and Future Work

3
Introduction

• Motivation and Goals
• Every network requires monitoring
• Monitoring in traditional networks - SNMP
• Sensor network is no exception but need for a new
  architecture
• Contribution
• Architecture for sensor network infrastructures
• Three classes of software - Digests, Scans, and
  Dumps
• Design of protocols to continuously compute
  network digests
• Approach
• Digest protocols must be aggressively
  energy-efficient
• Piggybacking digest computation messages
• Digest computations must be autonomously derived
• Digest diffusion

4
Architecture

• Three software components of the architecture.
• Dumps
• Collects detailed node states or logs per node
  for diagnosis
• Implemented as an applications upon directed
  diffusion
• Amount of data per node is costly to collect
• Scans (indicate WHERE should dumps be collected
  from)
• Global view to the location of problems
• Abstracted views of resource consumption without
  referring to individual node
• Derived using in-network aggregation
• Significant energy cost of computing scans over
  entire network
• Should be invoked only when necessary
• Network Digests (indicate WHEN should scans be
  invoked)
• A digest is an aggregate of some network
  property.
• E.g. size of the network, average energy left at
  a node etc.
• Continuously collects aggregates of network
  properties in the background

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Definitions, Assumptions, and Models

• Digest function is denoted by f(v1,v2,,vn)
• vi is the value contributed by sensor node i
• f is decomposable by a function g
• f(v1,v2,,vn) g(f(v1,,vk), f(vk1,,vn))
• f is monotonic if and only if, when two partial
  results r1 and r2 are combined by a function r
  g(r1,r2), the result r satisfies i1,2 r ri
  for an ordering relationship
• f is exemplary if the result can be determined
  from one single value
• Decomposable digest function
• final result can be calculated from partial
  results
• VMAX maximum of v1,v2,, vn -gt monotonic and
  exemplary
• VAVG average of v1,v2,,vn -gt not monotonic
  (if negative) and not exemplary
• VSUM sum of v1,v2,,vn -gt not monotonic (if
  negative) and not exemplary
• VCNT number of the nodes in the network -gt
  monotonic but not exemplary

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Computing Digests 1/2

• Naive centralized approach does not scale well
  and has a single point of failure
• Some Requirements of Digest Computation
• Should be energy efficient
• Digests should be available all the time and
  everywhere in the network
• Typical Digest functions are node with max
  energy, count of no. of nodes, sum of node
  energies, avg. residual energy
• These functions are decomposable
• Hence partial result can be added to get overall
  result

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Computing Digests 2/2

• One can use hierarchical approach for computing
  the digests E.g. clusters like LEACH
• However this involves overhead in computing
  leaders and maintaining the hierarchy
• Authors propose Digest Diffusion

8
Digest Diffusion 1/2

• Nodes periodically send tuple (Mi, Si, Hi)
• Initially node sets Mi to its perceived maximum
  value E.g. its own residual energy
• Si is source of maximum and initialized to i
• Hi is hop distance of the source of maximum
• Upon receiving a tuple from neighbor a node
  performs following processing
• If Mj gt Mi then set Mi Mj, Si Sj, Hi Hj1
• Also set parent Pi j
• Algorithm converges in steps proportional to
  network diameter
• Approach is scalable
• Periodic messages can be piggybacked

9
Digest Diffusion 2/2

• Digest diffusion implicitly constructs a tree
  without any overlaps
• Digest tree is rooted at the node with the
  maximum value
• Every node i aggregates values from its children
  and passes the partial result to its parent along
  the tree
• The root will have the final aggregate value
• Metric for establishing the tree should be
  carefully chosen such that the tree is relatively
  stable. E.g. link degree of a node is a bad
  choice since it fluctuates a lot with node
  failures

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Computing Other Digests

• Digest diffusion cannot be used for non-exemplary
  digests such as VAVG due to possible overlap from
  partial results
• Other digest functions can be computed easily on
  digest tree
• Node i periodically calculates a partial result
  from most recent report from its children c1,c2,
• VCNT
• VAVG

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Digest Tree Maintenance

• Topology changes such as node failure and
  addition is combined within the process of
  updating VMAX
• Each node periodically broadcasts a message M
  (m,s,h) for updating VMAX every To seconds
• It takes Tp seconds to detect parent node failure
  or disconnection
• Each node keeps timer Tc for partial result sent
  by its child
• Sequence number or TTL value from root is placed
  into each message to avoid root crashes
• TpTo is ideal but choose Tp4To for soft-state
  tree stability

Digest Message for MAX, CNT, SUM, and AVG
12
Impact of Packet Loss 1/2

• Packet losses are frequent in wireless
  environments
• Packet losses make aggregation tree unstable and
  hence affect the quality of aggregate digest
• More than 10 of links suffers loss rate greater
  than 50

Percentage of Links
Probability of Successful Reception (1-p)
Distribution of link quality
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Impact of Packet Loss 2/2

• Symmetric links are good links with avg. packet
  loss p30
• Asymmetric links are bad links with avg. packet
  loss p80
• A link pair is relatively asymmetric if their
  loss difference is greater than 35

Percentage of Symmetric and Asymmetric Links
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Link Quality Profiling and Rejection

• Packet loss and link asymmetry can be prevalent
  in wireless networks
• Result in oscillating digest tree and cause
  significant error
• Solution is link quality profiling and rejection
• blacklist links with poor quality or asymmetry
• A node chooses as parent a node with which it has
  good and symmetric communication
• Use packet sequence number to estimate how many
  of the packets sent by the neighbor are getting
  lost
• Exchange I can hear you lists to identify
  asymmetric links

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Experimental Evaluation 1/3

• Used REAL Berkley Motes
• Used root mean square (RMS) error to quantify
  performance
• Vt is observed digest value at time t
• V is actual digest value
• Metrics
• communication cost
• robustness to packet loss
• latency

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Experimental Evaluation 2/3

• Compared proposed solution against 3 schemes
• Scheme 1 Digest computation algorithm without
  link rejection
• Scheme 2 Scheme 1 rejection of poor incoming
  links
• Scheme 3 Scheme 1 rejection of asymmetric
  (poor outgoing) links
• Proposed Scheme scheme 1 with both incoming and
  asymmetric link rejections turned on
• Communication cost is much less than the
  centralized solution because of in-network
  aggregation

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Experimental Evaluation 3/3
Relative RMS Error
Relative RMS Error
Network Diameter
Network Diameter
Relative RMS error for CNT
Relative RMS error for SUM
Convergence Time (second)
Relative RMS Error
Network Diameter
Network Diameter
Latency for MAX and CNT
Relative RMS error for AVG
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Simulation Results 1/2

• Used TinyOS simulator
• Packet loss model
• Packet loss over distance d is defined
• p(d) 0.2d/r d r
• 1.6d/ - 1.4 r lt d 1.5r
• 1.0 d gt 1.5r
• (where r is the nominal transmission range)
• Packet loss slightly drops to 20 at r
• Packet loss sharply drops to 100 at 1.5r
• Asymmetric links can be generated by setting r
• Each simulation runs approximately 40 minutes

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Simulation Results 2/2

• Scalability
• Sensitivity to data distribution
• Uniform distribution 0, 100
• Skewed distribution 10 of 90, 100 and 90 of
  0 or 1

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Conclusions and future work

• Proposed an architecture for monitoring sensor
  network
• Suggested design for computing network digests
• Carefully handling lossy and asymmetric links
  reduces error in digest computation
• Future work includes experiments on larger
  testbed and designing a full-fledged monitoring
  suite