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Lance: Optimizing High-Resolution Signal Collection in Wireless Sensor Networks. Geoffrey Werner-Allen, Stephen Dawson-Haggerty, and Matt Welsh School of Engineering and Applied Sciences, Harvard University, Cambridge SenSys 2008. Outline. Introduction Lance System Architecture

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lance optimizing high resolution signal collection in wireless sensor networks

Lance: Optimizing High-Resolution Signal Collection in Wireless Sensor Networks

Geoffrey Werner-Allen, Stephen Dawson-Haggerty, and Matt Welsh

School of Engineering and Applied Sciences, Harvard University, Cambridge

SenSys 2008

  • Introduction
  • Lance System Architecture
  • Policy Module
  • Case Study
  • Implementation
  • Evaluation
  • Deployment
  • Conclusion
  • Acquisition of high resolution signals using low-power wireless sensor nodes.
    • EX: Acoustic, seismic, and vibration waveforms (high data rate)
  • Constraint : (1). Radio bandwidth (2). Energy usage
  • Typically: Only focus on “interesting” signal
  • Managing:
    • Limited energy capacity
    • Severely constrainedradio bandwidth.
  • Depending on the (1). Sampling rate and (2). Resolution.
  • Low-power sensor node radios single-hop throughput : 100 Kbps
  • The best reliable protocols :Less than8 Kbps for a single transfer over multiple hops.
  • Lance -- General approach to bandwidth and energy management for reliable signal collection.
  • Application data unit (ADU) :
    • Summary (node)
    • Value (base station)
    • download schedule (lance)
  • Incorporates a cost estimator that predicts the energy cost for reliably downloading each ADU from the network.
  • Cost estimator : Load-balancing download operations.
  • Policy modules optimization metrics
    • lifetime targeting
    • acquiring temporally- or spatially-correlated data
lance system architecture
Lance System Architecture
  • ADU : Unit of data storage and retrieval
  • Each unique aiconsists of a tuple {i, ni, ti, di, vi, ¯ci}
    • i : ADU identifier
    • ni : The node storing the ADU
    • ti : Timestamp
    • di : Raw sensor data
    • vi :Application Specific value
    • ¯ci : Energy requirement to download the ADU from the network
  • ¯ci : vector , the estimated energy expenditure of node j when ADU i is retrieved.
  • Assume : ADUs are of uniform size and that nodes have sufficient flash storage to buffer collected signals.
lance system architecture1
Lance System Architecture
  • Energy model:
    • Cost for downloading the ADU.
    • Cost to nodes that overhear transmissions by nodes participating in the transfer.
  • a priori assumption : Battery capacity C joule
  • Lifetime target : L (each node)
  • discharge rate : no more than C/L (joule/per unit time)
  • High Level Goal :
    • Download the set of ADUs that maximizes the total value, subject to the lifetime target.
  • epoch duration ∆
  • : Multidimensional knapsack problem
lance system architecture2
Lance System Architecture
  • Design Principle
    • Decouple mechanism from policy
    • Simplicity through centralized control
    • Low cost for maintenance traffic
  • System Overview:
lance system architecture3
Lance System Architecture
  • Two application-provided components
    • Summarization Function (node) : Local Information
    • Chain of Policy Module (base) : Global Information
  • Constraint on Summarization Function
    • Small Summary (a few bytes)  limits the overhead for storing and transmitting
    • Run Efficiently  as ADUs are sampled
  • Example: Seismic events
    • Commonly used measure : RSAM(Real-Time Seismic Amplitude Measurement)
lance system architecture4
Lance System Architecture
  • Cost Estimation
    • Compute Download Energy cost vector ¯ci for each ADU sampled by the network.
    • Assumption : Spanning tree topology rooted at base station.
    • Cost Function (factor) = (reliable transmission protocol )+ (node’s position in routing path)+ (radio link quality) + (MAC protocol)
  • Complex Dynamics in Sensor Network
  • Empirical Model—Three primitive energy cost:
    • Ed :reading data from flash + sending multiple radio pkt (including retransmit) to next hop
    • Er: intermediate node (forward message)
    • Eo: overhear transmission
lance system architecture5
Lance System Architecture
  • Lance Optimizer
    • Scheduling ADUs for download
    • Reliable Transmission Protocol—Fetch or Flush
  • Adhering life target L, maximize the values of ADUs retrieved.
  • Greedy heuristic approximation of the multidimensional knapsack.
  • Procedure
    • Step 1. Exclude ADUs without enough energy to perform a download.
    • Step 2. Determine next ADU to download (Scoring Function)
  • Scoring Function
    • 1. Value Only
    • 2. Cost Total
    • 3. Cost Bottleneck
policy module
Policy Module
  • Application-supplied function : Input ADUs
  • Produce new ai’ with a possibly modified value vi’
  • Linear chain of policy modules m1,m2,m3…
  • Standard tool kit of policy module

1. Value Thresholding

2. Value Adjustment and noise removal

3. Value Dilation

4. Correlated Event Detection

5. Cost Based Filtering

case study
Case Study
  • Geophysical monitoring
    • Volcano monitoring at Reventador
    • Seismic and acoustic data at 100 Hz per channel with a resolution of 24 bits/sample
  • Deficiencies
    • 1. System could not prioritize certain events over others.
    • 2. Following each trigger, the network initiated a nonpreemptive download from every node in the network in a round-robin fashion.
    • 3. No attempt to manage energy.
  • Adaptation to Lance
    • Node-level event detector  ADU summarization function
    • Base Station  Lance’s optimizer and policy modules
case study1
Case Study
  • Exponentially weighted moving averages (EWMA) of the seismic signal.
    • Short term average and Long term average
  • Ratio of two averages  Summarization Function
  • Filter, Correlated, and SpacespreadPolicy module
  • Report max-ratio over ADU allowing Lance to prioritizing different events.
  • Download management is value-driven rather than FIFO
    • Avoiding the nonpreemptive download
  • TinyOS 2.x for TMote Sky and iMote2 sensor nodes.
  • 1 MB flash memory (ST M25P80) divided into 16 sectors of 64 KB each.
  • Per ADU/ 64KB each
  • Collection Tree Protocol (TinyOS 2.0)
    • Nodes send a periodic storage summary to the base station.
    • Reliability consideration : last 5 ADUs / each summary
  • “Fetch” reliable transfer protocol
  • Simulator and Synthetic Data Set
  • Optimal solution : Maximize data value subject to bandwidth and energy constraints.
  • Optimality : the fraction of the data value downloaded by Lance compared to the optimal solution.
  • MoteLab
    • 10-node linear topology
    • 25-node realistic tree topology
  • Download speeds : Based on empirical measurements.
  • Three value distributions are used:
    • (1). Uniform random
    • (2). Exponentially distributed
    • (3). Zipf with exponent α= 1.
  • 10-node linear topology with exponentially-distributed ADU values.
  • Different lifetimes and value distributions, run on the 25-node tree topology.
  • Bandwidth adaptation
    • 25-node tree topology
    • cost-bottleneck scoring function
    • target lifetime at 8 months
  • Stress the system in a realistic setting subject to
    • radio interference and congestion
    • exercise the multihop routing protocol
    • Fetch reliable data-collection protocol
    • ADU summary traffic generated by the nodes
    • cost-bottleneck scoring function.
  • Tungurahua Volcano
  • Lance was used to manage the bandwidth resources
  • Seven of the nodes were deployed in a three armed “star” topology radiating away from a central hub node
  • RSAM-based summarization
    • Sensitive to DC bias(causing Lance to generally prefer downloading ADUs from one or two nodes (those with the largest positive bias).)
  • Introduce Policy Module
    • computing the median RSAM
    • subtracting the median
  • Wide range of application-specific resource management policies.
  • Lance achieves near-optimal data retrieval under a range of energy and bandwidth limitations, as well as varying data distributions.
  • Study the use of more sophisticated node-level data processing, including feature extraction, adaptation to changing energy availability, and data summarization.