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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


Outline
Outline

  • Introduction

  • Lance System Architecture

  • Policy Module

  • Case Study

  • Implementation

  • Evaluation

  • Deployment

  • Conclusion


Introduction
Introduction

  • 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.


Introduction1
Introduction

  • 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


Implementation
Implementation

  • 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


Evaluation
Evaluation

  • 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.


Evaluation1
Evaluation

  • 10-node linear topology with exponentially-distributed ADU values.


Evaluation2
Evaluation

  • Different lifetimes and value distributions, run on the 25-node tree topology.


Evaluation3
Evaluation

  • Bandwidth adaptation

    • 25-node tree topology

    • cost-bottleneck scoring function

    • target lifetime at 8 months


Evaluation4
Evaluation

  • 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.




Deployment
Deployment

  • 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



Deployment2
Deployment

  • 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


Conclusion
Conclusion

  • 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.


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