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 Policy Module Case Study Implementation Evaluation Deployment Conclusion

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 constrained radio 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 than 8 Kbps for a single transfer over multiple hops.

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 ADU : Unit of data storage and retrieval Each unique a i consists of a tuple {i, n i, t i, d i, v i, ¯c i } – i : ADU identifier – n i : The node storing the ADU – t i : Timestamp – d i : Raw sensor data – v i :Application Specific value – ¯c i : Energy requirement to download the ADU from the network ¯c i : 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 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 Architecture Design Principle – Decouple mechanism from policy – Simplicity through centralized control – Low cost for maintenance traffic System Overview:

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 Architecture Cost Estimation – Compute Download Energy cost vector ¯c i 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: – E d :reading data from flash + sending multiple radio pkt (including retransmit) to next hop – E r : intermediate node (forward message) – E o : overhear transmission

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 Application-supplied function : Input ADUs Produce new a i ’ with a possibly modified value v i ’ Linear chain of policy modules m 1,m 2,m 3 … 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 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 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 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 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.

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

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

Evaluation Bandwidth adaptation – 25-node tree topology – cost-bottleneck scoring function – target lifetime at 8 months

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.

Evaluation

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

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 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.