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Data Gathering Tours in Sensor Networks. Alexandra Meliou, David Chu, Carlos Guestrin, Joe Hellerstein, Wei Hong. Fundamental Task. Basic Task: Data gathering. Challenge: Limited Resources. Goal: Minimize energy consumption. Focus: Selective Data Gathering. Motivating examples:

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data gathering tours in sensor networks

Data Gathering Tours in Sensor Networks

Alexandra Meliou, David Chu, Carlos Guestrin, Joe Hellerstein, Wei Hong

fundamental task
Fundamental Task

Basic Task: Data gathering

Challenge: Limited Resources

Goal: Minimize energy consumption

focus selective data gathering
Focus: Selective Data Gathering
  • Motivating examples:
    • Specified area of interest
    • Model driven data acquisition (BBQ) [Deshpande et al, VLDB’04]
  • Standard approach:

Flooding, tree-based

selective data gathering with flooding
Selective data gathering with flooding

More nodes participate in communication than necessary

Flooding-based methods are good when we are interested in a large portion of the nodes

For more selective queries, we need more specialized retrieval mechanisms

a better alternative
A better alternative

Less nodes participate in communication

problem statement














Problem Statement
  • Given:
    • a network graph
    • link qualities for every edge

(represented by cost function c(.))

    • a data gathering query
  • Find:
    • Least expensive communication strategy, which:
      • Propagates the query to all nodes of interest
      • Retrieves all required measurements
  • Centralized knowledge of the network topology
  • Nodes do not maintain neighbor tables


Number of links

Success probability variance

query propagation data gathering
Query Propagation & Data Gathering
  • In previous work treated as 2 separate phases
  • But: Can be done simultaneously


simple solution a simple tour
Simple Solution: A simple tour

Optimal Solution: A splitting tour

minimum splitting tour problem
Minimum Splitting Tour Problem
  • The splitting tour with the minimum cost is optimal
  • Properties:
    • Spans all nodes of interest (red nodes)
    • Is strongly connected
      • For every node, there needs to be a path from the basestation to that node, and a path from the node to the basestation.
problems with splitting tours
Problems with Splitting Tours
  • Theorem: Solving the problem is NP-hard
  • Tricky to implement:
    • Merges require complicated waiting rules
simplifying the problem
Simplifying the problem
  • Relevance to a familiar problem
    • Traveling Salesman Problem (TSP)!!!
  • Advantages:
    • Simpler
    • Easy to implement
    • Well studied
  • Problem:
    • CostST ≤ CostTSP

Cost of the optimal TSP Solution

Cost of the optimal Splitting Tour

tsp vs splitting tour
TSP vs Splitting Tour
  • So… TSP is always worse than the optimal Splitting Tour: CostST ≤ CostTSP
  • But never too much worse:

Theorem: CostTSP ≤ 1.5CostST

  • Constant factor approximation!


  • TSP still NP-hard…
a polynomial algorithm




A Polynomial Algorithm
  • Christofides approximation algorithm gives a tour with cost Cost ≤1.5 CostTSP
  • A naïve bound would be:

Cost ≤ 1.5*1.5 CostST= 2.25 CostST

  • But we can get an even better bound of:

Cost ≤ 1.75CostST

  • Constant factor approximation and polynomial!
practical issue i
Practical Issue I
  • Packets are finite
    • Cannot fit all the information
  • Heuristics:
    • Using multiple packets
    • Cutting tours into smaller ones
      • Cutting is performed with dynamic programming
    • Hybrid: using both the above approaches
simulations big tours
Simulations: Big Tours


multiple packets


infinite packet

Using hybrid achieves performance very close to optimal with reasonably small packet size

practical issue ii
Practical Issue II
  • Assumed semi-static topology
  • But node and link failures will still occur
  • Recovery algorithms:
    • Backtracking
    • Local flooding

Nodes need to maintain the ID of the previous hop for the query duration

Cannot deal with disconnected components in the path caused by multiple failures

local flooding
Local Flooding

Worst case is full flood

Performance depends on exact protocol (see paper for details)

tossim recovery
TOSSIM: Recovery

Simple backtracking surprisingly effective

Backtracking (5% failures)

Backtracking (5% failures)

Flooding (5% failures)

Flooding (5% failures)

Backtracking (10% failures)

Backtracking (10% failures)

Flooding (10% failures)

Flooding (10% failures)

Backtracking (15% failures)

Backtracking (15% failures)

Flooding (15% failures)

Flooding (15% failures)

  • Combined the tasks of query propagation and data gathering
  • Suggested splitting tours as a communication strategy
  • Proved hardness
  • Provided polynomial constant factor approximations
  • Addressed practical issues (oversize tours, reliability)