A genetic algorithm for data mule path planning in wireless sensor networks

Yung Liang Lai, Jehn Ruey Jiang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


A data mule is a mobile device that can traverse a wireless sensor network field to move near stationary sensor nodes that are spatially dispersed for collecting data from them. Use of the data mule can significantly reduce energy consumption of sensor nodes compared to common multihop forwarding schemes. However, it also increases the latency of gathering data of all nodes. In this paper, under the assumptions that the data mule can sequentially move from a specific location to another specific location and that sensor nodes can adjust their radios to different power levels, we study thedata mule path planning optimization (DMPPO) problem to achieve two goals under one constraint. The two goals are (1) toplan the path for the data mule to move near every sensor node to collect data so that the data mule traversal time (or latency) is minimized, and (2) to adjust the sensor nodes transmission ranges so that the total sensor node energy consumption is minimized. The constraint is that the data mule must move near each sensor node at least once for gathering data. The DMPPO problem is a multi-objective optimization problem; it is challenging since a sensor node can shrink its transmission range to reduce energy consumption but the range shrinking requires the data mule to move more for data gathering, which incurs longer latency. We propose a genetic algorithm using heuristics to find Pareto optimal solutions to this problem. We also simulate the proposed algorithm to show its effectiveness.

Original languageEnglish
Pages (from-to)413-419
Number of pages7
JournalApplied Mathematics and Information Sciences
Issue number1
StatePublished - Jan 2013


  • Data mule
  • Genetic algorithm
  • Multi-objective optimization
  • Pareto optimal solution
  • Wireless sensor network


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