IEEE Access (Jan 2019)

Reducing Delay and Maximizing Lifetime for Wireless Sensor Networks With Dynamic Traffic Patterns

  • Qiang Wang,
  • Wei Liu,
  • Tian Wang,
  • Ming Zhao,
  • Xiong Li,
  • Mande Xie,
  • Ming Ma,
  • Guoping Zhang,
  • Anfeng Liu

DOI
https://doi.org/10.1109/ACCESS.2019.2918928
Journal volume & issue
Vol. 7
pp. 70212 – 70236

Abstract

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Fast and energy efficient data collection is the most important function of wireless sensor networks (WSNs) which governs the performance of WSNs. Time division multiple access (TDMA) is an effective data collection strategy in which node only schedules the active slot to save energy when transmitting data. However, many previous studies are designed for static traffic pattern such that each node to generate one data packet in a data collection cycle. Moreover, all nodes in the network use the same transmission radius r, so that the network data collection has a larger delay, a lower lifetime, and energy utilization rate. In view of the shortcomings of previous research, this paper proposes a reducing delay and maximizing lifetime (RDML) data collection strategy for WSNs with dynamic traffic patterns. The main innovation point of our work is the data transmission radius is no longer unified as r, while some nodes with energy surplus use a data transmission radius of mr(m > 1). The following network performance can be achieved by adopting the RDML scheme. First, because the nodes in the near sink mr radius area can send data directly to sink, the amount of data carried by hotspots nodes in the original strategy is greatly reduced, and the lifetime is improved. Second, since some nodes can go mr distance to the sink in data transmission, thus accelerating the data routing and reducing the time needed for data collection. Finally, since the remaining energy of nodes is fully utilized to increase the radius of data transmission, the energy utilization rate can be effectively improved. Through the theoretical analysis and experimental results, the RDML scheme can reduce the maximum delay of data collection by 22.32% and improve lifetime as well as the energy utilization rate by 0.86%-84.40%, and 23.33%-88.00%, respectively, compared with previous strategies.

Keywords