Sustainable and Efficient Data Collection from WSNs to Cloud

Tian Wang, Yang Li, Guojun Wang, Jiannong Cao, Md Zakirul Alam Bhuiyan, Weijia Jia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)


The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.

Original languageEnglish
Article number7891045
Pages (from-to)252-262
Number of pages11
JournalIEEE Transactions on Sustainable Computing
Issue number2
Publication statusPublished - 1 Apr 2019


  • data delivery
  • energy consumption
  • mobile sinks
  • Sensor-cloud
  • sustainability

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Software
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization


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