Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

M. Cattani, A. Loukas, Marco Zimmerling, Marco Zuñiga Zamalloa, K.G. Langendoen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)


Opportunistic routing protocols tackle the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty-cycled to save energy and the topology changes unpredictably due to node mobility and/or link dynamics. Unlike protocols that maintain a routing structure, in opportunistic protocols nodes forward packets to any neighbor that wakes up first, reducing latency and energy costs and increasing the resilience to network dynamics.
We claim the performance of existing opportunistic routing protocols can be improved while retaining their resilience by harnessing the synergy between duty cycling and opportunistic forwarding. To prove this claim, we present Staffetta, the first practical duty-cycle adaptation scheme for opportunistic low-power wireless protocols. Staffetta dynamically adapts each node's wake-up frequency to its current forwarding cost, so nodes closer to the sink become more active than nodes farther away. In this way, Staffetta biases the forwarding choices toward the sink as the neighbor waking up first is also likely to offer high routing progress. Experiments on two testbeds with four different opportunistic routing mechanisms demonstrate that Staffetta achieves severalfold performance improvements compared with a fixed wake-up frequency. As a case a point, Staffetta enables ORW, the state-of-the-art opportunistic routing protocol, to reduce end-to-end packet latency by 79-452 × and energy consumption by 2.75-9× while increasing packet delivery ratio compared with ORW's default link-layer settings.
Original languageEnglish
Title of host publicationProceedings of the 14th ACM Conference on Embedded Network Sensor Systems, SenSys 2016
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages14
ISBN (Print)978-1-4503-4263-6
Publication statusPublished - 2016
Event14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 - Stanford, United States
Duration: 14 Nov 201616 Nov 2016


Conference14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Country/TerritoryUnited States
Internet address


  • Duty Cycling
  • Data Collection
  • Opportunistic Routing


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