The DOI matches its Crossref record.
The 6LoWPAN architecture
Abstract
6LoWPAN is a protocol definition to enable IPv6 packets to be carried on top of low power wireless networks, specifically IEEE 802.15.4. The concept was born from the idea that the Internet Protocol could and should be applied to even the smallest of devices. The initial goal was to define an adaptation layer -- "IP over Foo" to deal with the requirements imposed by IPv6, such as the increased address sizes and the 1280 byte MTU. The final design takes the concepts used in IPv6 to create a set of headers that allow for the efficient encoding of large IPv6 addresses/headers into a smaller compressed header - sometimes as small as just 4 bytes, while at the same time allowing for the use of various mesh networks and supporting fragmentation and reassembly where needed. This paper describes some of the underlying assumptions and decision points made during the development of 6LoWPAN and how the "stacked header" concept is applied so that in using the protocol you only have to "pay for" what you use. It concludes with open problems and challenges for further development and research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Edge Machine Learning for AI-Enabled IoT Devices: A Review
Merenda, M., Porcaro, C., & Iero, D. (2020) · Sensors · doi:10.3390/s20092533 · Cited by 523
Farash, M. S., Turkanović, M., Kumari, S., & Hölbl, M. (2016) · Ad Hoc Networks · doi:10.1016/j.adhoc.2015.05.014 · Cited by 498
Edge-computing-driven Internet of Things: A Survey
Kong, L., Tan, J., Huang, J., Chen, G., Wang, S., Jin, X., Zeng, P., Khan, M., & Das, S. K. (2023) · ACM Computing Surveys · doi:10.1145/3555308 · Cited by 396
Ahmad, I., Shahabuddin, S., Kumar, T., Okwuibe, J., Gurtov, A., & Ylianttila, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916180 · Cited by 366
Privacy and Security in Internet of Things and Wearable Devices
Arias, O., Wurm, J., Hoang, K., & Jin, Y. (2015) · IEEE Transactions on Multi-Scale Computing Systems · doi:10.1109/tmscs.2015.2498605 · Cited by 337
Metadata
- Authors
- Mulligan, G.
- Venue
- Proceedings of the 4th workshop on Embedded networked sensors
- Publisher
- ACM
- Published
- 2007-06-25
- Pages
- 78-82
- OpenAlex
- W2014013723
- Cited by
- 436
- References
- 0
- FWCI
- 21.14
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The 6LoWPAN architecture. https://app.cochat.ai/papers/10.1145/1278972.1278992
- The paper
- Mulligan, G. (2007). The 6LoWPAN architecture. Proceedings of the 4th workshop on Embedded networked sensors. https://doi.org/10.1145/1278972.1278992
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
