Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Computer Science > Networking and Internet Architecture

arXiv:2308.04797 (cs)
[Submitted on 9 Aug 2023]

Title:A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques

Authors:Abbas Mirzaei, Shahram Zandiyan
View a PDF of the paper titled A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques, by Abbas Mirzaei and 1 other authors
View PDF
Abstract:Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.
Subjects: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
Cite as: arXiv:2308.04797 [cs.NI]
  (or arXiv:2308.04797v1 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2308.04797
arXiv-issued DOI via DataCite

Submission history

From: Abbas Mirzaei [view email]
[v1] Wed, 9 Aug 2023 08:35:00 UTC (908 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks Using Beamforming Techniques, by Abbas Mirzaei and 1 other authors
  • View PDF
license icon view license

Current browse context:

eess
< prev   |   next >
new | recent | 2023-08
Change to browse by:
cs
cs.NI
eess.SP

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences