HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

3.6k indexed citations

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This paper, published in 2004, received 3.6k indexed citations. Written by Ossama Younis and Sonia Fahmy covering the research area of Computer Networks and Communications. It is primarily cited by scholars working on Computer Networks and Communications (3.5k citations), Electrical and Electronic Engineering (2.1k citations) and Water Science and Technology (237 citations). Published in IEEE Transactions on Mobile Computing.

Countries where authors are citing HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

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This map shows the geographic impact of HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks more than expected).

Fields of papers citing HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1109/tmc.2004.41.

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