Benchmarks for evaluation of wireless sensor network clustering

Nishi Gupta, Pranav M. Pawar, Satbir Jain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Wireless Sensor Networks (WSNs) require sensor nodes to collect, aggregate and transmit data to the sink when they sense an event. Clustering has been proven to be an effective technique to do this job with increased efficiency. In clustering, the network is divided into smaller regions, namely clusters, and each cluster has a Cluster Head (CH). Data from all the member nodes in a cluster is collected by CH and then forwarded to the sink after aggregation and processing. Different clustering techniques have different advantages and disadvantages, trade-offs among desired properties and have been assessed using different parameters and metrics. This paper studies various clustering techniques and draws comparisons among them based on their basic working and properties. It also signifies the importance of benchmarking in WSNs and aims to identify benchmarks for the evaluation of the clustering techniques in wireless sensor networks.

Original languageEnglish
Title of host publicationSmart Trends in Information Technology and Computer Communications - Second International Conference, SmartCom 2017, Revised Selected Papers
EditorsDharm Singh, Kalpdrum Passi, Bharat Patel, A. V. Deshpande, Malaya Nayak, Shafi Pathan, Aynur Unal
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9789811314223
StatePublished - 2018
Externally publishedYes
Event2nd International Conference on Smart Trends for Information Technology and Computer Communications, SmartCom 2017 - Pune, India
Duration: 18 Aug 201719 Aug 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference2nd International Conference on Smart Trends for Information Technology and Computer Communications, SmartCom 2017

Bibliographical note

Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.


  • Benchmarking
  • Clustering
  • Energy balancing
  • Load balancing
  • Wireless sensor networks


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