Preference-Aware Edge Server Placement in the Internet of Things

Yuanyi CHEN, Yihao LIN, Zengwei ZHENG*, Peng YU, Jiaxing SHEN, Minyi GUO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

18 Citations (Scopus)


While it is well understood that edge computing can significantly facilitate IoT-related applications by deploying edge servers close to IoT devices, it also faces many challenges with numerous IoT devices connected and interacted. One of the most important issues is how to efficiently deploy edge servers under a certain budget with the explosive growth of data scale and user base. Existing studies for edge server placement fail to consider user's query preferences since individual users may be interested in events in particular regions and are keen to receive up-to-date data streams that originate in regions of interest. In this article, we present a preference-aware edge server placement approach that offers better workload distribution in terms of both minimizing query latency and balancing the load of edge servers. To achieve this, we formulate edge server placement with multiobjective optimization as a p -center problem and design two progressive approaches. We first propose quadratic integer programming (QIP) for small-scale data sets. Since the p -center problem is an NP-hard problem, we thus propose a heuristic algorithm named TAKG (TAbu search with K -means and Genetic algorithm) for large-scale data sets. To evaluate the utility of the proposed models, we have conducted a comprehensive evaluation on a large data set that is collected by more than 1900 IoT devices during 30 days. Experimental results indicate our approaches outperform all baselines significantly in terms of both query latency and load balancing.

Original languageEnglish
Pages (from-to)1289-1299
Number of pages11
JournalIEEE Internet of Things Journal
Issue number2
Early online date11 May 2021
Publication statusPublished - 15 Jan 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61802343 and Grant 62072402; in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LGF19F020019 and Grant LGN20F020003; in part by the Hangzhou Science and Technology Bureau under Grant 20191203B37; and in part by the Intelligent Plant Factory of Zhejiang Province Engineering Lab.


  • Edge server deployment
  • Internet of Things (IoT)
  • multiobjective optimization
  • query latency
  • user preference


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