Load Forecasting in WiFi Access Points over the LTE Network

Hugo Marques*, Pedro M.B. Torres, Paulo Marques, Rogério Dionísio, Jonathan Rodriguez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The concept of smart cities grew with the need to rethink the use of urban spaces based on the constant technological advances and respecting sustainability. Today the urbanism and the methodologies to think about the city are changing, as citizens want more access to digital information on almost everything. Therefore, cities need to be planned and equipped with infrastructures that enable connectivity between the citizens’ devices and the digital information. This challenge raises technological problems, such as traffic management, in an attempt to guarantee fair network access to all users. Solutions based on wireless resource management and self-organizing networks are key when design the connectivity for these smart cities. This paper presents a study on forecasting the daily load of Wi-Fi city hotspots, taking also in consideration the weather conditions. This is particularly interesting to predict the network load and resource requirements needed to ensure proper quality of service is provided to the hotspot users. The study was performed in a Wi-Fi hotspot located in the city of Castelo Branco, Portugal. The results show the ARIMA model is capable of identifying and forecasting seasonality events for one week in advance including its capability to correlate the number of hotspot users with weather conditions.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages6
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


  • First section
  • Forecasting
  • Iot
  • Smart cities
  • Wi-Fi


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