Abstract
This literature review analyzes and classifies methodological contributions that answer the different challenges faced by smart cities. This study identifies city services that require the use of artificial intelligence (AI); which they refer to as AI application areas. These areas are classified and evaluated, taking into account the five proposed domains (government, environment, urban settlements, social assistance, and economy). In this review, 168 relevant studies were identified that make methodological contributions to the development of smart cities and 66 AI application areas, along with the main challenges associated with their implementation. The review methodology was content analysis of scientific literature published between 2013 and 2020. The basic terminology of this study corresponds to AI, the internet of things, and smart cities. In total, 196 references were used. Finally, the methodologies that propose optimization frameworks and analytical frameworks, the type of conceptual research, the literature published in 2018, the urban settlement macro-categories, and the group city monitoring–smart electric grid, make the greater contributions.
Highlights
A city can define itself as intelligent when investments in social and human capital, modernization of information, communication technologies (ICT), and transport infrastructure stimulate sustainable economic growth, combined with a high quality of life and sustainable management of natural resources from participative governments [1]
Analytical framework (AF): studies that aim to organize and implement lines of inquiry to account for the object of study
The 161 selected investigations may not mention direct indicators or factors associated with artificial intelligence (AI); these solutions generate improved configurations that contribute to AI application areas, which is essential for the development of the smart city concept
Summary
A city can define itself as intelligent when investments in social and human capital, modernization of information, communication technologies (ICT), and transport infrastructure stimulate sustainable economic growth, combined with a high quality of life and sustainable management of natural resources from participative governments [1]. The international organization for standardization (ISO) provides a set of indicators that measure progress towards a smart city. They evaluate issues regarding the quality of city services, quality of life, complex risk management, and sustainable development. The administration of city services generates big data by obtaining, analyzing, and storing information and from making it freely available Analysis of these large volumes of data requires the continuously implementing methodological advances in all AI application areas. According to Chamoso et al [6], there are currently several different technological platforms that provide support to AI application areas in smart cities—examples include sentilo, smartsantander, IBM intelligent operation center, citySDK, open cities, i-SCOPE, and open source IoT platforms to name a few. All of the articles selected for the liTtehriastustruedryeviiselwimwiteerde taonaliltyezreadtuarcecforradminegd tionstchienatrifieac ocof netnrgibinuetieorninsgt.hAatllporofvthideeasrotilculteisonseslteocttehde fcohratlhleenlgiteesrpatruesrenrteevdiebwy wthersemanaratlycizteydcoacnccoerpdt,intoggteothsceirewntiitfhictchoenptrlaibtfuotrimonssththaattspurpopvoirdtetescohluntoiolongsietos, tshuechchasalIloeTn,gAesI, bplroecskecnhteadin,bcylouthdecosmaprutticnigty, kcnoonwcelepdt,getoaguettohmeratwiointh, bitghedaptalaetftoc.rms that support technBoalosegdieso,nsuthceh fiavs eIodTe,fiAnIi,tibolnocskocfhtahien,scmloaurdt cciotymcpountcinepgt, kmneonwtiloendegde ainutsoemssaiotinon, baingddtaatkainetgc.into accouBnatsetdheoncotnhceefpivtueadl effrianmitieownosrokf othf eFsigmuarret 1c,itfyuctuonrecewpot rmkecnotuiolndeedxipnansedsstihoen d1i,sacnudsstiaoknisngininthtoe apcrcoopuonstedthsetucdoyncaerpetausa. lFforarmexeawmoprkle,oof nFeigoufrtehe1,dfeufitunriteiownsortkhactonueldedesxtpoabnedetxhpeadnidsecdusissiothnes uin-Ctihtey pcoronpceopste,dtosktundoywathreaism. pFaocrt egxeanmerpalte,dobnyethoef athdevadnecfeins iotfiothnes AthIaatpnpeliecdatsioton abreeaesxipnasnodceiadl disevtheleopum-Ceintyt, cfroonmceaptp, etrospkenctoivwe ftrhaemiemdpinachtugmenaneraanteddsobcyialthceapaitdavl a[1n4c–e1s6o].f the AI application areas in social develIot pismeesnset,nftrioaml toabpreorasdpencttivhe flritaemraetdurienrheuvmiewan, bayndfoscoucsiainl gcaaplsitoalo[n14th–e16a]d. vantages of a machine learnIitnigs emsseetnhtoidal ftorberaocahdecnitythseelrivteircaetsugreroruevpie[w17,]b, ye.fgo.,cuinsintrgaffialscomonantahgeeamdevnatn,tamgaecshoifnae mleacrnhineg lperaorvniidnegs mthetahdovdanfotargeeasctho csaitvyestehrevcicoesstsgnreoeudped[1t7o],cree.agt.e, /iandatpratftfhice mheaunraisgteicms etontu, nmdaecrshtiannedl,eparrendinicgt, panrodvmidaensatgheeaandovmaanltiaesgeins mtoobsailvitey t[h18e].costs needed to create/adapt the heuristics to understand, predict, and manage anomalies in mobility [18]
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