Table Of ContentInternet of Things
Franco Cicirelli
Antonio Guerrieri
Andrea Vinci
Giandomenico Spezzano Editors
IoT Edge
Solutions
for Cognitive
Buildings
Internet of Things
Technology, Communications and Computing
SeriesEditors
GiancarloFortino,Rende(CS),Italy
AntonioLiotta,EdinburghNapierUniversity,SchoolofComputing,Edinburgh,UK
The series Internet of Things - Technologies, Communications and Computing
publishesnewdevelopmentsandadvancesinthevariousareasofthedifferentfacets
oftheInternetofThings.Theintentistocovertechnology(smartdevices,wireless
sensors, systems), communications (networks and protocols) and computing (the-
ory, middleware and applications) of the Internet of Things, as embedded in the
fieldsofengineering,computerscience,lifesciences,aswellasthemethodologies
behind them. The series contains monographs, lecture notes and edited volumes
in the Internet of Things research and development area, spanning the areas of
wireless sensor networks, autonomic networking, network protocol, agent-based
computing,artificialintelligence,selforganizingsystems,multi-sensordatafusion,
smartobjects,andhybridintelligentsystems.
Indexing:InternetofThingsiscoveredbyScopusandEi-Compendex**
Franco Cicirelli • Antonio Guerrieri •
Andrea Vinci • Giandomenico Spezzano
Editors
IoT Edge Solutions for
Cognitive Buildings
Editors
FrancoCicirelli AntonioGuerrieri
ICAR-CNR ICAR-CNR
Rende,Cosenza,Italy Rende,Cosenza,Italy
AndreaVinci GiandomenicoSpezzano
ICAR-CNR ICAR-CNR
Rende,Cosenza,Italy Rende,Cosenza,Italy
ISSN2199-1073 ISSN2199-1081 (electronic)
InternetofThings
ISBN978-3-031-15159-0 ISBN978-3-031-15160-6 (eBook)
https://doi.org/10.1007/978-3-031-15160-6
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AG2023
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Preface
TheevolutionoftheInternetofThings(IoT)technologiesandArtificialIntelligence
techniques has introduced a new paradigm shift from smart buildings to cognitive
buildings(CBs).CBsareabletoexploitdecentralizedarchitectureswhereanalytical
processingandcognitivebehaviorscanbeoperatedbothattheedgeandinthecloud
so as to exploit the advantages of both of them. Besides the well-known benefits
providedbythecloud,edgecomputingenablesreal-timeintelligenceattheedgeof
the network and greater agility of control while, at the same time, avoiding heavy
communication traffic. By embedding intelligence on edge devices, the cognitive
buildingscanbemoreresponsivetouserpreferencesandneeds.
CBsareenvironmentsaugmentedwithsensorsandactuatorsthatexploittheIoT
paradigmandcognitiveabilitiesaltogether.Theyareabletolearn,reason,adapt,and
cooperate with each other to undertake context-dependent actions. A cornerstone
characteristic of cognitive buildings is represented by the ability to collect and
analyze sensor data and information coming from user habits to manage building
resourcesandspacesefficiently.
CBscanmaketheiroccupantsmorecomfortable,productive,andhealthy.They
can sense their environments and also identify problems before they occur. They
also combine detailed facility management capabilities and cognitive computing
to drive toward better-managed buildings. Making cognitive a building can save
energy, optimize spaces, and improve safety and security, while also allowing for
customizationsthatsuiteachoccupant’sneeds.
This book aims to offer a broad overview of cognitive buildings and gives
insight into platforms, solutions, and applications in this field. In particular, the
bookmainlyfocusesontopicssuchas:(i)Self-learningandadaptiveenvironments;
(ii) Thermal, visual, and air-comfort management systems; (iii) Efficient energy
management; (iv) Human-in-the-loop systems; (v) Analysis of building dwellers’
needs,requirements,andnormativeregulations.Abriefintroductiontothechapters
isprovidedbelow.
The Chapter “COGITO: A Platform for Developing Cognitive Environments,”
by Marica Amadeo, Franco Cicirelli, Antonio Guerrieri, Giuseppe Ruggeri, Gian-
domenicoSpezzano,andAndreaVinci,introducestheconceptofCBsandoutlines
v
vi Preface
thechallengesrelatedtothedevelopmentofsuchsystems.Afterthis,theCOGITO
platformisintroducedandpresentedasanenablingtechnologyforCBdesignand
implementation. COGITO is an agent-based IoT platform tailored to the develop-
ment of CBs in a heterogeneous continuum computing environment comprising
cloud,fog,andedgeresources.Thepracticaluseoftheplatformisdemonstratedin
thechapterthroughthediscussionofsomeusecasesdevelopedattheICAR-CNR
headquartersatRende(Italy).
The Chapter “Cloud, Fog and Edge Computing for IoT-Enabled Cognitive
Buildings,” by Erdal Ozdogan, starts with the idea that, in order to implement
CBs and harness their potential, it is essential to exploit technologies such as the
InternetofThings,cloudcomputing,fogcomputing,andedgecomputing.So,inthis
chapter,thesekeyconceptsarediscussed.Inaddition,amodulardesignframework
for CBs is also proposed in the manuscript, and in accordance with the proposed
framework,somesamplescenariosarefinallypresented.
The Chapter “Edge Caching in IoT Smart Environments: Benefits, Challenges
and Research Perspectives Towards 6G,” by Marica Amadeo, Claudia Campolo,
GiuseppeRuggeri,andAntonellaMolinaro,overviewstheliteraturerelatedtoedge
cachingforIoTCBsandidentifiesthemostpromisingdecisionpoliciesforcaching
together with its key benefits and open challenges. In particular, conventional
cachingtechniquesarefirstscanned,beforedelvingintomoredisruptivein-network
caching solutions built upon the Named Data Networking (NDN) paradigm. The
focus of the chapter is then on the possible interplay of NDN-based edge caching
policieswithSoftwareDefinedNetworking(SDN),aswellasontheopportunities
toleverageedgecachingpoweredbyAItechniquesasaprominentsixth-generation
(6G)enabler.
The Chapter “Needs Analysis, Protection, and Regulation of the Rights of
Individuals and Communities for Urban and Residential Comfort in Cognitive
Buildings,”byGiovannaIacovone,GabriellaCerchiara,LuciaCappiello,Giordana
Strazza, Emanuela Sangiorgio, and Danila D’Eliso, presents a research work that
has seen the contribution of jurists, geographers, engineers, and anthropologists
who have jointly used their competencies to support the design of CBs. The
aim is to make exportable the research results related to the knowledge of the
regulatoryframeworkonsustainablelivingandtheefficientuseofenergyinliving
spaces.Thechapterfocusesontherelationshipbetweenpeople,livingspaces,well-
being,andtechnology.Allofthisallowstoidentifyandanalyzecustomers’needs,
translatingthemintoasetofvariablesandparametersessentialtotheex-antedesign
and ex-post evaluation of a CB. Finally, the chapter introduces a supranational
(international) and national normative analysis of innovative technological models
fortheimprovementofcomfort.
The Chapter “Real Case Studies Towards IoT-Based Cognitive Environments,”
byAntonioFrancescoGentile,focusesonseveralapproachesusedinsomeItalian
researchprojectswiththeaimofensuringeffectivecommunicationsinthecontext
ofCognitiveIoTEnvironments,ingeneral,andCBsinparticular.Theapproaches
above have been applied in some real case studies. These case studies overcome
thechallengesrelatedtotheheterogeneityofcommunicationprotocols,scalability
Preface vii
(both geographic and in terms of connected nodes), security, and robustness. In
particular, the considered scenarios comprehend the realization of some cognitive
IoTinfrastructuresrelatedtosmartstreets,smartbuildings,andsmartoffices.
TheChapter“AudioAnalysisforEnhancingSecurityinCognitiveEnvironments
ThroughAIontheEdge,”byMarcoAntonioMauro,aimstoshowasecuritysystem
forCBsbasedonmicrophones.Inparticular,thechapteranalyzes theinformation
content of raw recording data obtained from microphones and their processability
into audio events, with detailed, actionable human-readable information. The
chapter also proposes a completely edge-based processing approach with special
safeguards for data filtering and information control. All of this is to obviate
any privacy concerns that might arise. Finally, the chapter introduces a complete
implementation of the proposed system applied in two case studies, namely, a
residentialapartmentandafreeaccessroom.
The Chapter “Aggregate Programming for Customized Building Management
and Users Preference Implementation,” by Giorgio Audrito, Ferruccio Damiani,
Stefano Rinaldi, Lavinia Chiara Tagliabue, Lorenzo Testa, and Gianluca Torta,
first of all introduces the eLUX Lab. The eLUX lab, at the Smart Campus of the
University of Brescia (Italy), is the first Italian CB where educational spaces are
monitored, and dashboards promote users’ awareness. There, a fixed IoT network
allowsgatheringdatatoperformanalyticsforpromptfaultdetectionandfine-tuning
oftheenvironmentalconditionsand,possibly,operatingenergymanagement.Then,
the chapter shows how the eLUX Lab can be enhanced to support the aggregate
programming paradigm for offering resilient distributed services (exploiting a
real-time location system) that run on wearable devices without relying on the
connectiontoacentralserver.
TheChapter“IoTControlBasedSolarShadings:AdvancedOperatingStrategy
toOptimizeEnergySavingsandVisualComfort,”byFrancescoNicoletti,Cristina
Carpino,andNataleArcuri,involvesthedevelopmentofanadvancedsolarshading
control algorithm with the aim of reducing energy requirements and improving
visual comfort. The proposed control system is based on IoT devices that sense
the environment and interact with it following real-time intelligence that allows
adaptation to changing situations. The designed control strategy is aimed at
adjusting the tilt angle of movable Venetian blinds to take the greatest advantage
of natural light in the presence of occupants, avoiding glare, and ensuring energy
savings.Thestudyintegratestheuseofanartificiallightmanagementsystem,which
is necessary to reach the setpoint illuminance. The results show that the control
systemcanhalvecoolingenergydemandanditcanreducetheelectricityusedfor
artificiallighting.
The Chapter “Room Occupancy Prediction Leveraging LSTM: An Approach
for Cognitive and Self-Adapting Buildings,” by Simone Colace, Sara Laurita,
GiandomenicoSpezzano,andAndreaVinci,aimstodevelopadata-drivenmodelfor
occupancypredictionusingmachinelearningtechniquesbasedonacombinationof
temperature,humidity,CO2concentration,light,andmotionsensors.Theapproach
is designed and realized in a real scenario by leveraging the COGITO platform.
TheexperimentalresultsshowthattheproposedLongShort-TermMemoryneural
viii Preface
network is well suited to account for occupancy detection at the current state
and occupancy prediction at the future state with good detection rates either in a
simulated scenario, using a dataset known in the literature, or in a real one. These
outcomes indicate the ability of the proposed model to monitor the occupancy
informationofspacesbothinareal-timeandinashort-termway.
TheChapter“EdgeIntelligenceAgainstCOVID-19:ASmartUniversityCampus
Case Study,” by Claudio Savaglio, Giandomenico Spezzano, Giancarlo Fortino,
Mario Alejandro Paguay Alvarado, Fabio Capparelli, Gianmarco Marcello, Luigi
Rachiele,FrancescoRaco,andSamanthaGenovevaSanchezBasantes,”presentsan
example of a CB environment denominated Smart Cafeteria. It is a highly sensor-
and-actuators-augmented environment, aimed at monitoring the users’ presence in
order to detect those dangerous situations for COVID-19 virus spreading. Driven
by the development guidelines of the ACOSO-METH methodology, the Smart
Cafeteria exploits a set of heterogeneous edge devices, IoT technologies, cloud
services, and neural networks for acquiring, gathering, analyzing, and predicting
temperature and humidity values, since the latest studies have recently suggested
thatcold,dry,unventilatedaircontributestovirusestransmission,especiallyinthe
winter season. The Smart Cafeteria has been designed within the campus of the
UniversityofCalabria,inItaly.
TheChapter“StructuralHealthMonitoringinCognitiveBuildings,”byRaffaele
Zinno, Giuseppe Guido, Francesca Salvo, Serena Artese, Manuela De Ruggiero,
Antonio Francesco Gentile and Alessandro Vitale, tries to monitor the health
status of real buildings through the joint use of IoT, structural health monitoring,
and artificial intelligence techniques. In such a way, a building becomes a CB
able to autonomously furnish information about its health. The use of the above
techniquesallowsforidentifyingdamagesandanomaliesinCBstructures’behavior
and implementing early warning systems. In this case, the use of accelerometric
sensorsiskeyforidentifyingtherepresentativeparametersofthebuildingstructural
behavior. All of this helps determine precious information comprehending, for
example,damagelocation,damageassessment,anddamageprediction.Thechapter
also presents a case study to highlight how the proposed approach applies to real
cases.
The Chapter “Development of Indoor Smart Environments Leveraging the
InternetofThingsandArtificialIntelligence:ACaseStudy,”byMicheleDeBuono,
NicolaGullo,GiandomenicoSpezzano,AndreaVennera,andAndreaVinci,focuses
on the development of an IoT application based on the COGITO platform for the
intelligent management of meeting rooms in the context of CBs. By processing
data collected from a set of IoT devices, cameras, and cognitive microphones, the
developed prototype is able to autonomously monitor and make decisions about
aspects that continuously affect environmental comfort, event management, and
assessment of compliance with anti-contagious regulations. After reviewing the
state of the art, the chapter describes the developed application. It highlights the
features that turn a meeting room into a cognitive environment that is highly
comfortableforusersandeffectiveinmanagingeventssuchasmeetingsorlectures.
Preface ix
TheChapter“Human-CenteredReinforcementLearningforLightingandBlind
Control in Cognitive Buildings,” by Emilio Greco and Giandomenico Spezzano,
presents a human-centered reinforcement learning controller for visual comfort
management in CBs. A satisfaction-based visual comfort model is coupled with a
reinforcementlearning(RL)algorithmtoadapttheboundariesofthecomfortzone
in the presence of a group of occupants. Compared with more traditional control
techniques,theproposalispersonalizedandhuman-centricsinceusers’perceptions
of the surrounding environments are explicitly exploited in the RL feedback loop.
Acasestudyofanofficeroomanditsperformanceisalsopresented.
TheChapter“IntelligentLoadSchedulinginCognitiveBuildings:AUseCase,”
by Franco Cicirelli, Vincenzo D’Agostino, Antonio Francesco Gentile, Emilio
Greco, Antonio Guerrieri, Luigi Rizzo, and Giuseppe Scopelliti, proposes a case
study in which a load scheduling for CBs of in-home appliances is used. In the
last few years, in fact, many appliances are spreading into our houses and are
dailyused.Suchequipmentsignificantlyimprovesthequalityoflifeofpeople,but
theiruse,whennotwellregulated,canbringaneedlessincrementintheelectricity
bill. Such an increment could be mitigated by using cognitive scheduling policies
that guide the users toward correct exploitation of electric devices so optimizing
their use while, at the same time, saving energy, money, and time. Such a case
study,implementedinthecontextoftheCOGITOproject,isdevotedtocognitively
scheduling electric loads in houses according to user preferences, self-produced
energy,andvariableenergycosts.
The Chapter “Cognitive Systems for Energy Efficiency and Thermal Comfort
in Smart Buildings,” by Luigi Scarcello and Carlo Mastroianni, exploits cognitive
technologies, based on Deep Reinforcement Learning (DRL), for automatically
learning how to control the HVAC system in a CB office room. The goal is to
developacyber-controllerabletominimizeboththeperceivedthermaldiscomfort
and the needed energy. The learning process is driven through the definition of
a cumulative reward, which includes and combines two reward components that
consider, respectively, user comfort and energy consumption. Moreover, a human
reward,inferredbythefrequencywithwhichauserinteractswithanHVACsystem,
helps the DRL controller to learn users’ requirements and readily adapt to them.
Simulationexperimentsareperformedtoassesstheimpactthatthetwocomponents
of the reward have on the behavior of the DRL controller and on the learning
process.
Rende,Cosenza,Italy FrancoCicirelli
AntonioGuerrieri
AndreaVinci
GiandomenicoSpezzano
