Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and

OPEX for Smart Home Transition from a Provider’s Perspective

Nilton F. S. Seixas, Adriano H. O. Maia

, George P. Pinto

, Dhyego Tavares M. da Cruz

Bruno P. Santos

, Ivan do C. Machado

, Eduardo S. Almeida

, Frederico A. Durao

Maycon L. M. Peixoto

, Gustavo B. Figueiredo

and Cassio V. S. Prazeres

Institute of Computing, Computer Science Department, Federal University of Bahia (UFBA), Salvador, Bahia, Brazil

{nilton.seixas, adriano.maia, george.pacheco, dhyegocruz, bruno.ps, ivan.machado, eduardo.almeida, fdurao,

maycon.leone, gustavobf, prazeres}@ufba.br

Keywords:

IoT, Smart Homes, Smart Grids, CAPEX, OPEX.

Abstract:

The urgency of addressing global warming has driven global efforts to enhance energy efﬁciency and transform

energy acquisition methods. In this context, the adoption of smart technologies has gained relevance across

various domains, including smart cities and smart homes. While smart cities are often promoted through gov-

ernment initiatives, transforming conventional homes into smart homes largely depends on consumer adoption.

However, there is a signiﬁcant gap in the literature regarding the implementation costs and beneﬁts of this tran-

sition, with many studies focused on unrealistic scenarios tailored to the average American consumer proﬁle.

This study aims to ﬁll that gap by proposing a methodology to estimate the conversion of conventional homes

into smart homes, accounting for both capital expenditures (CAPEX) and operational expenditures (OPEX).

The proposed approach seeks to enable an affordable transition for a wider range of consumer proﬁles. Four

case studies are presented to demonstrate how smart systems can be integrated into homes, maximizing eco-

nomic and environmental beneﬁts for end-users. Additionally, the paper analyzes the commercial relationship

between manufacturers and smart environment providers, exploring acquisition and operational cost mod-

els. As an alternative to the traditional device-based business model, the study suggests a subscription-based

system, supported by the continuous delivery of smart solutions, promoting greater customer retention and

scalability.

1 INTRODUCTION

The emergence of smart environments has ushered in

an era where billions of devices are seamlessly con-

nected to the Internet. The current count surpasses

16 billion devices (Sinha, 2023), exceeding twice the

global population of approximately 8 billion. Inte-

grating home and street devices with 5G networks

has facilitated a profound transformation across var-

https://orcid.org/0009-0007-1739-4295

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https://orcid.org/0000-0001-9027-2293

https://orcid.org/0000-0002-9312-6715

https://orcid.org/0000-0002-7766-6666

https://orcid.org/0000-0002-4851-5228

https://orcid.org/0000-0001-9756-378X

https://orcid.org/0000-0003-0197-0909

ious sectors, including the economy, health, agricul-

ture, and education. This transformative landscape

has given rise to the Internet of Things (IoT) ﬁeld,

providing a platform to comprehensively study and

understand the intricate phenomena associated with

this interconnected web of devices.

The IoT establishes a paradigm connecting

dayling physical objects to the Internet, enabling

seamless communication. Coined by Kevin Ash-

ton (Ashton, 2009), the term ‘Internet of Things’ envi-

sions a world where these interconnected ‘things’ can

communicate, collect data, and make informed deci-

sions. The IoT provides advantages like improved re-

source utilization, increased productivity, and an en-

hanced quality of life for human communities. Mean-

while, smart environments are characterized “as one

that can acquire and apply knowledge about the en-

vironment and its inhabitants to improve their experi-

ence in that environment” (Cook and Das, 2007). In

this sense, the IoT stands as a fundamental enabler

Seixas, N. F. S., Maia, A. H. O., Pinto, G. P., M. da Cruz, D. T., Santos, B. P., Machado, I. C., Almeida, E. S., Durao, F. A., Peixoto, M. L. M., Figueiredo, G. B. and Prazeres, C. V. S.

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective.

DOI: 10.5220/0013201900003944

In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 27-38

ISBN: 978-989-758-750-4; ISSN: 2184-4976

of smart environments, including smart homes, smart

cities, and smart health, among others (Gomez et al.,

2019).

The IoT-enabled Smart Homes pose as an impor-

tant application of IoT. Their technologies have revo-

lutionized the way we interact with our living spaces,

offering a multitude of possibilities for utilizing IoT

devices (Martins et al., 2024; Jr et al., 2024). Their

innovations are pivotal in enhancing our daily lives

by monitoring and adjusting various aspects of our

homes. From measuring environmental conditions to

seamlessly overseeing household appliances and even

regulating access to our homes, Smart Homes pro-

vide security, energy efﬁciency, low operating costs,

and convenience (Campos et al., 2024). Moreover,

they possess a dynamic and ﬂexible nature, allowing

them to accommodate the evolving requirements of

the home residents. Additionally, they have an adapt-

able infrastructure capable of integrating with devices

from different providers and standards (Domb, 2019).

Although smart homes seem to be the future for

homes and despite the advantages highlighted earlier,

embracing smart home systems requires an invest-

ment in both hardware and setup (Larionova et al.,

2024). Additionally, we also face a signiﬁcant imped-

iment to their widespread adoption that lies in con-

sumers’ perception that the investment in this transi-

tion may not justify the associated costs. This percep-

tion forms a barrier, hindering the broader acceptance

of smart home solutions (de Souza Dutra et al., 2020).

Moreover, while there has been extensive scrutiny

of capital and operational expenditures (CAPEX and

OPEX) in the context of smart cities, particularly in

energy-related domains like smart lighting and grids,

research on the corresponding costs for smart homes

remains scant. Previous studies (de Souza Dutra et al.,

2020; Larionova et al., 2024) have provided prelim-

inary CAPEX/OPEX estimations for smart homes,

with estimated costs distant from the average Ameri-

can. Hence, there is a critical need for a more com-

prehensive assessment to facilitate a smooth transition

from traditional residences to smart ones. Addition-

ally, it’s essential to delve deeper into the operational

expenses associated with managing data generated by

smart home devices.

To address the aforementioned problem, this pa-

per introduces a novel methodology aimed at esti-

mating a cost-effective CAPEX for end-users looking

to upgrade their residences into smart homes, along-

side OPEX considerations for smart home providers.

These providers manage the inﬂux of messages and

API calls from smart devices, facilitating the delivery

of smart services to end-users. This approach stream-

lines the transition from conventional houses to smart

ones by eliminating the need for users to invest in ex-

pensive devices outright. The contributions of this pa-

per include: (i) methodology for estimating CAPEX

for smart end users and providers; (ii) methodology

for estimating OPEX for smart providers; (iii) intro-

duction of a new player: smart providers; (iv) four

smart home case studies; (v) suggestion of subscrip-

tion plans for monthly billing.

The remainder of this paper is organized as fol-

lows: Section 2 presents the related works. Section 3

describes our methodology. Section 4 summarizes

and discusses the results of our study cases. Finally,

Section 5 summarizes our contributions and presents

some future works.

2 RELATED WORKS

The energy consumption has become a matter of

greater importance to the world, especially after the

pandemic. The authors in (Pare

zanin, 2023) under-

scores the critical necessity for the European Union

to diminish its energy reliance on the Russian Feder-

ation. Central to this imperative is the exploration of

adopting smart grids across the twelve member states

of the bloc, a move fraught with multifaceted chal-

lenges. The authors interrogate key aspects, such as

the readiness of distributed system operators to seam-

lessly integrate services with smart meters, as well as

the feasibility of categorizing member states based on

energy usage levels, among others.

Furthermore, the authors conduct a thorough anal-

ysis of the costs and advantages linked to deploy-

ing smart meters across all member states, presented

in a detailed tabular format. Despite the growing

signiﬁcance of Smart Homes as a solution for en-

ergy conservation and reducing energetic dependency,

their absence in the discussion warrants further in-

vestigation. Subsequent subsections will explore the

CAPEX/OPEX associated with implementing smart

homes and other intelligent environments in greater

detail.

2.1 Smart Homes

The paper (de Souza Dutra et al., 2020) presents a

comprehensive framework for determining a set of

combinations of home appliances, taking into account

factors such as the structural layout of the house, local

weather conditions, pricing, and energy consumption.

The selected home appliances considered in this study

include wind turbines, photovoltaic panels, energy

storage systems, electric vehicles, heating, air con-

ditioning, and ventilation. Additionally, the authors

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

have provided a detailed cost analysis of these appli-

ances, ranging from USD 424.31 to USD 31,775.14.

While the paper introduces an intriguing method-

ology for generating appliance combinations based on

pricing considerations, it is pertinent to note that prior

research, particularly in the related works section, has

utilized mixed-integer linear programming (MILP)

techniques to yield superior results. To enhance the

credibility of the proposed framework, it is recom-

mended to compare its outcomes directly with those

obtained through MILP, thereby gauging its proxim-

ity to optimal solutions.

Furthermore, the selection of appliances for ex-

perimentation may not adequately address the transi-

tion from conventional to smart homes. The absence

of discussion regarding existing appliances within

households also raises questions about the practical-

ity and effectiveness of the smart home implementa-

tion. A more thorough examination of household ap-

pliances is imperative to facilitate a seamless transi-

tion towards smart homes, ensuring compatibility and

integration with pre-existing systems.

The paper (Larionova et al., 2024) introduces

a compelling study aimed at elucidating the poten-

tial energy and operational cost savings achievable

through the adoption of smart appliances, including

smart thermostats, lighting systems, security systems,

and HVAC units. Initially, the authors delineate the

CAPEX involved, detailing the costs associated with

each equipment, ranging from USD 250.00 to USD

1800.00, with installation and setup expenses total-

ing USD 500.00, amounting to USD 3720.00. Sub-

sequently, they present the OPEX through a tabu-

lar representation showcasing monthly energy savings

from each device under scrutiny, cumulatively reach-

ing USD 240.00.

While the paper serves as a commendable foun-

dation for assessing the costs of integrating smart ap-

pliances into conventional households, it falls short of

adequately addressing the concept of a smooth transi-

tion. Speciﬁcally, there is a notable oversight in con-

sidering the average income levels of individuals, par-

ticularly in the context of the USA, along with associ-

ated living expenses. For instance, the average salary

is noted as USD 4,537.48, while the cost of living

for a single person, excluding rent, is USD 1,170.40.

Factoring in average rent in suburban areas, the total

living expenses surge to USD 2,943.48, leaving a re-

mainder of USD 1,594.00 for discretionary spending.

This crucial aspect merits further attention to ensure a

more comprehensive evaluation of the feasibility and

practicality of transitioning to smart homes ((Zillow),

2024).

2.2 Other Smart Environments

The paper (Cacciatore et al., 2017) investigates the

cost analysis of implementing smart lighting in smart

cities, evaluating four solutions: Current implemen-

tation (CUR), Delay-based (DEL), Encounter-based

(ENC), and Dimming (DIM). These solutions involve

combinations of two types of lighting devices: Light-

Emitting Diode lamps (LEDs) and High-Pressure

Sodium (HPS) lamps. LEDs offer the capability to

adjust light intensity, whereas HPS lamps only pro-

vide on/off functionality.

In the CUR solution, traditional lamps are utilized,

operating at full intensity for a predetermined dura-

tion. The DEL solution utilizes both LED and HPS

lamps, activating at full intensity when sensors detect

people within a designated radius R and turning off

when no presence is detected within a period W. The

ENC proposal activates lights in the morning upon de-

tecting the presence and remains on for a period W,

exclusively employing HPS lamps. Lastly, the DIM

solution adjusts light intensity based on the number

of people detected within a radius R, utilizing only

LED lamps. The authors ascertain that the DIM solu-

tion, leveraging LEDs, yields the most favorable en-

ergy savings.

The study in (Yaacoub and Alouini, 2020) advo-

cates for embracing the concept of ”smart living” as

a means to counter the migration from rural to ur-

ban settings. They deﬁne smart living as the inte-

gration of advanced technologies and connectivity to

stem or mitigate the ﬂow of individuals to urban cen-

ters. Achieving this objective entails ensuring robust

connectivity in rural areas to facilitate remote work

opportunities, leveraging solar panels for sustainable

energy generation, implementing high-speed rail sys-

tems for occasional urban transit, and employing vir-

tual and augmented reality tools to enhance educa-

tional experiences for children. To support the re-

alization of these initiatives, the paper outlines the

necessary capital and operational expenditures for es-

sential infrastructure components such as microwave

towers and equipment, spectrum licenses, ﬁber optic

networks, satellite systems, and related installations.

The paper (Mehta and Eleftheriadis, 2022) intro-

duces a framework aimed at assessing the expenses

associated with deploying edge cloud resources, with

the goal of mitigating energy consumption’s effects

on the nodes within the electrical grid. This frame-

work categorizes CAPEX into two distinct categories:

active and passive equipment. Active expenditures

encompass the primary costs associated with servers,

radio equipment, and network infrastructure. Con-

versely, passive expenditures encompass expenses re-

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective

lated to building construction, procurement, and in-

stallation of power distribution systems, as well as

cooling infrastructure. Additionally, OPEX is bifur-

cated into two segments: the electricity required to

power and cool the servers and operational costs cov-

ering aspects such as security measures and mainte-

nance activities.

3 METHODOLOGY

In this section, we delineate a comprehensive method-

ology to estimate the expenses of transforming a tra-

ditional household into a Smart Environment. To

achieve this, we explore two key cost dimensions.

Firstly, we examine the perspective of the com-

pany offering Smart Environment solutions (OPEX).

This entails outlining all expenditures associated with

licensing communication channels between device

manufacturers and the new provider, the procurement

costs of sensors for resale, licensing fees for Software

Development Kits (SDKs) required for implementing

intervention applications on the devices, and cloud

hosting expenses for delivering associated services.

We also highlight the communication costs of the de-

vice with the manufacturer’s cloud, charged monthly

throughout the entire useful life of the device. Sec-

ondly, we delve into the costs from the customer’s

viewpoint, encompassing the acquisition of Smart En-

vironment solutions and projecting potential expenses

(CAPEX).

3.1 OPEX

In this subsection, we describe our approach to ana-

lyzing and calculating the costs related to operating

the devices through a Smart Environment provider.

We aimed to understand all the values involved in

the relationship between Smart Devices manufactur-

ers and the new Smart Devices provider. This work

aims to establish a guide that provides an overview of

costs for a new provider so that new entrants can ad-

equately consider the values related to CAPEX and

OPEX of their new venture, having detailed infor-

mation to avoid unforeseen circumstances and main-

tain ﬁnancial health in their new operation. Further-

more, the customer-centric aspect may offer valuable

commercial insights for composing sensor and device

sales kits or establishing a monthly service plan for re-

curring income. Additionally, it allows the customer

to assess their investment in this type of solution tan-

gibly. To carry out this study, we followed ﬁve steps,

which are described as follows.

Step 1

We researched potential Smart device manufactur-

ers and their methods of establishing partnerships

with new providers interested in commercializing

and distributing these devices.

Step 2

We selected a manufacturer to conduct the case

study and investigated the commercial and con-

tractual conditions for establishing partnerships be-

tween the manufacturer and the new provider.

Step 3

After analyzing these conditions, we identiﬁed the

related costs within two main areas. The ﬁrst area

pertains to acquiring a physical batch of speciﬁc

devices, such as power plugs, smart bulbs, robotic

vacuums, and electronic locks, among others. The

values are presented in product batches and are

based on US dollars. The second area involves

licensing for communication between the devices

and the manufacturer’s cloud. In this scenario, the

messages are charged to the provider from the mo-

ment of sale until the device is discarded by the

customer.

After selecting the manufacturer and obtaining the

devices, It is essential to highlight the associated costs

for the interactions among the device, manufacturer,

and cloud provider, considering the associated costs.

The devices typically communicate using the Mes-

sage Queuing Telemetry Transport (MQTT) protocol,

renowned for its efﬁcient transmission of data over

networks with limited resources. MQTT is favored in

IoT environments due to its simplicity in implementa-

tion and its ability to minimize data usage and energy

consumption during operation.

The MQTT protocol is used in this context to pe-

riodically report their status whenever there is any

change in the environment or conﬁguration, referred

to by the manufacturer as a message subscription. An-

other form of communication is a direct intervention

by a user or application in the device, whether to turn

it on, or off, change its conﬁguration, or request its

current status, this scenario is referred to by the man-

ufacturer as an API call.

Figure 1 provides a general overview of how

the communication scheme between the device and

the manufacturer’s cloud operates. Additionally, the

same ﬁgure illustrates how the new provider physi-

cally operates in this context.

In general, there exist charges regarding commu-

nication licensing between companies. In this anal-

ysis, the manufacturer offers three annual plans with

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

Figure 1: Device communication scheme.

ﬁxed values charged in US dollars. These plans fea-

ture ﬁxed values for speciﬁc monthly quotas. In

other words, regardless of the chosen plan, the tar-

iffed amount will remain predetermined if the quota

limit is not reached. However, if the quota limit is

exceeded, an excess fee will be charged and added

to that month’s quota. This excess fee is calculated

per million subscription messages and API calls and

is charged in US dollars. Additionally, it’s worth not-

ing that the excess fees differ between subscription

messages and API calls. The manufacturer also ap-

plies different rates between China and the rest of the

world, with lower rates in the former. This structure is

independent of the chosen plan, with only a potential

difference in value based on the selected plan.

In the monthly plans, the manufacturer under anal-

ysis initially offers a trial plan, which is solely for

experimentation purposes. In this case, the quota is

set at a small value, and when the limit is exceeded,

there is a charge for the automatic transition to the

subsequent plan. Additionally, the manufacturer re-

stricts the number and types of devices for this plan.

The intermediate plan is viewed by the manufacturer

as the bestseller and is geared towards sales consoli-

dation and structural expansion of the new provider.

The monthly quota for both subscription messages

and API calls is set at over 250 million each, aiming

to be sufﬁcient for an entry-level provider to operate

with minimal excess during their ﬁrst year. The third

and ﬁnal plan is seen by the manufacturer as corpo-

rate and features a quota approximately double that

of the intermediate plan. This plan targets an estab-

lished smart home provider with a deﬁnitive product

mix.

To determine the method for calculating the cost

per device, we ﬁrst measured the message subscrip-

tions performed by a plug-in operation in the lab for

one hour. Then, we performed the multiplications to

arrive at the monthly consumption of message sub-

scriptions, and to determine the value of the API calls;

we assumed that it was equal to the number of mes-

sage subscriptions collected.

Finally, we divided the result of these two items

by one million and multiplied it by the speciﬁc over-

age rate. The result is then added together, generating

the cost of the device in relation to the communica-

tion license per month. It is worth mentioning that

we used the overage values to establish a calculation

mechanism; however, it is important to remember that

within the plans offered, there are monthly quotas.

This cost serves the provider as part of the com-

position of the sales price, in addition to a point of

attention in the development of the sales planning and

the maintenance of the entire operation, as this cost

must be sustained by the provider throughout the en-

tire useful life of the device. In light of this, the plan-

ning should consider not only the initial sales costs

but also the ongoing expenses related to communica-

tion and maintenance, ensuring that the operation re-

mains ﬁnancially viable and sustainable. Equation 1

illustrates this calculation mechanism.

C =



SMH×24×30

1,000,000

×CS M





APIH×24×30

1,000,000

×CAPI



(1)

where C is the cost per device per month in US dol-

lars, SMH stands for the number of message sub-

scriptions per hour, CSM indicates the excess cost per

message subscription per million, APIH refers to the

quantity of API calls per hour, and CAPI is the value

of excess cost per API call per million.

Step 4

A requirement from the device manufacturer for

the new provider to interact via its application on

its smart devices is the acquisition of the SDK. This

resource is essential for the provider to effectively

abstract the manufacturer from the customer’s per-

spective, with the customer using the application

assuming that the particular device is manufactured

and provided by the provider. The cost of the SDK

is annual and based on US dollars.

Figure 2 provides a general overview of how the

communication scheme between the device and the

manufacturer’s cloud operates. It considers the adop-

tion of the SDK and cloud hosting service. To de-

termine the method of calculating the annual cost of

a provider, add up the yearly communication licens-

ing plan, the annual SDK cost, and the annual cloud

hosting cost for the new provider. The Equation 2 il-

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective

lustrates this calculation mechanism.

AnnualCost = AnnuaPlan +SDK + (Cloud × 12) (2)

Figure 2: Device communication scheme with the adoption

of the SDK and cloud hosting service.

Step 5

For the new provider to have an overview of all de-

vices within its domain, apply additional services,

or simply manage them in a global view, the acqui-

sition of cloud servers to support these services is

necessary. Therefore, in this work, we estimated a

minimum structure for adopting a platform to man-

age all devices of a new provider and sought prices

from providers such as AWS and Azure to estimate

the costs of this operation.

Important to highlight that this value may be sub-

ject to additional charges due to any monthly ex-

cesses. In this case, simply apply the mechanism of

Equation 1 for the months and add it to the annual

cost (Equation 2).

Annual Cost Exc = Annual Cost + (C × Dev Exc × Months Exc) (3)

Where the AnnualCostExc is the ﬁnal value of the

year in case there are excesses, AnnualCost is the to-

tal cost of the year without excesses, C is the cost per

device per month in US dollars, DevExc is the num-

ber of devices that had excesses, and MonthsExc is

the number of months that had excesses in the year.

In this work, we are abstracting costs related to labor,

compliance with legislation, and accounting, among

others. Thus, we highlight the more speciﬁc costs

of the commercial relationship between the manufac-

turer and the new smart environment provider.

Table 1: Symbol table for CAPEX, OPEX, and equations.

Symbol Description

CAPEX Total capital expenditure for smart devices

n Number of devices

Device

The i-th smart device

UnitCost

The cost of the i-th smart device

C Operational expenditure for communication licensing per device

SMH Number of message subscriptions per hour

CSM Excess cost per message subscription per million

APIH Number of API calls per hour

CAPI Excess cost per API call per million

24 Hours per day

30 Days per month

1, 000, 000 Normalization factor (per million)

Annual Cost Total annual cost without exceeding quota

Annual Plan The annual cost of the communication licensing plan

SDK The annual cost of the Software Development Kit (SDK)

Cloud The monthly cost of cloud hosting services

Annual Cost Exc Total annual cost with excess quota charges

C Cost per device per month (for excess calculations)

Dev Exc Number of devices that exceeded the monthly quota

Months Exc Number of months where the quota was exceeded

3.2 CAPEX

We applied a customer-centric approach in our cost

analysis to assess the feasibility of Smart Environ-

ment and Smart Homes solutions. This study was

conducted to evaluate the cost-beneﬁt for various

types of customers, considering factors such as the

size of the residence, number of rooms, family size,

and purchasing power, among others. This analysis

aims to understand the ﬁnancial investment required

by users for the transition from a conventional house

to a smart home, as well as to provide commercial

guidance for new providers in this market segment.

To carry out this task, we initially needed to de-

ﬁne usage models based on certain criteria. For this

purpose, we used data from ((Zillow), 2024) to un-

derstand the ﬁnancial potential and associated cost of

living issues for American citizens. Subsequently, we

had to consider which devices would have the great-

est utility in the context of a smart home, taking into

account the legacy structure of a conventional house.

The deﬁnition of this device acquisition structure

is important for sizing the capacity that a provider

must have to support an operation that reﬂects this

acquisition. For example, the CAPEX of all devices

acquired by all customers is calculated by summing

the total number of devices multiplied by the unit

cost within a purchase lot offered by the manufac-

turer. This equation 4 illustrates the relationship be-

tween the initial investment represented by the acqui-

sition of the device lots and the consequent number

of active devices at customers. Providers need to con-

sider CAPEX comprehensively, as these devices will

be sending messages and generating monthly costs

while they are active, potentially consuming all as-

sociated proﬁtability.

CAPEX =

∑

i=1

(Device

× UnitCost

) (4)

Following this approach, we determined that

smart plugs, light switches, and universal remotes

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

would have the highest effectiveness in terms of con-

version to a smart environment. These devices fa-

cilitate the transformation of conventional appliances

into smart ones; for example, a smart plug can auto-

mate the occasional shutdown of a freezer that might

otherwise remain on for the entire month.

Table 2: Basic smart devices costs.

Devices Required (USD)

Smart plug 17.80

Light Switch 19.80

Universal Control 17.00

Mesh router 69.00

Another key point highlighted in this analysis is

the high dependence on device connectivity, consid-

ering the constant need for message exchange with

the cloud. To address this, we researched and rec-

ommended mesh routers to meet the speciﬁc needs of

a smart home environment. Table 2 summarizes the

costs of basic devices to convert conventional houses

into smart ones.

4 STUDY CASES

In this section, we will present the cost analysis we

conducted for acquiring Smart Environment solutions

to convert a conventional house into a smart home

(CAPEX). Based on the analysis presented in Sec-

tion 3, Subsection 3.2, we deﬁned four usage models,

taking into account the utilization of the smart envi-

ronment and the quantitative aspects related to house

size and the ﬁnancial potential of American citizens.

This analysis is essential for guiding and align-

ing a provider’s operational costs, considering the mix

of different types of residences that will be explored

throughout this article. Understanding the variations

in consumption proﬁles and the speciﬁc demands of

each kind of residence enables the provider to adjust

its strategies more precisely in terms of infrastructure

and the services offered. This ensures not only a more

efﬁcient operation but also the opportunity to opti-

mize resource allocation and improve proﬁtability.

Considering this scenario, we will present the cost

perspective for the end customer and estimate the

number of residences for the development of the cost

model for a service provider in the following subsec-

tions.

4.1 End Customer View

These models will be detailed below:

Smart Environment Starter Kit: The suggested

Smart Environment starter kit is speciﬁcally designed

to meet the needs of individuals living alone or cou-

ples who are at the early stages of their ﬁnancial

journey, with a growing yet still limited purchasing

power. This proposal aims to provide an affordable

and effective home automation experience for small

to medium-sized residences, with up to 5 rooms. Ta-

ble 3 provides a detailed breakdown of this composi-

tion.

Table 3: Cost analysis of the starter model.

Smart Environment Starter Kit

Device Appliance Quantity Unit Price Total Price

TV 1 $17.80 $17.80

Refrigerator 1 $17.80 $17.80

Microwave 1 $17.80 $17.80

Washing Machine 1 $17.80 $17.80

Computer 1 $17.80 $17.80

Outlets 1 $17.80 $17.80

Plug

Fan 2 $17.80 $35,60

Light Switch Light Switch 1 $19.80 $19.80

Universal

Control

Air Conditioner -

TV -

TV Receiver

1 $17.00 $17.00

Router Mesh 1 $69.98 $69.98

Total

Equipment

$249,18

To ensure a smooth transition to a smart environ-

ment, the kit includes a variety of essential devices.

Among them are 8 smart plugs, ideal for remotely

controlling and monitoring the operation of various

household appliances efﬁciently. Additionally, a

smart switch is included to offer convenient lighting

control throughout the home. For seamless inte-

gration of devices and a uniﬁed experience, the kit

also features a universal remote, allowing control

of multiple infrared-compatible devices, such as

TVs and audio systems, through a single device.

Lastly, a mesh router is recommended to ensure

reliable and consistent Wi-Fi coverage throughout

the residence, providing a stable connection for

all smart devices. This combination of devices

is designed to deliver comfort, convenience, and

energy efﬁciency, all within an affordable budget

for those taking their ﬁrst steps into home automation.

Smart Environment Model 1: The suggestion for

Smart Environment Model 1 is speciﬁcally aimed at

families with at least 5 members, characterized by a

stabilized yet constantly expanding purchasing power.

The recommended residence for this scenario has a

maximum of 8 rooms and aims to automate at least 17

different household appliances, ensuring a smart and

efﬁcient environment. The table 4 provides a detailed

breakdown of this composition.

In this context, we recommend including 21 smart

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective

Table 4: Cost analysis of the model 1.

Smart Environment Model 1

Device Appliance Quantity Unit Price Total Price

TV 1 $17.80 $17.80

Refrigerator 1 $17.80 $17.80

Microwave 1 $17.80 $17.80

Coffee Maker 1 $17.80 $17.80

Air Conditioner 1 $17.80 $17.80

Washing Machine 1 $17.80 $17.80

Computer 1 $17.80 $17.80

Printer 1 $17.80 $17.80

Dishwasher 1 $17.80 $17.80

Plugs 5 $17.80 $89.00

Fan 1 $17.80 $17.80

Air fryer 1 $17.80 $17.80

Water ﬁlter 1 $17.80 $17.80

Stove 1 $17.80 $17.80

Freezer 1 $17.80 $17.80

TV receiver 1 $17.80 $17.80

Plug

Videogame 1 $17.80 $17.80

Switch Light Switch 3 $19.80 $59.40

Universal

Control

Air Conditioner -

TV -

TV Receiver

1 $17.00 $17.00

Mesh Router 2 $69.98 $139.96

Total

Equipment

$638.36

plugs, ideal for remote control and monitoring of

a wide variety of household appliances, providing

convenience and energy savings. Additionally, we

suggest installing 3 smart switches to facilitate

lighting management throughout the residence,

ensuring comfort and safety for the entire family.

For seamless integration and a uniﬁed experience,

the kit also includes a universal remote, allowing

convenient control of multiple infrared-compatible

devices, such as TVs, audio systems, and other

electronic devices, all from a single device. To ensure

comprehensive and stable Wi-Fi coverage throughout

the residence, we recommend including two mesh

routers, providing reliable connectivity for all smart

devices, regardless of their location in the house.

Smart Environment Model 2: The Smart Environ-

ment Model 2 suggestion is aimed at larger fami-

lies, with at least 10 individuals, characterized by

stable and deﬁned purchasing power. The recom-

mended residence for this scenario has a maximum

of 12 rooms but with a considerable area compared to

Smart Environment Model 1. Additionally, the aim

is to automate at least 17 different household appli-

ances, with redundancy in some cases, such as 3 tele-

visions. The table 5 provides a detailed breakdown of

this composition.

In this context, we recommend the inclusion of 39

smart plugs, providing remote control and monitoring

of a wide variety of appliances, ensuring convenience

and energy efﬁciency. Furthermore, we suggest

installing 8 smart switches for lighting management

and 3 universal remotes to facilitate control of

Table 5: Cost analysis of the model 2.

Smart Environment Model 2

Device Appliance Quantity Unit Price Total Price

TV 3 $17.80 $53.40

Refrigerator 1 $17.80 $17.80

Microwave 1 $17.80 $17.80

Coffee Maker 1 $17.80 $17.80

Air Conditioner 2 $17.80 $35.60

Washing Machine 1 $17.80 $17.80

Computer 2 $17.80 $35.60

Printer 1 $17.80 $17.80

Dishwasher 1 $17.80 $17.80

Plugs 16 $17.80 $284.80

Fan 2 $17.80 $35.60

Air fryer 1 $17.80 $17.80

Water ﬁlter 1 $17.80 $17.80

Stove 1 $17.80 $17.80

Freezer 1 $17.80 $17.80

TV receiver 3 $17.80 $53.40

Plug

Videogame 1 $17.80 $17.80

Switch Light Switch 8 $19.80 $158.40

Universal

Control

Air Conditioner -

TV -

TV Receiver

3 $17.00 $51.00

Mesh Router 3 $69.98 $209.94

Total

Equipment

$1095.74

entertainment devices throughout the residence. To

ensure seamless integration and comprehensive Wi-Fi

coverage, we recommend including 3 mesh routers.

This will ensure a stable and reliable connection for

all smart devices, regardless of their location in the

house.

Smart Environment Model 3: The Smart Environ-

ment Model 3 suggestion is aimed at very large fami-

lies or small businesses, such as those in the hospital-

ity industry, with signiﬁcant purchasing power. The

suggested location has a maximum of 22 rooms, with

a considerable area, and aims to automate at least 17

different household appliances, with redundancy in

some cases, such as 5 televisions. The table 6 pro-

vides a detailed description of this composition.

Within this context, we highly advocate for the

integration of 65 smart plugs, offering remote con-

trol and real-time monitoring capabilities for a diverse

range of appliances. Moreover, we propose the instal-

lation of 25 smart switches dedicated to efﬁcient light-

ing management, complemented by the addition of 5

universal remotes. These remotes serve to streamline

device control across the entire residence or establish-

ment, further enhancing the smart living experience.

Figure 3 shows a bar chart illustrating the capi-

tal expenditure (CAPEX) associated with each smart

home model: the starter kit, model 1, model 2, model

3, and model 4. Each bar represents the total cost re-

quired to implement the respective model, highlight-

ing the scalability and different investment levels of

the smart home solutions offered from the end cus-

tomer’s perspective.

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

Table 6: Cost analysis of the model 3.

Smart Environment Model 3

Device Appliance Quantity Unit Price Total Price

TV 5 $17.80 $89.00

Refrigerator 2 $17.80 $35.60

Microwave 1 $17.80 $17.80

Coffee Maker 2 $17.80 $35.60

Air Conditioner 5 $17.80 $89.00

Washing Machine 1 $17.80 $17.80

Computer 2 $17.80 $35.60

Printer 1 $17.80 $17.80

Dishwasher 1 $17.80 $17.80

Plugs 30 $17.80 $534.00

Fan 5 $17.80 $89.00

Air fryer 1 $17.80 $17.80

Water ﬁlter 1 $17.80 $17.80

Stove 1 $17.80 $17.80

Freezer 2 $17.80 $35.60

TV receiver 3 $17.80 $53.40

Plug

Videogame 2 $17.80 $35.60

Switch Light Switch 25 $19.80 $495.00

Universal

Control

Air Conditioner -

TV -

TV Receiver

5 $17.00 $85.00

Mesh Router 5 $69.98 $349.90

Total

Equipment

$2086.90

Starter Model 1 Model 2 Model 3

500

1,000

1,500

2,000

2,500

249.18

638.36

1,095.74

2,086.9

CAPEX Costs (USD)

Figure 3: CAPEX for Different Smart Home Models.

To guarantee robust and consistent Wi-Fi coverage

throughout the area, we strongly advise the incorpora-

tion of 5 mesh routers. This strategic addition ensures

a stable connection for all smart devices, irrespective

of their placement within the space. Carefully curated

for optimal performance, this combination of devices

promises unparalleled convenience, comfort, and efﬁ-

ciency in expansive family residences or commercial

settings. It represents a noteworthy investment in the

advancement of home or business automation, posi-

tioning stakeholders for a seamless transition into the

future of smart living and operational excellence.

Also, Figure 3 effectively underscores the range of

smart home solutions available, allowing consumers

to select a model that aligns with their speciﬁc needs,

household size, and ﬁnancial capacity. The progres-

sive increase in CAPEX from the Starter Kit to Model

4 demonstrates the ﬂexibility of smart environment

offerings, catering to a diverse spectrum of users,

from individual homeowners to large families and

small businesses.

4.2 Provider Cost Analysis

The deﬁnition of acquisition models for converting

conventional homes into smart homes serves as a ref-

erence for a provider to design the costs associated

with its operations, considering a speciﬁc volume of

assets. This mechanism is essential for identifying the

proﬁtability of this type of operation. In addition to

the costs related to acquiring a speciﬁc lot of devices,

such as importation, logistics, storage, resale, market-

ing, post-sales, support operation, and infrastructure,

the provider must calculate the monthly communica-

tion costs between the device and the manufacturer.

This communication occurs automatically when the

device reports its status or when the application re-

quests information about its condition.

This scenario directly impacts the maintenance of

the proﬁt obtained from a direct sales transaction,

which tends to be consumed over the equipment’s

lifecycle. To address this situation, the provider must

ensure a high and constant volume of sales of this

type of equipment or consider adding solutions to this

structure. An effective strategy is to implement a sub-

scription model, which enables recurring billing. In

this way, the provider can accommodate these ongo-

ing costs and establish a new source of revenue.

Table 7 presents an overview of the estimated

monthly and recurring costs for a universe of 500,000

homes. We considered the costs associated with the

development of the device management software, in-

cluding expenses for the software development kit

(SDK) necessary for creating the application. Addi-

tionally, the costs related to cloud servers, which are

essential for managing the APIs and services that en-

sure the proper functioning of the platform, are in-

cluded. We also accounted for the labor required to

support this entire infrastructure, as well as the com-

munication costs between the devices and the manu-

facturer. This comprehensive analysis is fundamental

for understanding the ﬁnancial viability of the project

and for formulating strategies that ensure proﬁtability

over time.

With this analysis, we were able to project the re-

curring cost of each house model for the previously

mentioned universe, using the mechanics of Equa-

tion 5.

Cost P House =

Mess Cost∗Qtd Houses+Cost Cloud+Cost OPEX Empl

Qtd Houses

(5)

Where Cost P House represents the monthly unit

cost per house model, Mess Cost refers to the to-

tal recurring cost of messages per house model for

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective

Table 7: Cost Assessment - Provider Perspective per house.

Cost Assessment - Provider Perspective per house

Description

Monthly Operation Cost

Capex Cost - AWS (Estimated) $ 3,642.38

Opex Cost - Employees (Estimated) $ 172,384.64

Number of Houses 500,000.00

Model Message Cost Operation Cost Unit Cost per house

House 1 $ 9.67 $ 5,014,895.75 $ 10.03

House 2 $ 19.35 $ 9,853,764.47 $ 19.70

Starter Kit $ 19.36 $ 2,111,574.52 $ 4.22

House 3 $ 36.77 $ 18,563,728.16 $ 37.13

the month. Qtd Houses pertains to the universe of

500,000 smart homes, while Cost Cloud is the value

associated with the cloud servers that support this

large-scale operation. Finally, Cost OPEX Empl cor-

responds to the labor cost necessary to maintain this

operation monthly. It is important to note that we are

abstracting costs related to regulatory compliance, ac-

counting, and other operational expenses. Thus, the

focus is on the more speciﬁc costs of the commercial

relationship between the manufacturer and the new

smart environment provider.

This tool enables the understanding of the sales

volume that the provider needs to maintain to sup-

port these recurring costs. By analyzing the costs of

each model, we can identify the variations that di-

rectly inﬂuence the provider’s ﬁnancial sustainability.

Furthermore, this projection aids in formulating re-

curring sales strategies, ensuring that the provider not

only covers its expenses but also achieves additional

recurring proﬁtability.

Based on these results, the vendor can design busi-

ness scenarios and assess whether current sales strate-

gies, which rely exclusively on device transactions

with customers, are sustainable in the long term. As

illustrated in Table 7, there are ﬁxed operational costs

that occur monthly, regardless of whether the supplier

carries out new sales transactions. In other words,

even if sales are interrupted, the supplier’s ﬂeet of

devices will continue to generate costs. This means

that the proﬁtability obtained from previous sales will

have to be used to cover these ongoing operating

costs.

In this scenario, during a sales plateau, where

sales reach a point of stagnation, suppliers will face

a direct impact on their ﬁnancial health. The ab-

sence of new revenue streams to offset these costs

may compromise the sustainability of the business in

the medium and long term.

The graphs 4 illustrate these trends, highlight-

ing the relationship between ﬁxed costs, sales, proﬁt,

sending messages and time. Therefore, it is essential

that providers diversify their monetization strategies

and seek new sources of revenue to maintain the ﬁ-

nancial health of their operations.

Figure 4: Trends in sensor transaction-based model.

When analyzing the relationships and trends pre-

sented in the graphs, it is observed that proﬁt can be

entirely consumed over time. Simultaneously, it is

noted that as the volume of messages sent increases,

whether through API calls or device status messages,

monthly costs also rise. There is a direct relationship

between sales and proﬁt in this context: if sales stabi-

lize at an equilibrium point, proﬁt decreases as it gets

absorbed by recurring costs.

These conclusions are supported by data analysis,

which shows the increase in costs over the months,

directly impacting proﬁt. Over time, the accumula-

tion of messages tends to intensify this effect, further

increasing operational costs.

To address this scenario, we propose implement-

ing a solution that integrates device commercializa-

tion with a qualiﬁed sales strategy. Additionally, we

suggest adopting a monthly subscription model for

the service, based on the studies conducted on the

house model, allowing for a more predictable and sta-

ble revenue stream.

Table 8 offers an alternative to address the recur-

ring costs associated with devices by proposing the

adoption of monthly plans tied to the different types

of homes outlined in this study. These plans are de-

signed as the ﬁnancial component of an integrated so-

lution aligned with the concept of a smart home, cre-

ating a sense of added value for the end customer and

encouraging subscription to a monthly plan.

From the provider’s perspective, this approach fa-

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

Table 8: Suggested Plans and Simulation.

Suggested Plans

Device Range Price (USD)

Between 1 and 15 Devices $ 7.20

Between 16 and 40 Devices $ 16.00

Between 41 and 65 Devices $ 32.00

Between 66 and 100 Devices $ 59.80

Plan Simulation

Model Distribution Operation Cost (USD) Revenue (USD) Proﬁt (USD)

House 1 100,000 $ 1,143,800.78 $ 1,599,800.00 $ 455,999.22

House 2 100,000 $ 2,111,574.52 $ 3,199,800.00 $ 1,087,225.48

House 3 50,000 $ 2,014,797.15 $ 2,990,000.00 $ 975,202.85

Starter Kit 250,000 $ 1,143,800.78 $ 1,799,500.00 $ 655,699.22

Total 500,000 $ 6,413,973.24 $ 9,589,100.00 $ 3,175,126.76

cilitates the management of ongoing messaging costs,

which are incurred regardless of new sales. For the

end customer, the plan goes beyond the mere pur-

chase of a device, offering an additional solution that

enhances both functionality and value. In this way,

the subscription model not only fosters customer re-

tention but also provides a recurring revenue stream,

increasing the ﬁnancial predictability of the operation.

In this simulation scenario, we projected the dis-

tribution of 500,000 smart homes in the suggested

models to estimate total operating costs. We then de-

signed monthly subscription plans based on the range

of devices corresponding to the home models used in

this study. With the projected distribution in place, we

calculated the monthly revenue from these subscrip-

tions. At the end of the process, we determined the

difference between the projected subscription revenue

and the total monthly costs, providing insight into the

proﬁtability of the distribution. In addition, we out-

lined a scenario for supplemental proﬁtability, which

could offset recurring operating expenses if sales vol-

ume stabilized over time. Below, we detail the steps

taken to perform these calculations, providing clarity

on the underlying assumptions, methods, and results.

Equation 6 calculates the operational costs based

on the distribution of different home models. For

example, for Model 1, we assigned a distribution of

100,000 units, and the equation computes the associ-

ated costs for this quantity and type of home.

Cost P Dist = Mess Cost ∗ Q Houses Dist + Cost Cloud + Cost OPEX Empl (6)

Where Cost P Dist represents the total monthly

cost per residential model, Mess Cost refers to the to-

tal recurring cost of messages per residential model

for the month. Q Houses Dist refers to the universe

of smart home distribution for the study model, while

Cost Cloud is the value associated with the cloud

servers that support this large-scale operation. Fi-

nally, Cost OPEX Empl corresponds to the labor cost

required to maintain this operation on a monthly ba-

sis.

Equation 7 The revenue is calculated based on the

distribution of different smart home models. For ex-

ample, Model 2 has a distribution of 100,000 units,

and the equation multiplies this quantity by the sug-

gested plan price to determine the total revenue for

that model.

Reven P Dist = Q Houses Dist ∗ Price Plan Model (7)

Where Reven P Dist represents the total monthly

revenue per residential model, Q Houses Dist refers

to the distribution universe of smart homes for the

study model, while Price Plan Model is the value as-

sociated with the suggested monthly subscription per

residential model.

Proﬁtability, as shown in Equation 8, is also de-

termined based on the distribution of different smart

home models. It is calculated by ﬁnding the differ-

ence between the total revenue generated and the as-

sociated distribution costs

Proﬁt P Dist = Reven P Dist − Cost P Dist (8)

Where Proﬁt P Dist represents the total monthly

proﬁt per residential model.

Finally, the total values of operating costs, rev-

enue, and proﬁtability are calculated. These metrics

are represented by the equations 9, 10, and 11, re-

spectively. In this context, Total Operation Cost cor-

responds to the accumulated operating cost of the en-

tire scenario, Total Revenue represents the total rev-

enue generated by the distribution of smart home

models, and Total Proﬁt reﬂects the net proﬁtability,

calculated as the difference between total revenue and

operating costs. These calculations provide a compre-

hensive ﬁnancial overview, allowing the assessment

of the sustainability of the business model and guid-

ing the provider for strategic decisions.

Total Operation Cost =

∑

i=1

(Cost P Dist

) (9)

Total Revenue =

∑

i=1

(Reven P Dist

) (10)

Total Proﬁt =

∑

i=1

(Proﬁt P Dist

) (11)

Bridging the Cost Gap: A Comprehensive Analysis of CAPEX and OPEX for Smart Home Transition from a Provider’s Perspective

5 CONCLUSION AND FUTURE

WORKS

To enable an affordable transition from conventional

to smart homes, the introduction of a new key player,

the smart environment provider, is essential. This en-

tity is responsible for delivering and managing smart

applications that control household appliances and

devices, ensuring an integrated and seamless experi-

ence for users. The analysis in this study highlights

that even the most advanced packages proposed are

more cost-effective than those presented in previous

research, offering a sustainable and advantageous so-

lution.

Additionally, our study explored subscription

models as an alternative to the traditional device sales

model, demonstrating the feasibility of a monthly

subscription-based approach. The research showed

that this model can foster greater customer reten-

tion, ﬁnancial health, and scalability for smart ser-

vice providers. Future work will focus on optimizing

the cloud infrastructure needed to efﬁciently support

these services and further reﬁning the transition pro-

cess, ensuring that smart solutions are accessible to

consumers with diverse proﬁles and economic back-

grounds.

ACKNOWLEDGEMENTS

Thanks to FAPESB, CAPES, and CNPq organizations

for supporting the Graduate Program in Computer

Science at the Federal University of Bahia. Alto,

thanks to Fundac¸

ao de Amparo a Pesquisa do Estado

da Bahia (FAPESB) grant INCITE PIE0002/2022.

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IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security