Smart Communities: From Sensors to Internet of Things and to a

Marketplace of Services

Stephan Olariu

Department of Computer Science, Old Dominion University, Norfolk, VA, U.S.A.

Keywords:

Society 5.0, Marketplace of Services, Smart communities, Sensor Networks, IoT, Crowdsourcing,

Incentivization.

Abstract:

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government that seeks to create a

sustainable human-centric society by putting to work recent advances in technology: sensor networks, edge

computing, IoT ecosystems, AI, Big Data, robotics, to name just a few. The main contribution of this work is a

vision of how these technological advances can contribute, directly or indirectly, to making Society 5.0 reality.

For this purpose we build on a recently-proposed concept of Marketplace of Services that, in our view, will

turn out to be one of the cornerstones of Society 5.0. Instead of referring to Society 5.0 directly, throughout

the paper we shall deﬁne a generic Smart Community that implements a subset of the goals of Society 5.0. We

show how digital technology in conjunction with the Marketplace of services can contribute to enabling and

promoting sustainable Smart Communities. Very much like Society 5.0, our Smart Community can provide

a large number of diverse and evolving human-centric services offered as utilities and sold on a metered

basis. The services offered by the Smart Community can be synthesized, using the latest technology (e.g.

3D printing, robotics, Big Data analytics, AI, etc.), from a hierarchy of raw resources or other services. The

residents of the Smart Community can purchase as much or as little of these services as they ﬁnd suitable to

their needs and are billed according to a pay-as-you-go business model.

1 INTRODUCTION

In 2016, the Japanese Government issued and publi-

cized a bold initiative and a call to action for the im-

plementation of a “Super Smart Society” announced

as Society 5.0. The vision and novelty of Society 5.0

is that it embodies a sustainable human-centric soci-

ety enabled by the latest digital technologies. Society

5.0 meets the various needs of the members of so-

ciety by providing goods and services to the people

who require them, when they are required, and in the

amount required, thus enabling its citizens to live an

active and comfortable life through the provisioning

of high-quality services (Shiroishi et al., 2018). So-

ciety 5.0 provides a common societal infrastructure

for prosperity based on an advanced service platform

which turns out to be its main workhorse. The in-

sight behind Society 5.0 is that continued progress

of ICT and digital technologies of all sorts will pro-

vide individuals and society tremendous opportunities

for innovation, growth, and unprecedented prosperity

and well-being through various forms of human-to-

human, human-to-machine, and machine-to-machine

cooperations and collaboration. Most of these forms

of cooperation and collaboration between humans and

machines or between autonomous machines systems

have yet to be deﬁned and understood (Horwitz and

Mitchell, 2010).

Our paper was inspired and motivated by some of

the challenges that will have to be overcome in order

to implement Society 5.0. With this in mind, the main

contribution of this work is a vision of how sensor

networks, edge computing, and IoT ecosystems can

contribute, directly or indirectly, to making Society

5.0 reality. For this purpose, we build on the recently-

proposed concept of Marketplace of Services that, in

our view, will turn out to be one of the cornerstones

of Society 5.0.

Instead of referring to Society 5.0 directly,

throughout the paper we deﬁne a generic Smart Com-

munity that implements a subset of the goals of Soci-

ety 5.0. We show how digital technology, in conjunc-

tion with the Marketplace of Services can contribute

to enabling and promoting sustainable Smart Com-

munities.

This work is a continuation and extension of a re-

cent paper (Eltoweissy et al., 2019) where we have

deﬁned the concept of Marketplace of Services. In

Olariu, S.

Smart Communities: From Sensors to Internet of Things and to a Marketplace of Services.

DOI: 10.5220/0009430700070018

In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pages 7-18

ISBN: 978-989-758-403-9; ISSN: 2184-4380

(Eltoweissy et al., 2019) we have argued that the Mar-

ketplace of Services is, along with an IoT ecosystem,

an integral part of a Smart Community infrastructure.

Very much like Society 5.0, our Smart Community

can provide a large number of diverse and evolving

services offered as utilities and sold on a metered ba-

sis. We expect that most of the services offered by the

Smart Community can be synthesized within the com-

munity itself, using the latest ICT and digital tech-

nologies (e.g. 3D printing, robotics, Big Data, AI,

etc.), from a hierarchy of raw resources or other ser-

vices.

The remainder of the paper is organized as fol-

lows: Section 2 reviews recent digital technologies

that will be key ingredients in realizing Smart Com-

munities. Speciﬁcally, Subsection 2.1 reviews wire-

less sensor networks; Subsection 2.2 surveys the

recently-proposed edge computing paradigm, a mod-

ern incarnation of sensor networks; Subsection 2.3 re-

views IoTs, a common extension of both sensor net-

works and edge computing; Subsection 2.4 reviews

the Smart City concept proposed by visionaries two

decades ago; Subsection 2.5 reviews the basics of util-

ity computing; ﬁnally, Subsection 2.6 reviews he ba-

sics of crowdsourcing. Moving on, Section 3 reviews

the concept of a Smart Community as an extension

of the well-known Smart City concept. Section 4 il-

lustrates our vision in the context of reviving small

communities fallen onto hard times. Finally, Section

5 offers concluding remarks and highlights a number

of challenges that will have to be overcome in order

to implement the Smart Communities of the future.

2 THE NUTS AND BOLTS

The main goal of this section is to review known ICT

and digital technologies that, in our vision, will play

an important role in implementing sustainable Smart

Communities. These technologies will be surveyed

in chronological order since newer technologies of-

ten extend old ones, while avoiding their limitations

and shortcominings. As an illustration, edge com-

puting is a natural extension of wireless sensor net-

works, where individual edge devices are more pow-

erful and have fewer limitations than the traditional

sensor nodes. In turn, edge computing and sensor net-

works have suggested the rise of the IoT an eclectic

collection of networked devices. We consider sensor

networks, edge devices and IoT ecosystems as under-

lying the Smart Community concept.

To orient the reader, we begin by a table of acronyms

that will be used throughout the paper.

Table 1: A guide to acronyms.

Acronym Description

ICT Information and Communications Technology

OGS Open eGovernment Services

AI Artiﬁcial Intelligence

IoT Internet of Things

IoPaT Internet of People and Things

CC Cloud Computing

CPS Cyber Physical System

2.1 Sensor Networks

Over the last two decades, rapid advances in inex-

pensive sensor technology and wireless communi-

cations have enabled the design and cost-effective

deployment of large-scale wireless sensor networks.

Such networks appeal to a wide range of mission-

critical situations, including health and environmen-

tal monitoring, seismic monitoring, industrial pro-

cess automation as well as a host of military applica-

tions ranging from situation awareness to battleﬁelds

surveillance, to tactical operations (Chen et al., 2011;

Mohrehkesh et al., 2014; Olariu et al., 2007; Oliveira

and Rodrigues, 2011). The common thread that uni-

ﬁes these applications is that the sensors are affording

novel, and sometimes surprising, perspectives on phe-

nomena at a scale that was not possible before.

Sensor networks are viewed as time-varying sys-

tems composed of autonomous mobile sensing de-

vices (using mobile robots) that collaborate and use

distributed coordination to successfully accomplish

complex real-time missions under uncertainty. The

major challenge in the design of these networks is

attributable to their dynamic topology and architec-

ture, caused either by sensing devices mobility or else

by the limited energy budget that suggests turning off

individual sensors to save energy. This state of af-

fairs may have signiﬁcant impact on the performance

of sensor networks in terms of their sensing coverage

and network connectivity. In such dynamic environ-

ments, sensing devices must self-organize and move

purposefully to accomplish any mission in their de-

ployment ﬁeld, while extending the operational net-

work lifetime. In particular, the design of sensor net-

works should account for trade-offs between several

attributes, such energy consumption (due to mobil-

ity, sensing, and communication), reliability, fault-

tolerance, security, and delay (Jones et al., 2003;

Jones et al., 2005).

The designers of sensor networks face another

challenge, namely that of reaching consensus fast

in order not to delay action (Frederick et al., 2002;

Nakano and Olariu, 2000; Olariu et al., 1992; Olariu

et al., 2013). Indeed, it is well known that the value of

information collected by sensors decays (often quite

SENSORNETS 2020 - 9th International Conference on Sensor Networks

dramatically) over time and space and aggregation

of sensor data needs to take this into account (Ra-

jagopalan and Varshney, 2006; Sachidananda et al.,

2010). Yet another challenge facing the designers of

sensor networks is retasking sensors and re-purposing

entire sensor networks in the face of changing mission

parameters (Olariu et al., 2014; Rufﬁng et al., 2014;

Wang et al., 2018).

2.2 Edge Computing

The past decade has witnessed a fundamental

paradigm shift in computing as the number of smart

device users (e.g., smartphone and tablet users) has

exceeded 3 billion (i.e., 40% of the global popu-

lation) in 2017 and is expected to exceed 4 bil-

lion by 2020. In recent years, the realization that

Moore’s law no longer applies has motivated an em-

phasis shift in computer architecture towards energy-

efﬁcient special-purpose architectures (Hennessy and

Patterson, 2019). In conjunction with recent advances

in nano-technology and smart materials, this has lead,

quite naturally, to the development of new types of

connected smart devices (e.g., smart watches, smart

glasses, smart meters, smart robots, connected vehi-

cles, among others). These pervasive and ubiquitous

smart mobile devices are referred to, collectively, as

edge devices, or the “edge” (Lopez et al., 2015).

It was reported that in 2017 the amount of data

generated each month at the edge by smart devices,

such as smartphones, vehicles, and wearables, has

reached 14 exabytes and is expected to exceed 24.3

exabytes by 2019 (Systems, 2015). As a result, we

are beginning to see more and more network trafﬁc

originating at these “edge” devices.

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

Cloud!!

Wired!

Network

Smart!

Devices!

at!the!

Network!

Edge!

Wireless!

Access!

Network

Cellular

Network

Internet!

Local Area Network

Hotspot!

Home

Network

Figure 1: Illustrating the ofﬂoading path from the network

edge to the cloud.

It was soon realized that the data generated at the

edge is incredibly rich in contextual information and,

hence, extremely valuable and should be harvested to

capture and understand context (Haig, 2015). Unfor-

tunately, because of the widening gap between band-

width capacity and data volumes, the data generated

at the edge will increasingly stay at the edge and

will be thrown away for lack of adequate process-

ing power. A good example of such contextually-

rich data is the sensor data collected by the cars that

criss-cross our roadways and city streets. This data

is highly ephemeral as it reﬂects instantaneous traf-

ﬁc conditions that are apt to change fast. Due to la-

tency, costs, and the risks involved in moving data to

and from a cloud, cloud-based real-time processing of

edge data is neither technologically feasible nor eco-

nomically viable. Refer to Figure 1 for an illustration

of the time delay incurred in ofﬂoading the process-

ing of the data generated at the edge to some remote

cloud for processing simply takes too long.

Given the transient nature of context and context-

sensitive needs of individuals and enterprises, the

highest value from edge data can be extracted only

by processing it near real-time.

In the light of our previous discussion, there is a

critical need for an alternative computing platform,

one that allows harvesting and aggregating the huge

amounts of data generated by edge devices right there

– at the edge (Mach and Becvar, 2017). It is an in-

teresting observation that the same edge devices that

generate huge amounts of data, also offer, potentially,

a huge compute and storage resource that at the mo-

ment is untapped (Shi et al., 2016). Indeed, it is ex-

pected that the collective computing and storage ca-

pacity of smartphones will exceed that of worldwide

servers by the end of 2018 (Haig, 2015).

2.3 The Internet of Things

The Internet of Things (IoT) has been deﬁned in myr-

iad ways (Atzori et al., 2010). At the most basic level,

an IoT is a network consisting of smart objects, com-

monly referred to as things. The things in the IoT can

be sensor nodes, actuators, everyday objects endowed

with some computation and communication capabili-

ties, edge devices, such as RFID tags, smart phones,

smart watches, tablets, smart meters and other sim-

ilar devices. The IoT things typically communicate

with each other wirelessly. However, more sophisti-

cated devices such as, for example, various types of

process controllers, may be part of an industrial IoT

system and, as such, have wired Internet connection

(Lade et al., 2017). The IoT devices can sense, col-

lect, and aggregate data from the physical environ-

ment. At the same time, through the use of actuators

and controllers, the IoTs have the ability to “close the

Smart Communities: From Sensors to Internet of Things and to a Marketplace of Services

loop” acting on the environment in response to the

aggregated data. In this sense, IoTs can be viewed as

Cyber Physical Systems (CPS).

IoT systems are expected to see a wide adop-

tion in industrial applications (Lade et al., 2017) and

(Wollschlaeger et al., 2017), healthcare (Mahmood

et al., 2017), (Sinclair, 2017), (Wang et al., 2017) and

(Yang et al., 2019) and, more broadly, to be incorpo-

rated in the fabric of society (Qiu et al., 2018) and

(Montori et al., 2018). See (Sollins, 2019) and (Whit-

more et al., 2015) for surveys of possible IoT applica-

tions.

Cisco predicted that the number of connected IoT

devices will reach 50 billion by 2020 (Chase, 2018).

However, the wide diversity and heterogeneity of de-

vices that participate in IoT, often by joining and leav-

ing dynamically, have direct consequences on work-

load assignment, networking interfaces, privacy and

security, among many others (Hussain et al., 2018;

Sfar et al., 2019; Sollins, 2019). These, and other sim-

ilar challenges will have to be overcome if IoTs are

to see the predicted phenomenal adoption rate. The

problem of providing security and privacy of IoT sys-

tem is already getting attention in the literature (Kim

et al., 2019). For example, (Al-Ameedee and Lee,

2018) proposes to use deep learning strategies, (Sfar

et al., 2019) proposes a game-theoretic solution, while

(Zhang and Wen, 2016) and (Hui et al., 2019) advo-

cate the use of Blockchain technology.

It has long been recognized the value of smart de-

vices in synthesizing sophisticated services (Medina-

Borja, 2015). We believe that one of the important di-

mensions of IoT is, indeed, that of providing services

that are important to the community in which they op-

erate. For example, (Krishnamachari et al., 2018) and

(Ramachandran et al., 2018), have suggested that sen-

sor data and other information produced by various

IoTs is of fundamental importance in Smart Cities.

2.4 Smart Cities

As it turns out, the term Smart City was coined in the

early 1990s to illustrate how urban development was

turning towards technology, innovation, and global-

ization (Gibson et al., 1992). The early visionaries

depicted the Smart Cities of the future as fully con-

nected entities supported by various forms of pre-

deployed infrastructure, including sensor networks,

ubiquitous and pervasive wireless communication in-

frastructure, supplemented by advanced in-vehicle re-

sources such as embedded powerful computing and

storage devices, cognitive radios and multi-modal

programmable sensor nodes. The visionaries an-

ticipated that, in the near future, vehicles equipped

with computing, communication and sensing capabil-

ities will be organized into ubiquitous and pervasive

networks with a signiﬁcant Internet presence while

parked or on the move. They predicted that this would

revolutionize the driving experience making it safer,

more enjoyable and more environmentally friendly.

In our view, the Smart Cities envisioned by the

early visionaries differ from present-day cities in

three major respects. First, the Smart Cities will be

instrumented with the latest ICT, and will actively

rely on, intelligent infrastructure – these are smart

devices that can sense the environment, send and re-

ceive data and are networked together and with other

networked elements in the Smart Cities. The intelli-

gent infrastructure is apt to provide real-time trafﬁc

data on which timely management decisions can be

based. Second, Smart Cities will make extensive use

of strategies and techniques to incentivize and to en-

gage its connected citizens.

While Smart Cities have been deﬁned in myr-

iad ways (Harrison and Donnelly, 2011; Hatch,

2013; Lakakis and Kyriakou, 2015; Litman, 2015;

Townsend, 2013), it is telling that all these deﬁni-

tions have two explicit or implicit characteristics in

common: ﬁrst, the Smart Cities assume an transpar-

ent governance and management style that anticipates

the read needs of the citizens; and, second, they as-

sume a broad and continued engagement and active

participation of the citizens. These two characteris-

tics of Smart Cities can be viewed as “putting the cit-

izen ﬁrst”, or being human-centric.

It is worth noting that the human-centric charac-

teristic of Smart Cities is consistent with, and was

echoed by, OGS – the open e-government services

proposed, in a slightly different context in the past

decade or so by Johansson and his coworkers (Jo-

hansson et al., 2015a; Johansson et al., 2015b) as well

as by various documents originating with (European

Commission, 2016).

In fact, empowering their citizens with increased

access to high-quality information is one of the deﬁn-

ing dimensions of a Smart City. And, the role

played by the citizens is poised to increase since, ac-

cording to recent statistics, as of the end of 2015,

over 70% of the US population resided in big cities

(United States Environmental Protection Agency,

2016; United States Census Bureau, 2015). In fact,

(National Academies of Sciences, Engineering, and

Medicine, 2017) predicts that, by 2050, more than

70% of the world population will reside in metropoli-

tan areas. It is not surprising, therefore, that many

countries are planning and deploying Smart Cities.

They are “urban centers that use intelligent, con-

nected devices and automated systems that maximize

SENSORNETS 2020 - 9th International Conference on Sensor Networks

the allocation of resources and the efﬁciency of ser-

vices” (National Academies of Sciences, Engineer-

ing, and Medicine, 2017)

The rise of IoT and adoption of Smart Cities create

opportunities for creative and efﬁcient management

and utilization of the available resources. One of the

characteristics of Smart Cities is the interconnectivity

of the city’s infrastructure, which allows data to be

collected from various human-generated or machine-

generated sources.

2.5 Utility Computing

Cloud Computing (CC) is a modern metaphor for util-

ity computing, implemented through the provisioning

of various types of hosted services over the Internet.

The underlying business model of CC is the famil-

iar pay-as-you-go model of metered services, where

a user pays for whatever she uses and no more, and

where additional demand for service can be met in

real time. This powerful idea was suggested, at least

in part, by pervasive low-cost high-speed Internet, a

good handle on virtualization, and advances in par-

allel and distributed computing (Barroso et al., 2019;

Hennessy and Patterson, 2019; Marinescu, 2017).

Three aspects are novel in CC: First, it gives users

the illusion of inﬁnite compute resources available to

them on demand. Second, it eliminates the up-front

ﬁnancial commitment by cloud users, allowing them

to increase hardware/software resources as needed.

Third, it gives users the ability to pay for resources

on a short-term basis and release them when they are

no longer needed (Marinescu, 2017)

2.6 Crowdsourcing

Crowdsourcing, a term coined in 2006 by Jeff Howe

(Howe, 2006), involves outsourcing tasks to an unde-

ﬁned group of people. The main difference between

ordinary outsourcing and crowdsourcing is that in the

former the problem to solve is outsourced by a re-

quester to a speciﬁc body of people, such as paid em-

ployees, while in the latter the task is outsourced to

an unstructured group of folks with no permanent re-

lationship to the requester.

As is typical of all emerging research areas,

crowdsourcing has appeared in the literature under

various other names including, peer production, com-

munity systems or collaborative systems. Indeed, it

has been argued by several authors that crowdsourc-

ing can be legitimately looked at as a collaborative

way of problem solving (Brabham, 2008). In the past

few years crowdsourcing applications have mush-

roomed (Franklin et al., 2011; Li et al., 2017; Ra et al.,

2012; Xu et al., 2017; Yang et al., 2012).. For exam-

ple, MicroBlog (Gaonkar et al., 2008) is used to build

a location-based map of videos by allowing partici-

pants to share videos to a cloud server through their

cellular connectivity. Similarly, CrowdDB (Franklin

et al., 2011) utilizes end user’s knowledge to con-

duct in a distributed fashion SQL queries in a crowd-

sourcing approach. Medusa (Ra et al., 2012), a pro-

grammable framework to facilitate crowdsensing by

allowing users to request sensing tasks (e.g. take a

video), recruit volunteers, upload preliminary task re-

sults, validate them, and choose a subset of the vol-

unteers to carry out the task. All of these approaches

and others considered collecting edge-generated data

and sending them to a cloud server, usually for com-

putational convenience. Recently, Wang et al. (Wang

et al., 2016) offered a literature review of crowdsourc-

ing in support of ITS.

There are two important issues that have attracted

attention in crowdsourcing: the use of incentives to

attract a competent and motivated workforce, and the

related topic of preserving the privacy and security of

all parties involved. Several strategies for incentiviz-

ing participation in crowdsourcing have been reported

in the literature (Hussain et al., 2018; Li et al., 2017;

Yang et al., 2012). As pointed out by (Doan et al.,

2011), the race is on to build general crowdsourc-

ing platforms in various application domains. For ex-

ample, vehicular crowdsourcing is an instance of a

crowdsourcing application domain where a group of

vehicles lend their on-board processing resources to

an authorized user (Florin and Olariu, 2015).

A related, but quite distinct, area is that of crowd

computing which combines mobile devices and social

interactions to achieve large-scale distributed compu-

tation (Murray et al., 2010). Here, an opportunistic

networks of mobile devices, including smart phones,

tablets, laptops and the like, offers an aggregate com-

pute power and communication bandwidth. Murray’s

seminal paper (Murray et al., 2010) points out a num-

ber of reasons crowd computing is attractive: key

among them is the willingness of people to contribute

to a common cause, even if no reward is offered. Typ-

ically crowd computing involves one or a series of

tasks that are farmed out to a number of mobile de-

vices. As these devices socially meet other such de-

vices, the tasks are shared with the new devices and

this process continues until the tasks are completed.

Smart Communities: From Sensors to Internet of Things and to a Marketplace of Services

3 WHAT ARE SMART

COMMUNITIES?

Recently, the U.S. National Science Foundation’s

“ Smart and Connected Communities” solicitation

(Smart and Communities, 2019) described Smart

Communities as communities “that synergistically in-

tegrates intelligent technologies with the natural and

built environments, including infrastructure, to im-

prove the social, economic, and environmental well-

being of those who live, work, or travel within it.”

Even though the NSF deﬁnition does not state ex-

plicitly that Smart Communities are human-centric,

their deﬁnition is not inconsistent with the vision and

stated goals of Society 5.0. Extending the NSF deﬁni-

tion, we deﬁne a Smart Community as a community

governed by the lofty goal of satisfying, through the

provisioning of high quality services, most of the rea-

sonable needs of the people, irrespective of whether

these needs arise from the stomach or the mind. In our

vision, one of the deﬁning characteristics of the Smart

Community is that the resources and services are val-

uated through a Marketplace of Services that acts as

an (impartial) arbiter between producers of services

and consumer of services.

Unlike a Smart City that assumes geographical co-

location inside a metropolitan area, we view Smart

Communities as only logically co-located and not

necessarily geographically co-located.

Evidently, Society 5.0 is a superset of the Smart

Community we just deﬁned. At the same time, a

Smart Community need not ﬁt the description of So-

ciety 5.0. This is because a Smart Community, while

human-centric, does not necessarily strive to become

a welfare society. Instead, the main goal and objec-

tive of a Smart Community is to equip the members

of the community with information and services that

they can use to make intelligent decisions. As we dis-

cuss later, some of the information and services pro-

vided by the Smart Community come in the form of

training the workforce in skills that are highly mar-

ketable and that correspond to their abilities.

As already mentioned, this paper builds on the

work of (Eltoweissy et al., 2019). The main contribu-

tion of (Eltoweissy et al., 2019) was to take the idea of

cloud computing to the next level. Speciﬁcally, they

envisioned the Smart Communities of the future as

offering services of all sorts bundled as utilities: the

citizenry consumes these services of a metered basis,

according to the well-known pay-as-you-go business

model. According to (Eltoweissy et al., 2019), the

Smart Community is synthesizing these services from

urban resources produced by various sensor networks,

edge devices, and IoTs. Suitably aggregated, the re-

sources are sold as services.

The main contribution of this work is to extend the

results of (Eltoweissy et al., 2019). Speciﬁcally,

a. we remove the assumption that Smart Communi-

ties are either urban areas or else are geographi-

cally collocated;

b. unlike (Eltoweissy et al., 2019) where the pil-

lars of a Smart Community are the various IoTs

deployed within the community and a central-

ized marketplace of services, in our vision, Smart

Communities are built around IoPaTs (to be de-

ﬁned in Subsection 3.1) and a distributed Mar-

ketplace of Services (to be deﬁned in Subsection

3.2).

In the remainder of this section we discuss IoPaTs,

the Marketplace of Services and show the symbiotic

relationship between them.

3.1 From IoTs to IoPaTs

For our purposes, the IoT concept discussed in Sub-

section 2.3 will be augmented as we are about to

describe. Instead of IoTs, we look at IoPaTs as

underlying the Smart Community concept. We as-

sume that within the Smart Community, resources and

services are produced by independently-owned, de-

ployed, and operated entities that we refer to, gener-

ically, as IoPaTs (short for Internet of People and

Things). At the highest level of abstraction, an IoPaT

is a CPS where the various sensing devices (e.g. sen-

sor networks and various edge devices) make up the

physical component, while the people in the IoPaT,

acting as the cyber component, “close the loop” by

enabling actuation and iteration.

To the ﬁrst approximation, the IoPaTs can be

thought of as startup companies that produce and

bring to the marketplace innovative goods and ser-

vices. In our view, the IoPaTs are the main pillars

of innovation in a Smart Community. The IoPaTs

generate value added in the form of services that they

expose to the community though the Marketplace of

Services. These services may be purchased and con-

sumed, in some form, by the general public or else

may be further aggregated by other IoPaTs to synthe-

size higher-level services.

Where does this process stop? To answer this

question, we need to remember that the marketplace

acts as an (impartial) arbiter that will indicate what

services are aligned with the needs (and wants) of the

society and which others are not. Occasionally, new

services will be produced that may or may not be suc-

cessful in the marketplace. The new services that turn

out to be successful will continue to be produced, and

SENSORNETS 2020 - 9th International Conference on Sensor Networks

may spawn other related services, while those that are

not will be discontinued.

The nature of the resources produced by the IoPaT

and of the locally-aggregated services is largely im-

material for this discussion. However, for the sake of

illustration, these services may include hiring mem-

bers of the community to work within the IoPaT itself,

training members of the community in skills that are

in high demand, providing (on a subscription basis)

personalized route guidance, etc.

While some of the services offered by IoPaTs are

sold in the Marketplace of Services, some others may

be offered free of charge or at discounted prices. For

example, such might be the case when some IoPaTs

within the community decide to offer job training

services to the unemployed. The service, here, is

to equip the unemployed with new, marketable job

skills. His human-centric service is expected to have

a very high societal value.

3.2 The Marketplace of Services

Traditionally, a marketplace serves the dual purpose

of bringing together producers and consumers, and of

providing valuation for the various goods and services

(Bass, 1969; Gates, 1995; Hill et al., 2006). However,

one can also look at the marketplace as enabling the

diffusion of innovation among consumers. As pointed

out by (Bass, 1969), once brought to the market in

some form or another, innovation is likely to create

new needs among consumers. In turn, through social

interactions, these needs encourage and foster more

innovation. Ideally, the marketplace plays the role of

an (impartial) arbiter since it provides a valuation of

services that reﬂects their usefulness to the commu-

nity and society.

It is now widely accepted that information has

value and therefore can be traded or sold (Mahajan

et al., 1990; Olariu and Nickerson, 2008). There are

many aspects of information that may increase or de-

crease its value: timeliness is an important one; accu-

racy is another. Assessing the value of information

and understanding the dynamics of its change over

time has been a topic of research in economics (Allen

and March, 2003; Frederick et al., 2002). Two re-

cent papers, (Krishnamachari et al., 2018) and (Ra-

machandran et al., 2018), have proposed that the sen-

sor data and other information produced by various

IoTs in a Smart City be sold and purchased in a mar-

ketplace. However, these authors were more inter-

ested in making the exchange fair, which is not our

purpose. Services and their effects have been stud-

ies intensely in the past decade and most of their dy-

namics are now well understood (Maglio and Spohrer,

2008; Maglio et al., 2009). The Marketplace of Ser-

vices can build with conﬁdence of this basis. The

Marketplace of Services plays a regulatory role in

three fundamental ways: ﬁrst, it will keep the price

of the services offered competitive; second, it will re-

ward quality services; and, third, it will promote in-

novation by rewarding new services aligned with the

needs of the Smart Community. Needless to say, the

Marketplace of Services will act as an indication that

some existing services do not sell well and should be

discontinued while new services are needed and may

be synthesized by innovative IoPaTs to ﬁll the gaps.

By using data analytics (and machine learning) the

Marketplace of Services acquires the ability to pre-

dict services needed by community members. The

Smart Community is synthesizing these services from

IoPaT-produced resources that, suitably aggregated

and synthesized, are packaged and sold as services.

In our vision, most if not all the resources exist within

the corresponding metropolitan community and are

being acquired within a resource marketplace whose

producers are various IoT systems within the conﬁnes

of the community. It is to be noted while initially we

expect Smart Community boundaries to be physical,

we envision that the future Smart Communities will

operate beyond the physical conﬁnes.

3.3 IoPaTs and Marketplace of Services

– A Symbiotic Relationship

To understand the symbiotic relationship between

IoPaTs and the Marketplace of Services, we note that

the IoPaTs are connected by, and contribute to, a

Marketplace of Services. Moreover, by sharing in-

formation, the IoPaTs create value (Chesbrough and

Spohrer, 2006; Spohrer et al., 2007). One of the fun-

damental role played by the Marketplace of Services

is to incentivize the IoPaTs to share information by

contributing their services in return for payment of

some form or another.

Even though independently-owned and operated,

the IoPaTs ﬁnd it beneﬁcial to become integrated with

other IoPaTs. The general arbiter of this integration is

the above-mentioned Marketplace of Services. The

marketplace will provide, based on supply and de-

mand, a valuation of the resources and services pro-

duced by individual IoPaTs. It follows immediately

that when several IoPaTs are producing the same ser-

vice (say, sensor readings at a certain resolution),

it becomes inefﬁcient for both of them to continue

ﬂooding the market with resources/services for which

demand may be limited.

We envision the various IoPaT subsystems to

be integrated into a city-wide IoPaT ecosystem that

Smart Communities: From Sensors to Internet of Things and to a Marketplace of Services

will revolutionize the citizens’ experience making liv-

ing safer, more enjoyable and more environmentally

friendly. An important challenge that needs to be

overcome is the thorny issue of integrating IoTs into a

harmonious ecosystem. While, to date, “silo” integra-

tion strategies for IoTs have been proposed (Larson,

2016), we believe that a more efﬁcient and effective

solution for both IoT and IoPaT integration into an

ecosystems is a marketplace-driven, open integration

based on a valuation of the services they provide.

4 A CASE STUDY: REVIVING

STRUGGLING COMMUNITIES

It is a sad but well-known fact of life that in the

US many small communities around the country are

struggling: they are trying to come to grips with

poverty, neglect, decaying infrastructure, drug addi-

tion and increasing crime rate. This predicament is

encountered frequently in small communities built

around a single employer or a single industry. Once

that employer leaves, the community enters a slow

process of stagnation and decay. All these factors

have a negative effect on the perceived quality of life

in the community. In turn, the perceived quality of life

that keeps deteriorating induces some of the inhabi-

tants of the small community to move away – to other

communities that provide better long-term prospects.

In reality, an increased out-ﬂow de-population that is

not offset by an equivalent in-ﬂow contributes to a

feeling of helplessness and motivates more folks to

seek a better life elsewhere.

We believe it is time to stop this process and to en-

list technological advances in an effort to revive strug-

gling communities. The process of reviving small

communities is multifaceted and involves, among oth-

ers:

1. Better managing local resources. This entails op-

timizing the use of existing resources and iden-

tifying potential new resources that can be ex-

ploited/aggregated;

2. Providing high quality services that the pop-

ulation needs and is ready to pay for, either

through taxes or by purchasing them from a ser-

vice provider;

3. Setting up a marketplace of resources and services

that provides valuation for the goods and services

produced and consumed by the community

4. Policies that support and promote the better man-

aging of resources and high quality services

aligned with the needs of the local population.

The process of reviving a struggling small com-

munity is often hard to jump-start mainly because of

the lack of technical expertise at the community level

and reluctance to rely on external help. Besides, we

are aware that the process of better managing local re-

sources, both existing and yet to be discovered, takes

time and technical skill that may not be available in-

side the community. The same holds true of the mar-

ketplace of goods and services. For example, provid-

ing training for the purpose of acquiring the required

skills by the local population is a service that many

of the folks in the community will be interested and

willing to pay for.

It is evident, therefore, that a single struggling

small community is very unlikely to boot-strap itself

out of poverty and decay. With this in mind, we pro-

pose REASON: a REgional Alliance of Small cOm-

muNities, a paradigm for reviving a group of like-

minded small communities in a geographic region.

These communities are very likely to share the same

predicament and to beneﬁt from the same approach

to revitalize themselves. The idea is that the partic-

ipating communities will set up a body in charge of

producing a registry of their resources and that will

monitor the production of goods and services within

the participating communities. In our view, our pro-

posed Internet of People and Things (IoPaT) is a pos-

sible platform for enabling the registry of resources

and services. The valuation of the resources and ser-

vices will be implemented by a Goods-and-Service

Marketplace (GSM, for short).

In the US, the funds necessary to jump-start REA-

SON could be obtained through various channels,

ranging from federal appropriations, to state funds,

to local lotteries, or to venture capital, among many

similar ones. We surmise that it is in the best interest

of the federal and state governments to ensure that the

folks in those communities are back to work leading,

again, normal lives.

As already mentioned, (Eltoweissy et al., 2019)

have put forth the vision of a Smart Community that

is largely self-sufﬁcient in terms of producing its own

resources and of aggregating sophisticated services

whose valuation is regulated by a marketplace.

There are a number of implicit assumptions in (El-

toweissy et al., 2019). First, that the community has

exceeded a critical mass, in terms of both population

size, technical skills and buying power of its inhabi-

tants, so that the marketplace forces can work unim-

peded. The second implicit assumption is that being

geographic proximity the resources and services can

reach all those who are ready to pay for them without

delay and that, moreover, the shipment costs of these

goods are negligeable and will not adversely affect

SENSORNETS 2020 - 9th International Conference on Sensor Networks

their marketability. Finally, (Eltoweissy et al., 2019)

make the implicit assumption of a centralized market-

place.

These assumptions do not hold true for individual

small communities and, in fact, may not hold for the

conglomerate of communities in the regional alliance

that we have deﬁned. The vision and main contribu-

tion of this case study is to show how that the implicit

assumptions of (Eltoweissy et al., 2019) are not es-

sential and that, perceived as a community of com-

munities, the regional alliance of small communities,

can satisfy all these conditions.

First, concerning critical mass, we argue that the

regional alliance should count a number of inhabitants

that is comparable with a community that is large self-

sufﬁcient in terms of producing the basic resources it

is consuming. This follows from the fact that, orig-

inally, each of the small communities in the alliance

must have specialized in producing a certain resource.

This “division” of work is a fundamental law of eco-

nomics and applies intra- and inter-communities. The

alliance can use this division of work to advantage

in the process of diversifying its resource and service

base. Second, for the delivery of material goods that

are subject to stringent deadlines, the regional alliance

can employ drones. Companies like Amazon, UPS

and others are already ﬁnding drones to be a cost-

effective alternative to the traditional truck delivery.

We expect that, in volume, the additional expense will

be amortized and will not adversely impact the mar-

ketability of goods and services.

Finally, our vision is to replace a centrally-

controlled Marketplace of Services by a distributed

one. A distributed marketplace can be viewed, es-

sentially, as a distributed database of key-value pairs

where the ﬁrst component is a service, the second its

market value. Distributed database technology is suf-

ﬁciently mature and well understood and the range of

its applications is tremendous. While this way of im-

plementing (modeling) a Marketplace of Services is

possible, there are numerous challenges to overcome

to make it reality.

The goal of the REASON is to revive its mem-

ber communities. This process involves a number of

human-centric goals that we now state:

1. Improve the quality of life in each of the mem-

ber communities. One signiﬁcant component is to

ﬁght crime. We expect that, in a small community,

the vast majority of crimes are petty crimes rang-

ing from burglary to larceny, etc. To combat this

type of crime we can rely, effectively, on drone

technology to discourage would-be criminals;

2. Enhance the outside image projected by the com-

munities. The idea here is to make the commu-

nity attractive to folks who would be interested in

joining the community. Of a special interest is at-

tracting industrial partners (new IoPaTs). In this

regard, inspired policies, including free land, tax

rebates and other similar incentives, supported by

the local governments are of a fundamental im-

portance;

3. Enhancing the technical skills of the population.

One way of implementing this idea is by using

assistance from federal programs;

4. Promoting tourism and organizing fairs and open

houses showcasing the natural beauty of the re-

gion,

5 CONCLUDING REMARKS AND

CHALLENGES AHEAD

Motivated by the recently-proposed Society 5.0, our

main contribution was to offer our vision of a sustain-

able human-centric Smart Community built around a

Marketplace of Services. The services offered by the

Smart Community can be synthesized, using the lat-

est ICT and digital technology including 3D printing,

robotics, Big Data analytics, AI, etc., from a hierarchy

of raw resources or other services. The residents of

the Smart Community can purchase as much or as lit-

tle of these services as they ﬁnd suitable to their needs

and are billed according to a pay-as-you-go business

model.

In our vision, the basic pillars of service provi-

sioning and innovation in a Smart Community are

the IoPaTs, cyber-physical systems, that behave very

much like startup companies. Some of the IoPaTs

thrive because the services they offer are aligned with

the real needs of the community; other IoPaTs, whose

services are less well aligned, will have to adjust or

else discontinue their services. This is very similar to

the survival of the ﬁttest service providers. The ar-

biter, in the Smart Community is the Marketplace of

Services that reﬂects the need and the willingness to

consume services expressed by the population.

To make our vision reality, a large number of

open problems and technical challenges need to be

addressed. Here is a sample of challenging problems

that await resolution:

• One of fundamental attributes of a Smart Com-

munity is sustainability. What safeguards, if any,

need to be added to guaranteed that the civil soci-

ety is sustainable?

• What is a minimal set of incentives that triggers

the formation of the ecosystem of IoPaTs?

Smart Communities: From Sensors to Internet of Things and to a Marketplace of Services

• Can the Marketplace of Services provide those in-

centives?

• Can the Marketplace of Service guarantee sustain-

able innovation in a Smart Community? What

other actors are at play here?

• In the process described above, some IoPaTs may

become more and more successful and powerful

while others will become weaker. Can the Mar-

ketplace of Services, by itself, prevent this imbal-

ance from having a negative effect on the commu-

nity?

• What is the role of community-wide administra-

tive policies?

• Can powerful IoPaTs manipulate the marketplace

and inﬂuence the needs and wants of the commu-

nity?

• What are the factors that can stiﬂe innovation?

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