ABSTRACT: In fact, WHO launched the Global Platform


idea of Smart Cities (SCs) has its roots in the so-called Healthy Cities
program. In the framework of SCs, the air quality is one of the fundamental
factors that should be constantly monitored because of its effects on health
and, more generally, on citizens’ Quality of Life (QoL). In fact, WHO launched
the Global Platform on Air Quality and Health, calling for a collaborative
effort in order to develop, implement and monitor air pollution abatement
strategies. The monitored data will be uploaded to the web page to collect the
information from anywhere and easy for everyone to know about environment
pollution.   From a technical point of
view, we can conclude that the use of sensor nodes can be quite effective in
monitoring the air quality in cities, increasing the monitoring area with
limited costs, w.r.t. to the use of only fixed nodes, at the cost of a
tolerable measurement error due to mobility and other factors, such as the
height at which the mobile sensor is used.

Keywords –
Healthy cities, monitor air quality, environment pollution, fixed sensor nodes,



The concept of ‘Smart Cities’ mainly facilitates the development and growth of a
particular city. This mission is initiated for the area development where
citizens reside thereby maintaining a sustainable life .Necessary factors like
health of citizens, availability of resources, cleanliness and hygiene of their
habitat and other societal issues are considered important for a city to emerge
a successfully developed city. The first objective is to build an efficient
smart city by means of cost effective, distributed sensor network of both fixed
and mobile sensors. These sensors can be used to measure several environmental
parameters including rainfall density, humidity, temperature, air quality, thermal
index, dew point etc.   

The mobile application will receive the
required information by means of the cloud server and thus acts as an alertness
for the environment officials so that they can fix the problems which causes a
prolonged danger or an obstacle to the residing citizens. This is implemented
by the technology called “Internet of things” in which several devices will be
connected together and information can be manipulated and distributed accordingly.
The main advantage of this architecture is the low cost of the components
involved, provide a user friendly programming environment and also useful for
data analysis purpose to study about the environment.


People centric
computing and communication in smart cities:

paper 1 Franca Delmastro et al.
mainly explains about the mobile technologies which keeps the people connected
by means of special participatory sensing nodes and mobile sensor network (MSN).
A mobile application called CAMEO has been built on social aware middleware
platform able to integrate features of smart citizen in sharing useful contents
related to quality of life. CAMEO overcomes the limitation of single person
centric paradigm.

Towards safe
cities- A mobile and social network approach:

this paper 2 Jaime Ballesteros et al. focusses upon manmade disasters and
growing population problems. It envisions upon the real time public safety .It
includes an android application namely I Safe to form a geo-social network. It
ensures both privacy and performance so as to prevent crime events happening in

IOT for smart

this paper 8 Andrea Zanella et al. presents the “Padora Smart City” project
which deals with several constraints like structural health, waste management,
air quality noise monitoring, traffic congestion, smart parking etc by means of
Bluetooth, Wi-Fi, Ethernet networks. It gives out a relevant example of IOT
paradigm short range communication technology Radio Frequency Identification
(RFID) and Near Field Communication (NFC).

A data driven
robustness algorithm for IOT in smart cities:

this paper 17 Tie Qiu et al. deals with information like node’s geographic,
neighbor kit, sensing data extracted by bid data server .The authors have proposed
an approach by enacting multi-population genetic algorithm(MPGA).It mainly
optimizes the robustness of topology against malicious  attacks .

An architecture
model for smart cities using cognitive Internet of Things (CIoT):

this paper 18 Manoj Kumar Patra et al. deals with the combination of
cognitive science and internet of things. It helps in dealing with massive
amount of unstructured data. CIOT focusses on heterogeneous and interconnected
devices along with centralized server. Various sensors includes (Accelerometer Sensor)
MEMS, CMOS (Image Sensor) actuator etc.


paper 20 Mario Weber et al. illustrates the two case studies on smart parking
and air quality monitoring .The basis technology for the urban development is
Information and Communication Technology (ICT) enabling interaction and
collaboration among citizen. The services are implemented according to general
data protection regulation (GDPR) and directive (11, 12).

IOT based smart
cities – Recent advances and challenges:

paper Yasir Mehmood 19 et al. presents an overview of major open platforms
for smart cities which categorizes network types, existing communication
protocols, crucial requirements. Wide range technology such as GSM, GPRS, LTE, cellular
IOT, ZigBee, Wi-Fi, WiMAX, IEEE 802.11p, etc. Open platform includes FIWARE,

Smart building
monitoring from structure to indoor environment:

paper J. V?elák 9 et al. deals with environmental monitoring in infrastructures
by integrating information gathered from buildings management system (BMS). It
not only monitors temperature and humidity but concentration values of CO2 and
other volatile organic compounds  by
using UCEEB sensor being emitted so as to prevent health problems .IOT upgrades
this system by means of low cost, low power sensor. Communication technology
like LoRa, Sigfox, LTE-M and sensors like FBG (Fiber Brag Grating).

IOT data
aggregation using compressed sensing with side information:

this paper Evangelos Zimos 10 et al. a mechanism is being introduced called
aggregation for large scale air pollution monitoring by means of IOT devices. Aggregation
techniques for WSN such as Differential Pulse Code Modulation (DPCM) and
alternative strategy like Distributed Source Coding (DSC) have been used. The
algorithm used here is data recovery which combines Principal Component
Analysis (PCA) with CS for grid network. The proposed systems provide move
robustness and efficiency compared to classical methods.

Developing smart
cities using IOT – An empirical study:                                                     

paper Gaurav Sarin 11 et al. consists of study of IOT and to identify
preferences of consumer based on smart cities solutions. It differentiates the
concept of internet and IOT which mentions connectivity not only possible in
hardware devices but also in variety of sensors. For this study primary tools
like IBMSPSS and MS excel used to analyze data .Other parameters like
information security correlation, IOT revenue model correlation, hardware cost
and reliability, device and sensor interoperability and independent variables
like energy, traffic, healthcare air quality noise, management values are
analyzed appropriately.

Understanding and
Personalizing Smart city services using Machine Learning, IOT and big data:

paper Jeannette Chin 12 et al. explores various technologies like MC (Machine
Learning) and Artificial Intelligence (AI) to leverage IOT and Big data to
study about smart cities. Well known algorithm like Bayes network, Naïve
Bayesian, J48, Nearest neighbor correlate weather data especially rainfall and
temperature. The data is mainly analyzed on WEKA platform which is a powerful
machine learning tool. All four classification provide accurate and consistent



Real time smart
traffic management system for smart cities by using IOT and big data:

paper Patan Rizwan 13 et al. provides a clear information about deploying
traffic indicates and a low cost real time traffic system. IOT is being used to
retrieve the data at a faster rate and sent for big data analysis. A user
friendly App serves purpose by providing traffic updates, status of read,
vehicle strength, traffic jam issues etc. Techniques like RFID, GPS are used
for vehicle tracking. Primary algorithm used here is VSN (Vector Distance
Routing Algorithm) to provide reliable communication.