Privacy-Aware and Secure Decentralized Air Quality Monitoring

Abstract

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, protection from external monitoring, and distributed data mining. In this paper, we describe an integrated framework that features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and sink placement optimization, and distributed data mining to answer these concerns. We describe the organization of our contribution and show its relevance with simulations and experiments over a set of use cases.

Full text

Citation: Mrissa, M.; Toši´c, A.;
Hrovatin, N.; Aslam, S.; Dávid, B.;
Hajdu, L.; Krész, M.; Brodnik, A.;
Kavšek, B. Privacy-Aware and Secure
Decentralized Air Quality
Monitoring. Appl. Sci. 2022,12, 2147.
https://doi.org/10.3390/app12042147
Academic Editor: Prashant Kumar
Received: 20 December 2021
Accepted: 8 February 2022
Published: 18 February 2022
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applied
sciences
Article
Privacy-Aware and Secure Decentralized Air
Quality Monitoring
Michael Mrissa 1,2,*,† , Aleksandar Toši´c 1,2,† , Niki Hrovatin 1,2,† , Sidra Aslam 1,2,† , Balázs Dávid 1,2,† ,
László Hajdu 1,2,† , Miklós Krész 1,3,† , Andrej Brodnik 2,4,† and Branko Kavšek 2,5,†
1InnoRenew CoE, Livade 6, 6310 Izola, Slovenia; aleksandar.tosic@innorenew.eu (A.T.);
niki.hrovatin@innorenew.eu (N.H.); sidra.aslam@innorenew.eu (S.A.); balazs.david@innorenew.eu (B.D.);
laszlo.hajdu@innorenew.eu (L.H.); miklos.kresz@innorenew.eu (M.K.)
2Faculty of Mathematics, Natural Sciences and Information Technology (FAMNIT), University of Primorska,
Glagoljaška 8, 6000 Koper, Slovenia; andrej.brodnik@fri.uni-lj.si (A.B.); branko.kavsek@upr.si (B.K.)
3Institute Andrej Marušiˇc (IAM), University of Primorska, Muzejski trg 2, 6000 Koper, Slovenia
4
Faculty of Computer and Information Science, University of Ljubljana, Veˇcna pot 113, 1000 Ljubljana, Slovenia
5Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia
*Correspondence: michael.mrissa@innorenew.eu
† These authors contributed equally to this work.
Abstract:
Indoor Air Quality monitoring is a major asset to improving quality of life and building
management. Today, the evolution of embedded technologies allows the implementation of such
monitoring on the edge of the network. However, several concerns need to be addressed related
to data security and privacy, routing and sink placement optimization, protection from external
monitoring, and distributed data mining. In this paper, we describe an integrated framework that
features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and
sink placement optimization, and distributed data mining to answer these concerns. We describe the
organization of our contribution and show its relevance with simulations and experiments over a set
of use cases.
Keywords: air quality monitoring; privacy; security; edge; decentralization
1. Introduction
As we spend most of our time inside buildings, the monitoring of Indoor Air Quality
(IAQ) is essential to ensure healthy living [
1
]. It also greatly contributes to improving
building operation and reducing energy consumption [
2
]. Nowadays, IAQ monitoring
systems typically rely on Wireless Sensor Networks (WSNs) that consist of a large number
of sensing nodes deployed for specific monitoring purposes. Generally, WSNs transfer
the collected data to the cloud for storage and further processing. Such a centralized and
cloud-dependent infrastructure represents a security and reliability risk as the connection
to the cloud becomes a single point of failure that can be subject to diverse attacks. The
risks related to data security and privacy also increase as the storage is remote. With
the development of embedded technologies over the last few years, decentralized IAQ
monitoring solutions have become appealing as they enable on-site data storage, processing,
and analysis [3].
However, decentralized monitoring solutions still suffer from several limitations. First,
the collected data require basic security protection as they describe physical phenomena
that can reveal the activities happening in a monitored space, for instance, the variation of
CO
2
(carbon dioxide) and temperature can indicate human presence in a room. Second,
a decentralized storage solution must make sure that data are only accessible to the right
stakeholder with sufficient permissions, making privacy a major concern as diverse stake-
holders may require access to different views on data. Third, the operation of the developed
Appl. Sci. 2022,12, 2147. https://doi.org/10.3390/app12042147 https://www.mdpi.com/journal/applsci

Appl. Sci. 2022,12, 2147 2 of 22
solution must be protected from external network monitoring that can reveal the activity
of involved stakeholders, the purpose of the WSN, and the contents of the exchanged
messages. Fourth, the complexity of such a solution must be reduced as much as possible
in order to efficiently handle larger amounts of data, number of sensors, and amount of
communication. Although these problems belong to the same scenario, they are generally
addressed individually. Therefore, we provide in this paper a decentralized framework
for IAQ monitoring that integrates different components to address the aforementioned
limitations. We present our contributions and show how they work together by describing
their decentralized implementations on sensors.
Section 2describes the use cases that motivate our work and shows the relevance of
our contribution. Section 3overviews the most relevant related work and highlights the
originality of our approach, with respect to the general framework and each individual
contribution. Section 4details our decentralized framework together with the role and
operation of each component. Section 5discusses the results of our experiments. Finally,
Section 6summarizes our obtained results and gives guidelines for future work.
2. Motivating Scenario
Our motivating scenario is based on a set of five selected buildings that form a starting
point towards a federation of smart buildings that contribute to building joint knowledge
about IAQ. Each building contributes to optimizing its own life cycle and to extending
common knowledge by sharing its monitoring model with other buildings. The pilot
buildings include:
• The Danilo Lokar primary school, located in Ajdovšˇcina, Slovenia;
• The Peavey hall research laboratory, located in Corvallis, Oregon, USA;
• The Nobatek research building, located in Anglet, France;
• The InnoRenew research building, located in Izola, Slovenia;
• The AlbaComp company offices, located in Szeged, Hungary.
The data coming from these buildings require privacy, security, and independence
from the cloud. As a public institution, the primary school requires an IAQ monitoring
that does not reveal information about pupils and teachers. The three research laboratories
need to keep their monitoring activities as well as building occupation patterns away from
public view. The private research company needs independence from the cloud to assess
its operation without any trusted third party.
However, all five pilots would benefit from enabling and exploiting common knowl-
edge that would enable better building operation and management and improved efficiency
in classrooms and offices. The main element that the multiple models have to overcome is
the different climate conditions the buildings are submitted to due to their geographical lo-
cation. Therefore, sharing models enables the identification of which factors responsible for
building behavior are local and which are not, which helps in identifying which measures
are only applicable to local buildings or to all.
This scenario motivates the need for autonomous communities of sensors that, locally,
collaborate to improve building operation and well being and, globally, contribute to
improving the prediction of building conditions. In the following, we review the most
relevant related works that connect to our contributions.
3. Related Work
In this section, we first overview previous works that compare to ours with the same
objective—IAQ monitoring—and identify the research gaps that justify the need for our
work. Then, we overview existing work related to the different parts of our contribution,
namely, decentralized data mining, decentralized privacy-aware data storage, secure data
processing, and, finally, route optimization and sink placement.

Appl. Sci. 2022,12, 2147 3 of 22
3.1. Decentralized IAQ Monitoring Frameworks
A considerable amount of work is dedicated to building IoT systems for IAQ moni-
toring, as shown in a systematic review published quite recently [
4
]. Some solutions rely
on lightweight communication protocols such as LORAWAN technology [
5
] or ZigBee [
6
]
to alleviate the processing load from the sensor and enable the use of small, less resource-
consuming devices. However, most solutions send data to the cloud for further processing
due to the constrained nature of those IoT devices. Although the work in [
4
] addresses
several aspects of IAQ monitoring, the parts that lie in the scope of this paper, related
to data storage, show that most solutions (65%) rely on cloud storage to store the data,
the others relying on non-decentralized IoT data storage solutions.
In [
7
], the authors propose a decentralized IAQ solution based on IOTA and Masked
Authenticated Messaging (MAM). One limitation of this work is the relative centralization
problems that come with IOTA. Another significant difference with our work is the lack of
privacy management, task protection from monitoring, and optimization.
In [
8
], the authors present a decentralized IoT data storage framework using IOTA
Tangle and IPFS (InterPlanetary File System). The proposed framework is based on a
central data handling unit (such as a local server) to collect and encrypt the data, which
is subject to a single point of failure. The encrypted data are sent to the IPFS, while the
corresponding hash and metadata are stored on the IOTA Tangle. However, in addition to
the aforementioned limitations of IOTA, the IPFS network is immutable, which does not
allow users to change the data after storing them [9].
The authors in [
10
] presented a framework that integrated the blockchain with IoT-
enabled air quality monitoring system. The proposed framework used the blockchain to
store the air pollution data of industries in the smart city. However, the security aspects are
not investigated.
In [
11
], the authors provide an Azure cloud-based system to predict air quality and
give a warning when the air quality status needs to change. The proposed system collects
air quality data and then predicts the quality of air pollution. The air quality data are stored
in the PostgreSQL database (built from the Azure cloud platform) and at the same time
replicates data on the blockchain nodes to ensure data transparency. Such a combination
raises several limitations, such as a lack of scalability of the blockchain for large quantities
of data, lack of data privacy and fine-grained access control, and single point of failure for
the PostgreSQL database.
As far as we could see from the existing literature, a fully decentralized IAQ monitoring
solution that integrates distributed data storage, fine-grained access control, integrated
data mining, secure task execution, and route optimization is still lacking, thus motivating
the contribution described in Section 4.
3.2. Decentralized Data Mining
While conventional data mining (DM) approaches rely on data available at a single
location, the key challenge of decentralized data mining (DDM) is to use “decentralized
coordination with local decision making to achieve the intended global goal” [12].
In [
13
], a survey of state-of-the-art DDM techniques is provided, including distributed
frequent itemset mining, distributed frequent sequence mining, distributed frequent graph
mining, distributed clustering, and privacy-preserving distributed data mining (PPDDM).
While [
13
] provides a relatively recent survey of DDM techniques with emphasis
also on privacy preservation, Ref. [
14
] introduces the notion of decentralized spatial data
mining (DSDM), where individual sensor-enabled computing nodes possess only local
knowledge about their immediate neighborhood but derive global knowledge through
local collaboration and information exchange. The latter is especially relevant for our
research, where we aim at monitoring IAQ over a WSN.

Appl. Sci. 2022,12, 2147 4 of 22
3.3. Security of WSN Data Processing
The task of processing the data sourcing from a network of sensors is usually carried
out in an external system, often by relying on cloud services. Therefore, the sensor node
data must be transferred to the external system; commonly, the data transmission is
protected using Transport Layer Security (TLS). However, the data in a WSN travel through
a multi-hop infrastructure-less network, and using TLS could rapidly overload the resource-
constrained technology WSNs consist of. Moreover, the processing power of nodes is not
utilized, and several studies point out the possibility of discovering associations between
TLS traffic patterns and activities in the monitored environment [15–17].
Additionally, gathering data from all sensor nodes in the WSN may be redundant since
closely located sensor nodes might sense the identical event or changes. Data redundancy
can be reduced by aggregating readings of closely located sensor nodes. The in-network
data aggregation technique works via the aggregation of sensor node’s readings as the
data travel through routing paths toward the sink node. Therefore, the sink node receives
an aggregated value summarizing the state of the monitored area. Several [
18
] privacy-
preserving data aggregation solutions were developed relying on data perturbation, data
slicing, hop-by-hop encryption, and Homomorphic Encryption [19].
Data perturbation techniques [
20
] modify individual sensor reading values to hide spe-
cific confidential information but still allowing users to retrieve the correct aggregated value.
In [
21
], twin-keys are used to add or subtract a shadow value to the sensor reading. The ag-
gregated value is correctly computed since any shadow value added by one node during
the aggregation process is subtracted by another node that shares the same twin-key.
The data slicing consists of slicing single sensor node data into a fixed number of
data pieces, all data pieces except one are sent to neighboring nodes. Nodes assemble data
pieces including their own piece and send the assembled data to an aggregator node [
22
,
23
].
However, data slicing leads to high communication overhead due to the large number of
data pieces sent to neighboring nodes.
In hop-by-hop data aggregation, sensor node readings are decrypted and encrypted
at each node in the aggregation path. Therefore, aggregator nodes can access the unen-
crypted data. Several solutions [
24
,
25
] apply a data perturbation technique to protect the
unencrypted data from malicious aggregator nodes.
The Homomorphic Encryption was used in several solutions [
26
–
28
] since it allows
operations to be performed on encrypted data without the need for decryption. How-
ever, the Homomorphic Encryption is computationally intensive [
29
,
30
], and reasonable
solutions for WSNs are designed to only compute addition and multiplication on en-
crypted data.
Secure Multi-Party Computation (SMC) [
31
] allows multiple parties to jointly compute
a function over their inputs while keeping those inputs private. Several studies propose the
SMC technique for privacy preserving data mining since using the SMC for training a data
mining model preserves the data privacy of individual data sources. Refs [32,33] describe
an SMC technique for privacy-preserving association rule mining; in [
34
], an SMC decision-
tree-based data classification was proposed; and the authors in [
35
] proposed a protocol for
securely computing a linear regression model. However, traditional solutions relying on
SMC require high computation and communication costs, thus being not convenient for
application in WSNs. Therefore, the SMC in WSNs is limited to low complexity tasks such
as data aggregation [36].
The presented decentralized framework provides data processing security using the
General Purpose Data and Query Privacy Preserving Protocol for WSN presented in [
37
].
The technique uses messages composed of several encryption layers to convey on WSN
nodes general-purpose computer code for in situ data processing. Data processing results
from multiple nodes along the message path are aggregated, and the privacy of data is
protected by encryption and by concealing the identity of nodes performing data processing
among a set of nodes that bogus data processing.

Appl. Sci. 2022,12, 2147 5 of 22
3.4. Decentralized Privacy-Aware Data Storage
In this section, we overview the most relevant work that focuses on privacy-aware and
decentralized data storage. Most existing solutions rely on a combination of decentralized
ledger technology to enable decentralized trust, combined with a Distributed Hash Table
(DHT) for data storage.
The authors in [
38
] propose an Ethereum-based blockchain framework. Although the
solution is decentralized, the data storage part relies on a MySQL database to store the
data while a hash sum of the data is sent to the blockchain. The main limitation of this
work is the MySQL database that becomes a single point of failure. In addition, the hash
stored on the Ethereum blockchain leaves information about the data storage operations
publicly available.
In [
39
], the authors propose a blockchain-based framework that allows users to control
and keep the track of the data they share with their friends, family, and others. The
proposed framework uses a software client to manage the record of the private key that is
shared with the circle members. It also encrypts the data with the circle’s public key before
sharing it. However, the paper mentions private key sharing, which is a major security risk
that could lead to key leakage and data security issues.
The authors in [
40
] develop a food traceability framework based on the blockchain
and smart contracts. The proposed framework stores original data on the IPFS, whereas
the corresponding hash of the data is stored on the blockchain. A smart contract is used to
handle traceability information. However, the proposed framework modules are managed
by a manufacturer node server that is subject to a single point of failure. In addition,
data stored on the smart contract are immutable and cannot be updated later. One more
limitation of the IPFS network is the immutability that stores file permanently [
9
]. On the
other hand, we use DHT that allows modification in the data after storing it in the database.
In [
41
], the authors combine a blockchain with a Distributed Hash Table (DHT) to
manage personal data. A DHT is used to store encrypted data and a symmetric key (a single
shared key for encryption/decryption) while the data hash is stored to the blockchain.
The proposed framework allows both the service and the user to query data using a data
hash. However, the authors did not discuss any solution to protect symmetric keys from
unauthorized access. Additionally, a fine-grained access control model is used to access the
blockchain. However, the proposed solution records permissions on the blockchain that is
subject to immutability concerns.
The authors in [
42
] propose a blockchain-based data management and access control
framework. A blockchain is used to store access control permissions. However, the per-
missions are stored on the blockchain are immutable and publicly accessible, which raises
privacy issues.
A decentralized data storage for PingER (Ping End-to-End Reporting) is presented
in [43]. The metadata of PingER files is stored on a permissioned blockchain, while actual
data references are stored in a DHT. The work is quite similar to ours but does not consider
security aspects.
In summary, the most relevant data storage frameworks rely somehow on a single
point of failure at one stage or another of the proposed solution. Another concern is
scalability for the solution that stores data on the blockchain. In addition, a good part of
them stores data publicly without any security mechanisms, which highlights the need for
a privacy-aware, decentralized solution.
3.5. Routing and Sink Placement Optimization
The problem of optimal sink placement requires data about distance from any node to
any potential sink. From the perspective of an individual node, the problem is a well-known
single-source shortest path problem (SSSP), which has at least two traditional solutions,
the Dijkstra’s and the Bellman–Ford’s solutions ([
44
]). Both solutions are centralized, which
means that the complete network description has to be made available at a node, which
performs computation. However, Bellman–Ford’s solution can be changed to also work in

Appl. Sci. 2022,12, 2147 6 of 22
a distributed environment, where each node is aware only of its direct neighborhood and
information in it. This change is built into the Routing Information Protocol (RIP, [
45
–
47
]).
In our scenario, initially all nodes are the same and only later are some of them defined
as sinks or gateways. In other words, any node can also potentially be a sink. Consequently,
we are not solving
n
, where
n
is a number of nodes, unrelated SSSP problems, but one single
all-pairs shortest path problem (APSP). The traditional algorithm solving this problem is
a dynamic-programming centralized Floyd–Warshall algorithm ([
44
,
48
]). Indeed, some
attempts were made to make it parallel, and most of them applied techniques used in
parallel matrix multiplication (cf. [49]).
The network design of a Wireless Sensor Network (WSN) can play a critical role in the
effectiveness of the proposed system. The two basic important performance indicators are
the coverage and the connectivity. The objective of the coverage is to monitor a set of targets
with a subset of sensor nodes, while, without connectivity, the sensor nodes cannot send
the measured data to the sink nodes. The literature often combines these two questions
in the case of centralized systems [
50
]. However, in the case of decentralized systems,
the objectives can be separated on the network level; no specific approach is requested for
coverage, and we need to concentrate on connectivity and communication.
For both centralized and decentralized systems, the optimal placement of the sink
nodes and the route optimization have a huge effect on the behavior of the system re-
garding the main design questions, including robustness [
51
], production cost [
52
], power
consumption [
53
], etc. In the case of sink placement, the sensor network is given, and the
goal is to identify the sink nodes in an optimal way.
The mathematical formulation of the discussed issues leads back to two graph theo-
retical problems, the dominating set problem [
54
] and the facility location problem [
55
].
Because of the connectivity constraints on the level of the sink nodes, for both problems,
the connected version is relevant: the connected dominating set and the connected facility
location problems. In the case of a dominating set problem, subproblems, for example,
distance dominating set [
56
] and k-hop connected dominating sets [
57
], are used in the
area of Wireless Sensor Networks. In our case, the problem is very similar; however, our
objective is to minimize the distances with a given fixed dominating set size. Regarding
dominating sets, several different approaches can be found in the literature [58–60].
The original facility location problem is about placing facilities based on geographical
demands in an optimal way in order to minimize facility costs and transportation distances;
however, it can also be used in sink placement. The difference in our case is that each
node can also be a demand. A similar subproblem of the facility location is the rent or buy
problem [
61
]. Regarding the facility location problem, different approaches can be found in
the literature [62–64].
4. A Privacy-Aware and Secure Decentralized Framework
Our contribution consists in a decentralized framework, where each node of the frame-
work is a sensor with some computing capacity. Each node implements our architecture
structured into several components that deal with each identified research problems. In
the following, we give a general overview of a node architecture and explain the com-
bined operation of our solution. Then, we detail the different parts that contribute to this
unique result.
4.1. General Framework Overview
In this section, we discuss the architecture that facilitates the interoperability between
aforementioned contributions so that the system can benefit from the desired features of
individual modules in an additive way. As shown in Figure 1, the global view of our
framework consists of communities of nodes that communicate (1) within a community
and (2) between communities, always in a decentralized fashion, which provides supports
for multiple levels of interaction.

Appl. Sci. 2022,12, 2147 7 of 22
We assume different configuration of WSNs depending on requirements ranging
from private homes, large residential buildings, and public buildings. There should be at
least one sink node per WSN deployment. However, depending on the requirements of
availability, redundancy can be added to increase the fault tolerance of sink nodes. Sink
nodes expose a RESTful API, either to the external or internal network depending on the
security requirements. The API serves as an entry point for querying the system, in our
use case, building data mining models. Data are secured using cryptographic methods
described in the corresponding section. Nodes provide data access under the conditions
enabled in the RBAC component of the node. It is then possible to send queries the
framework (in our case, monthly average). A query goes through the nodes and realizes
the actions necessary to produce the expected result, while using onion routing technique
to protect from external monitoring. Upon receiving a request, the sink node prepares the
onion so that the path starts and ends at the sink. Knowing the public key of the actor,
who initiated the request via the API, the sink node can encrypt the computation results
and interact with the blockchain(RBAC) and the DHT. The query results are then stored on
the DHT, while the access control is used to secure the pointer to the file on the blockchain.
The RBAC is used to limit access to the exposed API on sink nodes and protect the results
of the computation so that only the actor who submitted the query can decrypt the result
from the DHT.
Sink NodesSink NodesSink Nodes
Sink Nodes with a
blockchain based RBAC
Onion routed distributed
computing
WSN WSN WSN
Private Home Residential
Buildings
Public
Buildings
Actor
Figure 1. A layered system architecture.
Therefore, the nodes are responsible for preserving privacy and network anonymity
between the actors interacting with the API, the underlying WSN, and the computation by
onion routing the request as described in Section 4.3. In addition, each node contributes
to the decentralized data mining by locally processing the data it collects (for example,
precalculating local averages). Each node also participates to optimizing the overall com-
munication by contributing to the network through calculating the best routes as well as
the roles of each node.
4.2. Decentralized Data Mining
In this section, we show how data mining can be distributed over a set of nodes. While
the majority of existing DDM solutions presented in Section 3.2 rely on computing the
machine learning models (ML models) locally at WSN nodes and then combining their
knowledge in a global ML model, our proposed approach starts with computing an initial
ML model at a (randomly) chosen WSN node and then iteratively “augment” it by updating
it with the data at the next WSN node. By repeating this procedure following a (predefined)
path through the nodes of the WSN, the last visited node will contain an approximation of
the global ML model, as would be computed on all the data from all the WSN nodes on the
traversed path.
In this way, instead of resorting to ingenious ways of combining multiple local ML
models into one global ML model, we can use techniques from data stream mining (DSM)
to iteratively update our initial local ML model at each subsequent WSN node. Moreover,
our approach also minimizes the amount of data that has to be sent through the WSN—
the descriptions of the local ML models are only shared/sent between the nodes on a

Appl. Sci. 2022,12, 2147 8 of 22
predefined path, as opposed to multiple paths that are needed for sending the local ML
models to a sink node (for the computation of the global ML model) in the traditional
scenario of the “combined” local-to-global ML model computation.
A comprehensive review of the DSM methods is provided by Albert Bifet et al. in
their book [
65
]. We decided to use decision trees as our preferred ML algorithm due to
their space/time efficiency and explainability. The Hoeffding tree [
66
] is a version of the
decision tree that can be learned from streams of data. Thus, our DDM method of choice is
the Hoeffding tree, which we first learn from the batch data provided by some (randomly)
chosen sensor in our WSN and then send through the WSN, following a predefined path
of nodes, to be updated by streams of data at each subsequent WSN node. The described
procedure is depicted in Figure 2.
Figure 2. Incremental learning with Hoeffding trees.
The initial Hoeffding tree (
HT1
) is first learned in batch from the data provided by
the first sensor—
Sensor1
(
Data1
). This tree is then propagated through the WSN, and in
each subsequent WSN node (
Sensork
), an updated Hoeffding tree is generated (
HTk
) from
the previous tree (
HTk−1
) and the data from the current sensor—
Sensork
(
Datak
). The final
decision tree is then represented by the last Hoeffding tree in this chain—
HTn
(the output
of
Sensorn
). Notice that only partial Hoeffding trees (
HTk
’s) are propagated through the
WSN (not the actual sensor data), which is most suitable for privacy preserving and also
diminishes the load on the WSN.
4.3. Security of WSN Data Processing
The decentralized framework presented in this contribution makes use of the technique
described in [
37
] to distribute the processing load of training a data mining model among
sensor network nodes while ensuring the security of data processing. In the following, we
will refer to the General Purpose Data and Query Privacy Preserving Protocol for WSNs
described in [
37
] as DQ3P. DQ3P was proposed for retrieving aggregated data from a WSN
by securely conveying general-purpose computer code through a set of sensor nodes.
In this contribution, we adopt the DQ3P to deliver data mining models to WSN nodes.
Therefore, model training can be carried out in situ without endangering the data privacy
of the sensor nodes. DQ3P relies on encryption to deliver the data mining model only to
nodes that will carry out model learning. Furthermore, the identity of nodes carrying out
model learning is hidden among a set of other nodes that receive the data mining model
without encryption keys for accessing it. Thus, the identity of nodes contributing to model
learning is known only to the origin, in [
37
] it is shown that DQ3P is secure even if some
nodes associated with the network are intentionally collaborating to disclose data privacy
of other sensor nodes. Moreover, DQ3P withstands traffic analysis attacks by generating
uniformly distributed network traffic.
The DQ3P defines a particular message; we refer to it as the onion message, which
consists of the onion head and the onion body as shown in Figure 3. The onion head is
a layered object made of several encryption layers similar to the one used in the Onion
Routing [
67
]. The encryption layers of the onion head are formed using public-key cryp-
tography; therefore, the message must travel through the exact path specified at message
construction, each node in the message path unveiling one layer of the layered object. By
the DQ3P design, layers of the onion head include: (a) the IP address of the next node in the

Appl. Sci. 2022,12, 2147 9 of 22
message path OR (b) the next-hop IP address and two symmetric encryption keys. Onion
messages are issued by sink nodes, and message processing at sensor nodes is conditional
to the content obtained from layer decryption of the onion head, as follows: (a) Nodes that
receive the onion message and obtain only the next-hop IP address from layer decryption
of the onion head are not contributing to the model learning. These nodes retain only the
onion message for a time interval to simulate model learning task. The onion message
is then forwarded to the node at the next-hop IP address. (b) Nodes that also obtain the
two symmetric encryption keys from layer decryption of the onion head are nodes that
contribute to the model learning. These nodes use the first symmetric encryption key
to decrypt the onion body and obtain the data mining model. Model learning is executed
on data located on the sensor node, and the updated model is embedded in the onion body.
The onion body is then encrypted using the second symmetric encryption key, and the
onion message is forwarded to the node at the next-hop IP address.
onion head
onion body
dm model
sink node
sensor node
Figure 3. The figure shows the onion message traveling through the network and updating the DM
model at sensor nodes in the message path. The onion head and onion body color change at each
message processing to indicate the encryption operation.
As shown in Figure 3, onion messages are transiting in the WSN following the path
encoded in encryption layers of the onion head and processing model learning only on
specific nodes in the message path. The onion message path ends at the same sink node that
issued the onion message since the penultimate layer of the onion head always includes
the sink’s IP address. The last layer of the onion message includes the encryption key to
access the content of the onion body and thus obtain the data mining model trained on the
data of sensor nodes in the onion message path.
4.4. Decentralized Privacy-Aware Data Storage
As demonstrated in previous work [
68
], the limitations of blockchain make it necessary
to combine it with other technologies to enable fully decentralized storage and at the same
time allow for data modifications. In this section, we show a decentralized data storage
solution that provides different security levels and privacy protection.
Indeed, data privacy requires addressing the diversity of actors and their roles to
provide fine-grained data access and, sometimes, anonymity when disclosing particular
data pieces, for example, public statistics about a building. To answer these requirements,
we propose a pipeline that combines blockchain with a Distributed Hash Table (DHT), a
Role-based Access Control model (RBAC), encryption mechanisms, and a ring signature to
manage data access.
Our solution stores metadata and DHT hash keys on the blockchain, while correspond-
ing encrypted data are stored on the DHT that allows the data owners to modify their data.

Appl. Sci. 2022,12, 2147 10 of 22
We propose a metadata structure that includes the date and time of data entry, user ID,
DHT key, previous pointer, and data schema URI. We use the RBAC model to verify the
actors’ permissions to read and write data. In RBAC, a role represents a user’s rights to
perform actions according to their given permissions. A permission is an authorization
to access multi-level data within a domain [
69
]. RBAC is suitable for our scenario as it
allows us to define different roles, such as a building manager, room occupant, or scien-
tist. Each actor has different permission to access the data depending on their role. For
instance, with RBAC, we can define that the room occupant can access the fully detailed
data values for the room but not others, while for the building manager, only the averages
suitable to maintain and operate the building. The scientist could access all values without
identification of which room in the building they correspond to.
To complement RBAC, our framework provides different types of encryption mecha-
nisms to secure the data in a decentralized fashion. An authorized actor can choose between
asymmetric encryption or symmetric encryption to write and read data. Symmetric encryp-
tion is more suitable when a large quantity of data needs to be stored and is expected to be
shared with many users, so it is stored encrypted together with the key, and when the data
are requested, only the key needs to be decrypted and then encrypted with the requester’s
public key. Likewise, direct asymmetric encryption is more suitable for small quantities
of data that are not expected to be shared with many other users. Please note that in both
cases, asymmetric encryption is needed to ensure users’ identities, symmetric encryption
being an optimization for specific cases.
We use ring signature to hide the actor’s identity when sending data. This is useful,
for example, when we would like to send data from one sensor within a building without
revealing which sensor it is. To sign data, an actor uses their private key and the public
keys of other actors. Therefore, no one can identify who signed this data, and it helps to
maintain the actor’s anonymity within the group. The ring signature is used to ensure the
actor’s identity privacy. Then, the data requester reads the data and validates the signature
to verify that data are coming from an authentic source.
4.5. Routing and Sink Placement Optimization
Our integrated approach concerns the sink placement from the point of view of routing
feasibility. Therefore, we first consider the sink placement problem in a formal way. Let
G= (V
,
E)
be an undirected connected unweighted graph representing the sensor network.
It is important to note that the questions regarding sink placement can be generalized for
weighted networks, where the edge weights represent the cost of communication between
the nodes. Nevertheless, as the goal is to identify the sink nodes in the network, in the
case of a decentralized system, the weights can be also dynamic as the communication
cost between the sink nodes can be different from the cost between sensor nodes (or
sensor–gateway communication). By the above fact, the generalization in the case of certain
conditions is not straightforward.
Our goal is to identify an optimal gateway placement in a graph
G
with respect to
some cost function defined on the network. We assume that the number of
k
gateways is
determined in advance. In different versions of the problem, because of the characteristics
of the distributed systems, we need to ensure that all sink nodes (or a certain percentage
of the sink nodes) have all the data measured by the sensors. On the theoretical level, we
also assume that the system is operating by synchronous steps via data communication
executed through edges connecting adjacent nodes. In order to define the sink placement
problem, it is important to introduce different methods for the routing paths. We suppose
that the routing from each sensor is arranged in a unique way. In practice, multipath
routing is possible [
70
], but it is considered in a stochastic manner (one routing will be
ending at a single gateway, but the choices during the routing can be stochastic). In the
following, we will distinguish several versions of the problem. In certain cases, multipath
routing has no influence on the model, while, in other cases, it could be handled by a
stochastic model only with an expected value for each gateway having all the data. This

Appl. Sci. 2022,12, 2147 11 of 22
stochastic feature would cause a problem in ensuring the required level of data sharing
among the gateways; hence, in this study, we will consider deterministic models only.
First, we suppose that the set
S
of
k
gateways is selected. A path
p
from a sensor node
v
to a gateway node
w
is called an
S
-routing path if
p
contains a gateway node
u
such that
p[v
,
u]
is running outside from
S
, while
p[u
,
w]
is running inside
S
. In that case,
p[v
,
u]
is
called the external prefix of
p
, and
u
is referred as the
S
-entrance of
p
. If
p
is equal to its
external prefix, then it is called an external
S
-routing path. An
S
-routing system
R
is a set of
S
-routing paths such that each sensor node
v
is the endpoint of a path in
R
with all paths
starting from
v
having the same external prefix. We will call
R
aweak
S
-routing system if
each path of
R
is external. If each sensor node is connected with each gateway node by a
path of
R
, then
R
is called a strong
S
-routing system. We will define problems for both weak
and strong routing systems with appropriate cost functions. In each case, the cost
c(S)
of a gateway placement
S
is the cost of the weak (respectively, strong)
S
-routing system
with minimum cost. In a Type 1 problem, a gateway set
S
is given and determines
c(S)
and the
S
-routing system with cost
c(S)
, while in the Type 2 problem, the objective is to
determine the feasible gateway set
S
with minimum cost
c(S)
. Generally, Type 1 problems
can be handled with some standard path algorithms in polynomial time; on the other hand,
Type 2 problems are hard.
The feasibility of the solutions can be defined in different ways. Regarding the solution,
we separate different cases connected to the set of sink nodes that need to know the collected
information. The feasibility of the optimization problem depends on the information spread
regarding to the sink node level of the network and the user requirements. However,
the different cases regarding the feasibility of the problem can be defined in weak and
strong routing systems. It is also important to note that different objective functions can be
defined regarding the sink placement optimization. In the final system, many factors can
be defined by the user, for example, minimizing the largest distance between the sensor
and sink nodes, maximizing the number of sink nodes that are reached by the collected
data, minimizing the sum of distances, etc. The functions can be used in different ways: a
linear combination as a single objective function, bilevel optimization, or multiobjective
optimization (e.g., as Pareto optimum).
We conclude the section, for illustrative purposes, with a more concrete solution
proposal to the problem. Our network consists of
n
nodes that we model as vertices
V
. In the
process of designating the sink nodes, we compute the distance matrix
D= [ds,d]
between
all source nodes
s
and all destination nodes
d
. In order to define the final optimization
problem, for each node
s∈V
, we define the distance to the closest sink node
ds
. This yields
the definition of the optimization problem, which minimizes:
min
possible sink placements ∑
s∈V
ds. (1)
In detail, the solution finds ksink nodes or gateways in the following phases:
Phase 1: collect the information about the network at one node;
Phase 2: compute the distance matrix D;
Phase 3:
build an MILP (Mixed Integer Linear Program) to solve the optimization prob-
lem from Equation (1);
Phase 4: solve MILP;
Phase 5: distribute solution.
The practical implementation of Phase 2: takes
O(n2log2n)
time and the construction
of MILP in Phase 3: can also be performed in polynomial time. However, the optimization
problem in Equation (1) is, as already mentioned, NP-complete. Therefore, the MILP
solution gives us only an approximate solution.
Moreover, the presented solution is centralized and requires further changes, in par-
ticular related to parallelization, which, consequently, improves the efficiency and time

Appl. Sci. 2022,12, 2147 12 of 22
complexity. For example, replacing MILP with an ant-colony metaheuristics gives a very
practical parallel solution [71].
5. Evaluation and Discussion
5.1. Decentralized Data Mining
In the following, we present the experimental setting (Section 5.1.1) and simulation re-
sults (Section 5.1.2) of the application of the distributed data mining (DDM) method,
presented in Section 4.2, on the air-quality data collected at the “Mrakova Farm” in
Bled, Slovenia.
The aim of the simulation is to show a decision tree learned in an incremental way
(Hoeffding tree) in a distributed sensor environment does not significantly differ from a
decision tree learned in batches from all available data from all of the sensors. Moreover,
we want to show that the sequence, in which the nodes are visited in the WSN does not
significantly affect the final decision tree.
5.1.1. Experimental Setting
For the purpose of the simulation of our proposed DDM algorithm, we used the
air-quality data collected at the “Mrakova Farm” in Bled (Slovenia) during a period of the
last half of the year 2020. The data consisted of the air-quality measurements (described
in more detail later in this section) from two sensors (one placed on the ground floor
and the other on the first floor inside the farmhouse) operating at a 10 s interval from
24 June to 22 December 2020.
We decided to use the data from one sensor only (sensor 1—placed on the ground)
because we wanted to avoid dealing with concept drift, since values measured by sensor 1
and those measured by sensor 2 were quite different. The quantities that were measured
by the sensors are outlined in Table 1and include: air temperature (
T
), relative humidity
(
RH
), CO
2
level (CO
2
), air pressure (
P
), ambient light (
AL
), and particulate matter particles
of size less than 10 µm (PM10).
Table 1. Variables measured by the sensors at “Mrakova Farm”.
Variable Units Value Range
T°C 0–30
RH % 40–90
CO2ppm 400–1700
PhPa 910–1015
AL lux 0–50
PM10 µg/m30–2000
Table 1summarizes the six measured variables, where the first five were chosen as
independent ones (attributes), and
PM
10 as the dependent one (class). The task was thus
to learn a decision tree that will use the five attributes (
T
,
RH
,
CO2
,
P
and
AL
) to predict
the class (PM10).
Our initial dataset, after removing out-of-range measurements (see: Table 1—Value
range), contained 951.897 instances—time-stamped sensor measurements of 5 attribute
values and 1 class value. We then removed a hold-out set of the last 20% (190.379) of
these instances for testing—the test set, leaving in the training set the first 80% (761.518) of
instances (a standard k-fold cross-validation evaluation scheme is out of the question here
because of the time-series nature of the data). A more elaborate train–test scheme could
have been used, but for the purpose of our comparison, it would make no difference.
The experiments were then executed using the WEKA [
72
] and MOA [
65
] ML work-
benches. Models were learned on the training set and evaluated on the test set. Since
we wanted to pursue a classification task, we first discretized the class variable (
PM10
)
into
5 bins
using the equal-frequency discretization provided in WEKA—the data were
discretized even before splitting them into the train and test sets.

Appl. Sci. 2022,12, 2147 13 of 22
Furthermore, for the purpose of simulating a sensor network, we split the training set
into 10 equal subsets, assigning instances
i
,
i+
10,
i+
20,
i+
30, . . . to the
i
-th subset (where
i=
1
. . .
10). In this way, we simulated collecting the data by 10 instead of just 1 sensor.
Because all 10 are just subsets of the same set of measurements of the same phenomenon,
there will also be no concept drift, when we later use this data to incrementally learn a
decision tree.
5.1.2. Simulation Results
We now first learn a decision tree from the whole training set using the VFDT (Very
Fast Decision Tree) [
73
] implementation of the Hoeffding trees [
66
] with default parameters
in the MOA (Massive On-line Analysis) classifier and evaluate its classification accuracy on
the test set. Next, we run the Hoeffding tree learning incrementally by running the same
algorithm on the first subset and “augment” this initial tree by successively running the
algorithm on all other nine subsets (see Figure 2in Section 4.2). The final tree’s classification
accuracy is then again evaluated on the same test set. Since we want to show that the
sequence in which the nodes are visited in a network does not significantly affect the final
decision tree, we repeat the “incremental” decision tree learning process 10 times, each time
on a different random permutation of training set subsets (keeping the first and last subsets
in their place). The sequence of this random permutation of the subsets is represented in
Table 2.
Table 2. Ten runs of the incremental Hoeffding tree learning process—the subsets sequence.
Run Subsets Sequence
R1 1 →2→3→4→5→6→7→8→9→10
R2 1 →3→4→5→6→7→8→9→2→10
R3 1 →4→5→6→7→8→9→2→3→10
R4 1 →5→6→7→8→9→2→3→4→10
R5 1 →6→7→8→9→2→3→4→5→10
R6 1 →7→8→9→2→3→4→5→6→10
R7 1 →8→9→2→3→4→5→6→7→10
R8 1 →9→2→3→4→5→6→7→8→10
R9 1 →6→3→5→4→7→2→9→8→10
R10 1 →8→4→9→5→6→7→3→2→10
The results of the test-runs of the Hoeffding tree algorithm are presented in Table 3.
The table includes the classification accuracies and sizes (number of leaves in the tree) for
each of the 10 runs of the incremental Hoeffding tree (R1
. . .
R10) + the classification accuracy
and size of the Hoeffding tree learned from all the training data (ALL). The average and
standard deviation of all classification accuracies and sizes is also provided.
Table 3.
Results for 10 runs of the incremental Hoeffding tree learning process—the subsets sequence.
Run Accuracy (%) Size (# Leaves)
R1 65.34 285
R2 63.12 291
R3 66.01 280
R4 67.88 279
R5 64.32 285
R6 63.76 290
R7 64.91 281
R8 65.50 292
R9 67.46 291
R10 66.72 285
ALL 64.15 291
Avg. 65.38 286.36
Std. 1.46 4.66

Appl. Sci. 2022,12, 2147 14 of 22
The results in Table 3show that the trees from different test-runs of the incremental
Hoeffding tree algorithm, as well as the Hoeffding tree learned in batch from the complete
training set, are very similar regarding classification accuracies and sizes (observe the
very low standard deviation of the results in Table 3). This confirms our hypothesis
that Hoeffding trees learned in an incremental way do not differ much from their batch
counterparts learned from a large “joint dataset”. Moreover, the sequence in which the
nodes are visited in a WSN does not affect the final incremental Hoeffding tree.
5.2. Security of WSN Data Processing
5.2.1. Privacy Preservation against Eavesdropping
This section analyzes the framework against the attacker model, Eavesdropper (
E
).
The
E
is interested in learning information about the state of a building equipped with
the presented framework by eavesdropping on wireless transmissions generated by nodes
of the WSN. There is evidence [
15
–
17
] that in the typical WSN scenario where wireless
traffic is secured by encryption at data-link-layer or Transport Layer Security analogous,
an
E
could analyze large amounts of WSN traffic to extract descriptive features such as
transmission size, occurrence frequency, processing delay, etc. Therefore, the associating of
these features with facts or secrets and applying data mining techniques could allow the
E
to infer over current activities in the monitored region by observing the network traffic.
In the following, we show that the proposed framework withstands traffic analysis
attacks sourcing data from the eavesdropping of wireless transmissions of WSN nodes. We
motivate the focus on this type of attack by the following three aspects: (a) easy to launch
in the indoor monitoring WSN setting; (b) the attack cannot be detected since the attacker
is not interfering with the normal functioning of the WSN, and the attack does not require
physical access to WSN nodes; and (c) in addition to disclosing privacy, the extracted
information could favor other attacks aiming to compromise building security.
The proposed decentralized framework specifies wireless communication involving
sink-to-sensor or sensor-to-sensor transmissions. Therefore, the component responsible
for ensuring these transmissions’ privacy is the DQ3P protocol described in Section 4.3.
The DQ3P is a querying protocol in which queries are issued from sink nodes in the form
of an onion message. The onion messages are forwarded through a sequence of nodes
completing its circuit-like path at the origin sink node. In the following, we recall the
terminology defined in Section 4.3.
DQ3P guarantees data privacy against eavesdropping since onion messages are se-
cured by encryption.
DQ3P generates uniform network traffic that does not leak contextual information
about activities in the monitored environment due to the following properties:
1.
Sensor nodes are not reporting data to the sink node, but the sink node queries
sensor nodes.
2.
All onion messages traveling the network are of the same size since padding is added
at each message processing.
3.
DQ3P requires encryption at data-link-layer; therefore, after each message process-
ing, the onion message is forwarded to the next-hop node completely changed
by encryption.
4.
By DQ3P design, half of the nodes processing an onion message only retain it for a
time interval to simulate model learning. We refer to these nodes as decoy nodes.
5.
Onion messages travel a randomized path since decoy node addresses are encoded at
random positions in the message path during message construction.
Therefore, by property 1, onion message transmission does not imply activity in the
monitored environment. By properties, 2, 3, 4, and 5 nodes contributing to model learning
cannot be identified by eavesdropping on wireless transmissions. Moreover, if multiple
onion messages are issued to the WSN, these properties allow onion messages to mix
as they travel through the network. Therefore, even if considering
E
as able to intercept

Appl. Sci. 2022,12, 2147 15 of 22
the whole wireless traffic of the WSN. Due to the mixing of onion messages, it becomes
challenging for Eeven to track the circuit-like onion message path.
5.2.2. Evaluation of DQ3P for Distributed Data Mining
This section presents simulation results of DQ3P, the communication protocol for
secure data processing detailed in Section 4.3. The investigation aims to assess the delays
introduced by DQ3P applied to train the data mining model described in Section 4.2 on
nodes of a WSN.
To conduct the investigation, we base it on the results from the previous study [
37
].
The previous study provides a scalability study of DQ3P, highlighting that the response
times is mainly affected by the onion message size. Therefore, we examine delays in-
troduced by DQ3P by metering the Onion message Round-Trip Time (ORTT) of onion
messages having the onion body size corresponding to the size of the data mining model
described in Section 4.2, and the onion head of the following number of encryption layers
n={5, 20, 60}
shown as appropriate in the previous study. In the following, we refer to
n
as the onion message path length, and we define the ORTT as the elapsed time between the
issuing of the onion message from the sink node and the return of the issued onion message
to the issuer node. Since by DQ3P design, the processing time of onion messages at WSN
nodes is affected by randomness, these delays are not included in the ORTT. Moreover,
in the implemented simulation, the measured simulation time does not include processing
delays at the application layer by the design of the underlying simulation environment.
Experimental Setup
In order to conduct this investigation, we extended the simulator developed in previ-
ous work [
37
] to allow the broader manipulation of the onion message and the properties
of the simulated WSN. The developed simulator is based on the simulation environment
nsnam ns-3 [
74
] and is publicly released and described in [
75
]. Since the detailed simulation
description can be found in [
75
], in the following we will outline simulator parameters
selected to obtain the presented results.
The simulator is set up to construct an ad hoc wireless network of 200 nodes. Nodes
are deployed at random locations on a disc-shaped plane of radius 300 m. We selected
the deployment scheme affected by randomness to model the usually non-uniform node
density of WSNs. The simulated wireless communication conforms to the IEEE 802.11n
standard operating at 2.4 GHz at the data rate of 13 Mbps (Modulation Coding Scheme
index 1
), with the nodes having a wireless communication range of approximately 30 m.
The maximum transmission unit and maximum segment size are set to the ns-3 default
value, 2296 bytes and 536 bytes, respectively.
Onion messages are transmitted over the TCP protocol, and routing of packets in the
multi-hop network is handled using the Optimized Link State Routing Protocol (OLSR) [
76
].
Encryption layers of the onion head are produced using the Sealed box, an ECC-based [
77
]
public-key cipher of 256 b key length implemented in the Libsodium library [78].
As described in [
75
], the simulator operates in two phases; the first phase is meant
to identify nodes in the same network segment as the sink node. Due to the deployment
scheme being affected by randomness, some nodes might be located in isolated network
segments, unable to communicate with the sink node. In the second phase, the sink node
starts issuing onion messages sequentially. After an onion message returns back to the sink
node, the following onion message is issued. The sink node is set up to issue 30 onion mes-
sages for each value of n. Onion messages are constructed by randomly selecting nnodes
to include in the onion message path. The onion message path is encoded in the onion head
and the onion body consisting of padding p= {1 k, 2.5 k, 5 k, 10 k, 25 k, 50 k, 100k} bytes.
Sensor nodes receiving the onion message are deciphering the outer encryption layer of
the onion head to reveal the next-hop IP address and the inner encryption layer. The onion
head size is maintained uniform by adding padding of the same number of bytes as the
removed onion head layer. The onion body is maintained of uniform size, and the onion

Appl. Sci. 2022,12, 2147 16 of 22
message is forwarded to the next-hop node. If the onion message does not reach the
next-hop node in 30 s, the onion message is deleted, and a new onion message of the same
properties as the interrupted one is issued by the sink node. The 30 s timer is set up to
speed up simulation completion.
To select the adequate values of
p
, we trained a data mining model named Hoeffd-
ingTreeRegressor [
66
] from the skmultiflow [
79
] library on preliminary data from the Mrakova
farm pilot building. The available data were acquired from 1 sensor, with 15 k measure-
ments about indoor air quality assessment taken during a 2-month interval. The model
was trained to estimate the VOC (Volatile Organic Compounds) concentration based on
other predictors. We trained the model several times by constraining the maximum model
size in bytes to {10 k, 25 k, 50 k}. The high value of the coefficient of determination
0.65
<r2<
0.8 indicates the appropriateness of the values selected for the independent
variable p, the onion body size.
Simulation Results
The results of the simulation are displayed in Figure 4, and summary statistics are
presented in Table 4. From the collected data, it is possible to notice that at the onion body
size of {1 k, 2.5 k, 5 k}, the ORTT measurements do not substantially differ at matching
message path length. A probable explanation is the large size of the onion head, which
at a message path length of 60 nodes, corresponds to 3127 bytes; therefore, affecting the
message transmission more than the onion body at {1 k, 2.5 k}. Moreover, the ORTT is
considerably affected by onion body sizes larger than 10 k bytes, and at the onion path
length of
60 nodes
, the onion body size severely affects the ORTT. Interestingly, from the
data in Table 4, the number of interrupted onion messages seems quite irregular. We
emphasize that a simulator’s parameter affects the number of interrupted onion messages.
In the present investigation, we adopt the ORTT as the indicator of the delays intro-
duced by the DQ3P protocol applied to train a data mining model. The presented results
show that onion messages are complete their path on average in less than 1 min at onion
body sizes
≤
50 k, even for messages including 60 nodes in the message path. Therefore,
delays introduced by DQ3P should be far lower than the cumulative delay introduced by
the processing of the data mining model at nodes in the onion message path. Hence, we
can conclude that delays introduced by DQ3P applied to train a data mining model of size
equal to or smaller to 100 k at onion message path of up to 60 nodes are acceptable.
0
10
20
30
40
50
60
70
1k 2.5k 5k 10k 25k 50k 100k
onion body size (bytes)
average ORTT (seconds)
onion message path length 5 20 60
Figure 4.
A plot of average ORTT of onion messages traveling through
n={5, 20, 60}
nodes and
having onion body size of
p
= {1 k, 2.5 k, 5 k, 10 k, 25 k, 50 k, 100 k} bytes. The average was computed
on 30 onion messages.
5.3. Decentralized Privacy-Aware Data Storage
In this section, we demonstrate the results of privacy-aware decentralized data storage
discussed in Section 4.4.
We used Python 3 to implement the components of our data storage solution. We
evaluate the components of our framework using CPU Core i7, 1.80 GHz with 16 GB RAM.

Appl. Sci. 2022,12, 2147 17 of 22
In our motivating scenario, we have five buildings and each building has eight sensors. We
show our solution scales up by measuring the timings of our prototype with a growing
number of nodes.
Table 4. Summary statistics of onion message ORTT.
Onion Body Size
(Bytes)
Message Path Length
(Number of Hops)
Onion Head
Size (Bytes)
min ORTT (s)
avg ORTT (s)
max ORTT (s)
std ORTT (s)
# of Interrupted Onion Messages
1 k 5 267 0.07 2.66
12.32
3.33
3
20 1047 0.29 8.07
47.4
9.91
3
60 3127 1.56 14.97
41.84
12.11
1
2.5 k 5 267 0.09 5.78
26.24
7.67
1
20 1047 0.46 6.76
21.52
5.83
6
60 3127 1.64 12.93
36.19
9.87
7
5 k 5 267 0.14 4.22
24.21
7.03
2
20 1047 0.63 4.34
16.33
4.11
5
60 3127 5.04 15.85
60.12
11.82
6
10 k 5 267 0.22 5.89
25.39
6.5
4
20 1047 0.93 12.84
58.81
13.28
3
60 3127 9.22 20.87
78.34
13.44
2
25 k 5 267 0.37 4.26
23.75
4.96
2
20 1047 4.42 14.82
43.97
10.22
1
60 3127 16.45 36.63
72.51
13.46
5
50 k 5 267 0.76 5.78
21.47
4.58
0
20 1047 6.29 18.29
46.13
11.11
4
60 3127 30.06 54.44
98.2
17.2
5
100 k 5 267 1.32 11.13
30.42
6.94
1
20 1047 8.64 26.23
45.94
9.52
2
60 3127 49.71 71.66
107.16
13.58
8
For the experiments, we observed the time that is needed to write and read data on
a decentralized privacy-aware data storage framework. Figure 5presents time overhead
between different numbers of sensors such as 8, 16, 24, 32, and 40. We calculated the
average time consumption in seconds for data read and write operations.
In the case of 8 sensors, the time to write data has an average of 0.420 s, and read data
have an average of 0.042 s. For the case of 16 sensors, the data write time has an average of
0.451 s
that does not show much difference with the case of 8 sensors. Data read time has
an average of 0.058 s that is slightly higher than the average read time of 8 sensors. For the
case of 24 sensors, the average data write time is 0.552 s, and the average data read time is
0.054 s that is slightly less than the read time of 16 sensors.
In the case of 32 sensors, the average time to write data is 0.665 s, and the data read time
is 0.065 s. For 40 sensors, the data write gives an average of 0.689 s, and the data read has
an average of 0.082 s. The results show that a privacy-aware data storage prototype gives a
reasonable time overhead. The average time to write and read data is not affected much

Appl. Sci. 2022,12, 2147 18 of 22
by increasing the number of sensors. We explain that this result comes from the multiple
optimizations in the Python interpreter and at the operating system level (system cache).
Figure 5.
Overview of overall time needed for read and write operations on different numbers
of sensors.
5.4. Routing and Sink Placement Optimization
The dominating set and facility location problems provide a reliable theoretical frame
for our sink node placement optimization problem. Nevertheless, our approach is to
combine the toolset, restrictions, and different objective functions of the given theoretical
frame in the area of infection models. The advantage of using infection models (with
appropriate modifications) is that it can guarantee proper theoretical bounds regarding the
solution of the optimization problem as well as a reliable theoretical environment for our
problem. An example for the modification of the infection models can be found in [
80
]. In
our previous work, we introduced an infection model based on a simulation environment
using the greedy method with different spreading strategies and objectives for Wireless
Sensor Networks [
81
]. Due to the restrictions discussed in the given paper, our initial
work provided a solution for the centralized version of a sensor network. Regarding the
methodology used, it is important to note that, due to the submodularity of the problem we
introduced, the method in the area of infection models performs within a constant factor of
1
−
1
/e
as good as the optimal solution. The basic problem was further investigated in our
latest research, which is, however, a generalized solution that can be used in a decentralized
solution [82]. The main problem of the greedy algorithm is that it provides an insufficient
running time in the case of big networks, and it is not efficiently scalable. In our preliminary
results, we were aiming to reduce the running time of the method while, at the same time,
keeping the quality of the solution. Using structural properties of the given network, we
were able to improve the runtime as well as provide an environment to benchmark and
evaluate different community detection methods. Our solution places the seed nodes (or,
in a sink placement problem, the sink nodes), in places that are somehow in a central
position between dense subnetworks identifying the critical positions that can endanger the
wireless sensor network from a connectivity point of view. In the paper, we have compared
our solution using eight different methods with centrality-based approaches, as well as
with the original greedy method. As a final result, we can say that our methodology is
efficient from two points of view. First, it is suitable to reduce the search space of the general
optimization problem (as well as the sink node placement optimization), and second, it
provides an environment to test and compare the effectiveness of different methods in
reducing the search space of the sink placement optimization problem. The research gives
us a good basic model with which we are able to evaluate the different solutions regarding
to the structural properties of such networks.
6. Conclusions
In this paper, we present an integrated edge computing solution for decentralized
Indoor Air Quality monitoring. Our solution features the following advantages that

Appl. Sci. 2022,12, 2147 19 of 22
address well-known limitations of Wireless Sensors Networks: distributed storage using
Distributed Hash Table, blockchain-based Role-based Access Control for data privacy, onion
routing for monitoring protection, routing and sink placement optimization, and distributed
data mining.
In particular, this paper features the following original contributions: (1) a decentral-
ized data storage solution combining DHT and blockchain with a novel metadata structure
that supports data updates and privacy management, combined with a flexible solution
for supporting different security mechanism; (2) a novel protocol based on onion routing
that supports decentralized data mining while preserving privacy against monitoring;
and (3) the introduction of a theoretical concept called S-routing system that provides
insights on possible developments for routing optimization. We prove the feasibility of
our solution with indoor air quality monitoring prototypes and simulations to be de-
ployed in different pilots. Our solution executes distributed data mining for IAQ data
and produces results while remaining fully decentralized and including security, privacy,
and routing optimization.
Future work includes the integration and real-life deployment of our solution in large
scale environments and studying its applicability to different use cases that require different
data processing or different quantities of data. Further development includes an adaptable
solution that, based on the data and on the type of analysis required from the use case,
automatically adjusts to the most adapted choices for network optimization, data storage
and analysis, privacy and security. Agent-based computing and autonomic computing are
inspirations towards such a line of work.
Author Contributions:
Writing—review & editing, M.M., A.T., N.H., S.A., B.D., L.H., M.K., A.B. and
B.K. All authors have read and agreed to the published version of the manuscript.
Funding:
The authors gratefully acknowledge the European Commission for funding the InnoRenew
project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program and the
Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Regional
Development Fund). They also acknowledge the Slovenian Research Agency ARRS for funding the
project J2-2504.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors acknowledge Marina Paldauf for her contribution to the develop-
ment of the data mining part.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript,
or in the decision to publish the results.
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