=Paper=
{{Paper
|id=Vol-3145/short03
|storemode=property
|title=Towards Blockchain-based Smart Systems
|pdfUrl=https://ceur-ws.org/Vol-3145/paper03.pdf
|volume=Vol-3145
|authors=Hamza Baniata,Dragi Kimovski,Radu Prodan,Attila Kertesz
|dblpUrl=https://dblp.org/rec/conf/cerciras/BaniataKPK21
}}
==Towards Blockchain-based Smart Systems==
https://ceur-ws.org/Vol-3145/paper03.pdf
Towards Blockchain-based Smart Systems
Hamza Baniata1 , Dragi Kimovski2 , Radu Prodan2 and Attila Kertesz1
1
Software Engineering Department, University of Szeged, Hungary
2
Institute of Information Technology, University of Klagenfurt, Austria
Abstract
The unprecedented pace of technological development in smart systems, incorporating sensing, actu-
ation, and control functions, have the following properties and needs: (๐) they are interconnected and
need scalable, virtualized resources to run, store and process data, (๐๐) they are mobile and can poten-
tially access and build on user data made available by smartphones and tablets, and (๐๐๐) they are getting
smarter, so they may get access to user data provided by connected smart devices. As the number of
smart devices in smart systems grows, the vast amount of data they produce requires high-performance
computational and storage services for processing and analysis and other novel techniques and meth-
ods that enhance these services and their management. Blockchain applications have been proposed
in a wide variety of environments such as distributed voting, eHealth, Mobile Computing, Internet of
Vehicles, etc. We believe that integrating Blockchain technology with smart applications for managing
data of mobile devices can further enhance the privacy and security requirements of current complex
systems. In this paper, we discuss Blockchain-integration possibilities for smart systems to support
the efficient, secure, and privacy-aware execution of smart applications. We propose a design space
where issues need to be solved in different layers of such integrated systems. Accordingly, we envi-
sion a Blockchain-enabled simulation framework capable of analysing the integration possibilities with
fog/edge and cloud infrastructures at different layers of smart systems. The framework will be able
to model and analyse the behavior of Blockchain networks in large-scale fog-enhanced smart systems
while using different AI methods.
Keywords
Blockchain, Smart Systems, Cloud Computing, Internet of Things
1. Introduction
Nowadays, we are witnessing an unprecedented pace of technological development in smart
systems. A Smart System (SS) incorporates the functions of sensing, actuation, and control
in order to describe and analyze a situation and make decisions based on the available data
in a predictive or adaptive manner, thereby performing smart actions [1]. A Smart Device
(SD) is a fundamental component of a SS generally connected to other devices or networks
via different wireless protocols (such as Bluetooth, Zigbee, NFC, Wi-Fi, LiFi, 5G, etc.). They
can operate to some extent interactively and autonomously [2]. SSs address environmental,
societal, and economic challenges like limited resources, climate change, population aging, and
globalization. They are for this reason increasingly used in a large number of sectors, such
1st Workshop on Connecting Education and Research Communities for an Innovative Resource Aware Society
(CERCIRAS Cost Action CA19135), September 2, 2021, Novi Sad, Serbia
baniatah@inf.u-szeged.hu (H. Baniata); dragi.kimovski@aau.at (D. Kimovski); radu.prodan@aau.at
(R. Prodan); keratt@inf.u-szeged.hu (A. Kertesz)
ยฉ 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
as transportation, healthcare, energy, safety and security, logistics, ICT, and manufacturing.
One can also categorize SSs via regions by referring to smart homes and smart cities. The
management of SDs and their data in SSs require smart applications, raising many requirements
and open issues.
The current SS services and applications have the following properties and needs: they are
interconnected and need scalable, virtualized resources to run, store and process data; they are
mobile and can potentially access and build on user data made available by smartphones and
tablets, and they are getting smarter, so they may get access to user data provided by connected
SDs.
As the number of SDs interconnected within a SS grows, the vast amount of data they produce
requires high-performance computational and storage services for processing and analysis and
other novel techniques and methods that enhance these services and their management. To
support these needs, Cloud Computing [3] services started to be utilized almost a decade ago
by responding to the growing data management needs of large scale systems. Meanwhile, the
miniaturisation of electronic devices and improvements in battery lifetime led to the devel-
opment of small computational devices with communication capabilities giving birth to the
Internet of Things (IoT) paradigm [4]. From a different point of view, different integration
options of IoT devices with cloud-based services have become the reference models of SSs. To
cope with the possibly massive number of communicating entities, Fog (FC) and Edge Comput-
ing (EC) [5], born around five years ago, enhance data management operations by providing
computational services placed close to their origins within SSs. A group of such edge nodes
of a network forms the fog, which enables data processing and analysis to be performed with
reduced service latency and improved service quality compared to a remote cloud utilisation.
The application of these innovative technologies in smart cities led to the creation of a powerful
ecosystem among public administrations, private companies and citizens to improve the quality
of life by implementing new communication strategies, policies and solutions for their active
involvement in the service management [6]. Such a smart, citizen centric management of urban
services allows each city to reduce costs, and to increase security by keeping data locally (off
the cloud) and ensure citizens satisfaction with reduced service latencies at the same time.
Blockchain (BC) [7] is the backbone technology for many Distributed Ledger (DL) and
Distributed Computing (DC) applications, such as digital cryptocurrencies and digital smart
contracts. Solutions integrated with BCs excel the provenance of high levels of security and trust,
and guarantee fully-immutable log of transactional history without the interference or control
of a central authority. BC applications have been proposed in a wide variety of environments
such as distributed voting, eHealth, Mobile Computing, Internet of Vehicles, etc. We believe that
integrating the BC technology with smart applications for managing data of mobile SDs can
further enhance the privacy and security requirements of current SSs. Sharma et al. [8] were
the first to integrate BC technology into fog-enabled systems by addressing privacy challenges.
Critical implementation and deployment decisions in such complicated, blockchain-assisted
SSs cannot be made by system administrators and need various sophisticated methods. Fur-
thermore, the big data produced in SSs by SDs cannot be handled and analyzed via traditional
methods. The data handled by COVID-19-related applications, for instance, represents a much
higher potential to fight the pandemic, once these applications are made smarter. On top of this,
recent advances in the field of Artificial Intelligence (AI) [9] are also being actively deployed
in both SSs and BC-based systems. AI deployment implies that system entities are able to act
for maximizing the chances of successfully achieving their common goals and provide better
services and enhanced data propagation. The deployment of AI in SSs indeed was proven to
exponentially raise system abilities in terms of smartness. That is, system entities may learn
what to do and when, in an automated environment that allows sensors, actuators, and other
SDs to optimize their collaboration. AI deployment in BC-based systems, on the other hand,
equips BC entities (usually referred to as miners, which mint and confirm new blocks and verify
users and transactions) with methods to determine optimized collaboration practices for differ-
ent purposes, such as optimal selection of peers and optimized data verification/confirmation.
Such deployment in highly dynamic BC networks (e.g. public-permissionless BC) can increase,
or decrease the propagation time of new blocks, leading to lower/higher consistency of the
distributed ledger or latency of data retrieval. Despite all of these advantages of AI-BC-FC-SS
integration, these systems still inherit the trade-offs that appear in BC and SSs, which requires
further research regarding optimization and privacy-awareness.
To address all these issues and challenges, we propose a framework including novel integrated
methods for SSs using a BC-based edge and fog-enabled distributed infrastructure to handle
latency, single-points-of-failure and mobility issues, while AI technologies can optimize collab-
oration between these elements. To facilitate the use of such complex BC-based AI-enhanced
Edge/Fog-enabled SSs, we need to strengthen trust and to provide novel techniques to ensure
privacy, while optimizing the integration of these different technologies within different layers
of the SS. In the future, we plan to provide enhancements to COVID-19-related applications
and use them to validate our proposal.
The remainder of the paper is organized as follows: in Section 2 we present the state-of-the-
art, and in Section 3 we introduce our proposed and envisioned integrated architecture. Finally,
in Section 4 we state our future direction, and conclude the paper in Section 5.
2. State-of-the-art
The support for the future decentralized platforms for medical data storage and analysis in BC
with autonomous and democratic practices is still immature. Nevertheless, promising research
initiatives have started in the European research community, focused towards solving issues
related to medical data management with BC. One of these initiatives is the Horizon 2020
MyHealth โ MyData1 project that aims at the development of decentralised marketplace for
open sharing of anonymized sensitive medical data for research purposes. The project provides
secure ecosystem that encourages hospitals and medical centers to share their data, while making
the citizens the ultimate owners and controllers of this data. Furthermore, the Horizon 2020
FeatureCloud2 project focuses on creating a federated AI platform with centralized, yet transient
Cloud for shared intelligence in medical systems. The platform utilizes DLT technologies for
data access control and to secure features sharing.
Generally, BC have been utilized in the literature for providing a reliable distributed database,
where several parties collaborate for handling/managing sensitive data and controlling the
1
http://www.myhealthmydata.eu/
2
https://featurecloud.eu/
access to it. BC represents a database that need not be administered by a central authority.
Furthermore, all parties of the system can confirm or reject any piece of data added to it, while
no data can be deleted from it. This provides a full history of all transactions appeared on the
BC, giving system users a method to insure the correctness of retrieved information.
In the literature, Kuo and Ohno-Machado [10] propose a cross-institutional healthcare predic-
tive model for quality improvement initiatives by predicting the risk of re-admission of a group
of patients using data from multiple institutions. This approach sets the ground for developing
privacy-preserving ML technology in BC. Furthermore, Mettler [11] provides an initial medical
data management approach through BC, empowering patients and fighting counterfeit drugs
in the pharmaceutical industry. Jenkins et al. [12] discuss a distributed unsupervised learning
framework based on BC for bridging the gap between security and large medical data analysis
with functional bio-markers to identify possible inherited diseases. Recently, a feasibility study,
presented in [13], explores the idea of applying federated learning3 for secure multi-institutional
data analysis, with multiple local models coordinated by a centralized aggregation server. Al-
though the concept is promising, it still requires centralized model to gather all updates, which
can be prone to failures and undemocratic decisions. Moreover, a recent research [14] proposes
a BC-based healthcare data gateway architecture to enable rudimentary control and secure share
possibilities of patient data without violating privacy. The data is stored in a private blockchain,
thus not anonymized. Lastly, Omidshafiei et al. [15] present a generalized linear ML models for
the first time, which are able to perform model training in a fully decentralized setting. The
approach, termed COLA, provides communication-efficient decentralized framework, without
any requirement for parameter tuning.
Utilizing the BC technology has its own drawbacks and challenges [16]. Although BC is
considered the current state-of-the-art solution to reliably handle DC applications, privacy and
standardization issues are still major concerns for BC deployment. One problem is the use
of pseudonyms, which does not fully preserve privacy, even when combined with advanced
privacy-preserving methods (e.g. mixing services [17]). Additionally, BC deployment implies
higher latency for data aggregation and for maintaining DL consistency compared with central-
ized systems, depending on various methods employed in different BC-based applications to
reach consensus among system elements. However, SSs may suffer from heterogeneity, which
hardships the maintaining of data concurrency and credibility, and limits the computing and stor-
age abilities. BC-integrated systems can solve such problems, while maintaining high security.
On the other hand, infrastructure requirements of BCs, such as distributed and highly connected
Peer-to-Peer networks, resource management platforms/algorithms, standardized computing
entities, and fast communication channels through various scalable network topologies, are
provided in nearly all SSs.
Concerning the state-of-the-art for utilizing the BC technology to address challenges related
to the COVID-19 pandemic, we identified initial related literature, e.g. BeepTrace [18] for
global infection tracing, PPMF [19] for nationwide infection tracing. Biometric and identity
management companies, such as SCIPA, Mvine and iProov announced the trials of their Covid-
19 immunity and vaccination passport in the beginning of 2021. Meanwhile, European national
efforts have been reported by the European Commission regarding mobile contact tracing apps
3
https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
[20]. Although such applications are being approached vigilantly by both governments and non-
practitioners, it was argued by Barsocchi et al. [21] that a more transparent approach for data
treatment would benefit the adoption of such services. Accordingly, digital Verifiable Credentials
(VCs) using ZKPs were argued to be the most suitable approach for these applications. Digital
VCs can be instantly approved, no central authority collects private data of users, and the
verification process is more accurate, up-to-date, and thus more reliable than paper-based
schemes. To address the challenges of a successful, globally-trusted, and reliable digital VCs
application, a collaboration of different national projects is required.
3. Our proposal
BC deployment in a wide range of applications was proven as an enhancement factor in terms
of security [22], decentralization [23], reliability [24], and optimization of multi-party decision
making [25]. Generally, these criteria enhanced by successful BC integration, are considered
the main challenges in IoT, fog, and cloud based SSs.
In this position paper, we present a set of research questions and envisioned methods to tackle
them. Our future research focus mainly targets these questions, and we will apply different
methods to efficiently address their challenges. We aim to answer the following questions:
1. What are the best practices for integrating SS-IoT technologies with BC technology?
2. How to combine AI, BC, Cloud and FC in SSs to better serve the requirements of smart
applications?
3. What BC methods and consensus algorithms are most suitable for optimizing services
provided by FC-enhanced SSs?
4. How can the integration of fog/edge, AI, and BC technologies advance user experience,
trust and privacy protection at the same time?
5. What is the potential enhancement of COVID applications driven by the integration of
SS, IoT and BC technologies?
Figure 1 depicts the design space of our vision and research methodology for performing
research in BC technology integration. The main entities are: (i) blockchains, (ii) smart systems
and (iii) applications. Each of the entities in the demonstrated design space has unique iden-
tification layers, where different services and protocols can be placed and investigated, be it
the end-user devices, servers/APs in the fog tier, or VMs in the cloud. Generally, a BC-based
system can be defined according to the infrastructure it is occupied with, the local protocols
that define how transactions and blocks are mined, validated and shared, the algorithms that
control how system entities confirm a mined block and verify each other, and the purpose of the
BC deployment (in the application). The infrastructure, specifically, can be studied according to
different P2P connection models with technical consideration referred to by the OSI network
model. Finally, a smart system is defined by edge devices (mainly corresponding to end-user de-
vices in an IoT enabled system), edge gateways that locally control and secure communications
among edge devices and with upper layers, edge servers (corresponding to the fog nodes in the
lowest layer of the fog), and global servers, clouds or upper fog layers. To answer the research
questions raised earlier, our research aims at analyzing the requirements of COVID-19-related
Figure 1: Design Space for Blockchain and Smart Systems integration within a fog-enhanced cloud
architecture
smart applications to guide our research, and investigating and developing sophisticated AI
methods to be applied within individual layers of each of the combined technologies, then
validating them with smart applications by means of simulation. Consequently, we aim at the
optimization of our methods for technology integration, so that practitioners can decide, at
the time of systems deployment, which protocol, algorithm, infrastructure, etc. to adopt for
maximizing the efficiency of those systems, meanwhile fulfilling GDPR-compliance. To this
end, we plan to investigate how the GDPR regulation affects BC-integrated smart systems. We
plan to address these challenges by applying privacy and data protection by design methods of
GDPR for application data storage and processing. We believe that applying our proposed BC-
and AI-based methods in these applications can significantly improve their privacy and trust
reputation.
We will therefore research a simulation environment for the integration of AI, BC, Cloud
and FC supported by a visual user interface. An integrated template library will provide
reusable templates for simulation of complex smart applications and smart systems over BC
technology. The template library will be extensible with user-defined simulation models and
actions. The extensible templates will support simulation of various smart applications through
the injection of step-specific code provided by application owners. This solution regulates the
inter-step communication using a race-condition-free mixture of message-oriented consensus
algorithm over distributed file systems. Apart from the injected code, the simulation will support
the definition and simulation of SSs by considering the hardware requirements, application
parameters and scaling the number of concurrent instances of each component of the application.
The simulator will enable serialization and deserialization (saving and loading) of application
definitions using the standard output format (JSON, XML or YAML). The serialized application
definitions will produce a deployment service configuration for the services, including the
requirements of the smart systems and inter-application communication code as part of the
simulation scenario.
Besides, the simulator will utilize the application definition from the template library and
extract step definitions. Therefore, the users only need to provide a sample input for the entire
application used for simulation purposes. The tool will simulate each component in the smart
application by instantiating container-based templates and deploying an instance in a sandbox.
Additionally, it will automatically provide input to the sandbox tool, that will estimate the
throughput and performance of each component in the application. The estimated throughput
and execution properties will be used to simulate the entire application and mathematically
calculate its performance under configurable load conditions with high accuracy.
We have previously proposed an extensible tool for simulating integrated Fog-BC applications,
called FoBSim [26]. FoBSim provides easy configuration through its Command Line Interface
(CLI) for selecting BC deployment model, data model, Consensus algorithm and application
model suitable for scenarios to be simulated. To test and evaluate our future proposed solutions
for addressing our research questions, we will build up and extend FoBSim so that different
realistic AI-BC-FC-SS integrated scenarios can be configured and simulated.
The current version of FoBSim allows to utilize the PoW, PoS and PoA consensus algorithms.
It allows for simulating different BC services namely Digital payments, Identity Management,
Smart Contracts and Data Management. Additionally, FoBSim allows for different deployment
models of the BC in the Fog tier or end-user tier, where massive number of fogs and edge devices
can be simulated using multithreaded interactive networks realized using the networkx library
and a message-driven approach. Specifically, we plan to enhance FoBSim implementation by
adding AI methods and deploy easy-to-modify smart components to FoBSim, represented by
adding a module to the โMinerโ component where AI code can be injected and deployed by AI
practitioners without the need of previous knowledge on the other technical aspects of FoBSim.
FoBSim miners then regularly check this module and run its code, while the tool automatically
presents its results (if applicable) at the end of each simulation run. Additionally, we plan to add
mobility properties to end-user devices, which is represented by adding a mobility module to the
โEnd-userโ and โFogโ components, similar to [27], which allows the simulation and evaluation of
system behaviour when end-users and fogs are mobile.
We also plan to add new consensus algorithms such as PoSign and pBFT, to FoBSim that are
more suitable for different BC models (permission -ed/-less) and DL models (e.g. DAG). This
extension will be represented by adding new consensus algorithms to the โConsensusโ Module
that can be selected through the simulation run. Furthermore, we plan to add new benchmark
methods that are needed for evaluating different SS infrastructure (Edge devices, Things, Fogs)
such as energy consumption and QoS. This extension will be represented by adding more
variables to the tool throughout the code, as well as allowing FoBSim users to configure the
simulated machines in terms of power consumption according to their roles. As the current
version of FoBSim is not able to simulate mining pools, and only allows the utilization of one
BC system that runs one predefined consensus algorithm, we plan to allow these by adding a
clustering module to the โNetworkโ component of FoBSim. The extension will allow sharding,
pooling, as well as utilizing different BCs that uses different consensus algorithms at the same
simulation run. Finally, we plan to utilize previous simulation tools to allow the provision of
realistic cloud scenarios where critical decisions need to be made for enhanced overall system
efficiency.
4. Future work
As our future work, we plan to extend the simulator to support complex models for simulating
the scheduling and provisioning process of the entire application. The simulation models
will adaptively respond to significant changes in the pool of available SDs (Cloud or Fog
instances) during application execution and identify provisioned devices that do not provide
good performance for a given smart application component. They will further enable the
replacement of low-performing SDs, e.g., provisioned as VMs or containers that no longer meet
the application requirements, or reconfigure existing ones (increase number of CPUs to a VM
running).
The application scheduling and provisioning models will enable the simulation of decentral-
ized data-aware resources scheduling over multiple control and network domains with increased
trust. The model will utilize the transaction logs, stored in the simulated BC, to manage the
SDs in an efficient manner. The approach will use semantics to describe the simulated SDs and
check their compatibility through an Application Definition Machine (ADM). The ADM will
describe the recommended resources for a given smart application.
We also plan to model different migration techniques to provide accurate simulation of the
deployment and communication overhead for using smart devices from multiple providers. In
case of over-provisioning, we will simulate the release or downgrading of resources to minimize
the overall resource consumption without violating application requirements. Finally, the
model will enable the simulation of resources provisioning through ADM for each individual
application defined in the simulator.
We aim to focus our research on smart applications related to the prevention of virus spreading
or to the management of societal problems, such as travel restrictions caused by the pandemic.
The vast majority of such applications are mainly centralized and non-smart, which makes
them carry single-point-of-failure, privacy, high latency, and legal issues, along with the lack
of efficient handling of mobile SDs. The adoption and mass acceptance of such applications,
e.g. COVID-19-related applications, are greatly hindered by the general lack of trust associated
with the nature of tracing apps, and the reluctance of people to share their personal data. To
overcome these issues, we need to revise current solutions, and design methods addressing
privacy-preserving, privacy-awareness, explainability and interoperability.
5. Conclusions
Integrating the Blockchain technology with smart applications for managing data of smart
devices can enhance the management of current complex systems. In this paper, we proposed
Blockchain-integration possibilities for smart systems to support the efficient and secure execu-
tion of smart applications. As the heart of our solution, we envisioned a Blockchain-enabled
simulation framework capable of analysing the integration possibilities with fog and cloud
infrastructures at different layers of smart systems. Such a framework will be able to model and
analyse the behavior of Blockchain networks in large-scale fog-enhanced smart systems, while
using different AI methods.
Acknowledgments
The research leading to these results is partially supported by the European COST programme
under action identifier CA19135 (CERCIRAS), and by the National Research, Development
and Innovation Office within the framework of the Artificial Intelligence National Laboratory
Programme.
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