=Paper=
{{Paper
|id=Vol-2161/paper6
|storemode=property
|title=A Blockchain Infrastructure for the Semantic Web of Things
|pdfUrl=https://ceur-ws.org/Vol-2161/paper6.pdf
|volume=Vol-2161
|authors=Michele Ruta,Floriano Scioscia,Saverio Ieva,Giovanna Capurso,Agnese Pinto,Eugenio Di Sciascio
|dblpUrl=https://dblp.org/rec/conf/sebd/RutaSICPS18
}}
==A Blockchain Infrastructure for the Semantic Web of Things==
https://ceur-ws.org/Vol-2161/paper6.pdf
A Blockchain Infrastructure for the Semantic
Web of Things
Michele Ruta? , Floriano Scioscia, Saverio Ieva,
Giovanna Capurso, Agnese Pinto, and Eugenio Di Sciascio
Polytechnic University of Bari, via E. Orabona 4, Bari (I-70125), Italy
name.surname@poliba.it
Abstract. The Semantic Web of Things (SWoT) improves the Inter-
net of Things power by increasing resource representation capabilities
through knowledge management and reasoning technologies adapted from
the Semantic Web. This promotes information interoperability and de-
cision autonomy. Nevertheless, trust and reliability issues remain ba-
sically unsolved. Large-scale, decentralized and dynamic infrastructures
suffer from unpredictable volatility of nodes, which compromises resource
availability. Trust and coordination are still difficult. Blockchain is in-
creasingly used as a transactional data storage solution for distributed
ledgers. It enables trustless collaboration by enforcing smart contracts
and prevents data tampering by validating transactions through consen-
sus protocols. This paper proposes a blockchain framework for SWoT
contexts settled as a Service-Oriented Architecture. Nodes can exploit
smart contracts for registration, discovery and selection of annotated ser-
vices/resources. While semantic matchmaking enables relevant resource
retrieval with logic-based ranking and explanation features, blockchain
provides reliable transaction storage. A prototype has been developed by
enhancing the standard Hyperledger Iroha framework. Application areas
are discussed and experimental tests on a cluster of virtual nodes provide
early insight on effectiveness, performance and scalability.
Keywords: Semantic Web of Things · Distributed transactional sys-
tems · Blockchain · Smart contracts · Service-Oriented Architectures ·
Semantic matchmaking
1 Introduction
The Semantic Web of Things (SWoT) [10] vision extends the Semantic Web
initiative to the Internet of Things (IoT) by improving the representation capa-
bility of objects and environments through annotating them with semantically
rich languages. Knowledge representation models and languages defined for the
Semantic Web can provide the basic substrate for interoperable information mod-
eling and sharing in the IoT. Machine understandability of adopted formalisms
?
Corresponding author. Tel.: +39-339-635-4949; fax: +39-080-596-3410
SEBD 2018, June 24-27, 2018, Castellaneta Marina, Italy. Copyright held by the
author(s).
2 M. Ruta et al.
allows applying automated inferences so that heterogeneous micro-devices, each
conveying a small amount of information, can interact autonomously to provide
high-level services to users, via decision support and task automation.
Anyway, IoT suffers from unpredictability of node and resource availability,
due to the volatility of actors and appliances. This makes trust and coordination
management difficult and these limits are inevitably inherited by the SWoT:
their burden is particularly evident when reliable and trustworthy applications
are needed. Blockchain technology is interesting from this perspective. In con-
ventional distributed databases, a trusted intermediary is needed to guarantee
irreversibility (i.e., committed transactions cannot be altered or reverted) and
prevent censorship (i.e., all valid transactions are committed). Blockchain is a
data structure and protocol for trustless distributed transactional systems: they
avoid intermediaries by approving transactions through a distributed consensus
protocol, which guarantees no single node –or small group of colluding nodes–
can force the addition, removal or modification of data [2]. Blockchain could
incorporate SWoT approaches providing interesting possibilities for large-scale
distributed trustless systems. A SWoT blockchain basically amounts to a Service-
Oriented Architecture (SOA) for regulating registration, discovery and selection
operations. These tasks are intended as distributed and validated by consensus.
This paper proposes a framework for SWoT blockchain systems. A semantic-
based resource discovery layer is integrated in a basic blockchain infrastructure,
adding verifiable records for every single transaction. A distinguishing feature
of the systems is logic-based explanation of discovery outcomes, grounded on
non-standard inference services for semantic matchmaking [11]. The proposed
system preserves fundamental blockchain features, also when size increases. Par-
ticularly, the effective and secure structure of the chain is capable of detecting
erroneous or malicious changes on a transaction block, also in case of large
amounts of volatile nodes. Annotations are registered as assets on the chain and
non-standard inferences in [11] are executed by validating peers. The proposed
framework has been implemented and tested on the Hyperledger Iroha 1 plat-
form. It has been encapsulated in a cluster of Docker 2 containers to simulate a
large SWoT infrastructure. Experiments on the framework assess the feasibility
of the approach.
The remainder of the paper is reported hereafter. Section 2 recalls relevant
background, while a functional and architectural description of the proposed
framework is given in Section 3. Experimental results are provided in Section
4, followed by an overview of application areas for the approach in Section 5.
Conclusion closes the paper.
2 Background
In what follows some relevant background is surveyed in order to make the paper
self-contained.
1
https://www.hyperledger.org/projects/iroha
2
https://www.docker.com/
A Blockchain Infrastructure for the Semantic Web of Things 3
Block n-1 Block n Block n+1 Block n+2 Block n+3
Transactions Transactions Transactions
… Hash of block
n-1
Hash of block
n
Hash of block
n+1
…
Fig. 1. Blockchain structure
2.1 Blockchain
The blockchain technology was born in 2009 with Bitcoin, an open source plat-
form for electronic currency exploiting previous theoretical results on Proof-of-
Work consensus algorithms [13]. Bitcoin relies on blockchain as a ledger of cur-
rency transfers. In a blockchain, transactions approved in a given time window
are grouped in blocks. Figure 1 shows each block also contains a hash, com-
puted from both the content of the block and the hash of the previous block.
This forms a chain of blocks, preventing tampering even with old blocks without
node agreement. Transactions are validated through consensus algorithms where
all network nodes behave as peers, holding a copy of current blockchain status.
Many blockchain-based e-currencies have been proposed in latest years due
to the worldwide success of Bitcoin. Furthermore, finance, industry and research
communities have been increasingly experimenting with blockchain as a general-
purpose distributed database enabling practical implementations of Smart Con-
tracts (SCs) [12] i.e., programs encoding and enforcing multi-party agreements
and coordinated activities. While a trusted mediator is required in the origi-
nal SC concept –restraining real-world deployments– blockchain consensus-based
approach allows trustless collaboration of Decentralized Autonomous Organiza-
tions (DAOs) through parallel execution of SCs among network nodes. Every
SC-enabled blockchain can thus be seen as a general-purpose application plat-
form based on a distributed Virtual Machine (VM): emerging proposals include
proprietary platforms (e.g., Ethereum 3 ) and standardization efforts, such as the
Hyperledger 4 initiative steered by the Linux Foundation.
From a technical viewpoint, blockchain systems can be classified based on
the following core design decisions:
– Network access policy. A blockchain network is permissioned if a white-list
of allowed nodes exists and nodes are uniquely identified, or permission-less if
any node can join at any time, even anonymously.
– Consensus algorithm. Permission-less systems require stricter consensus
methods, such as Proof-of-Work, which is based on solving a cryptographic prob-
lem and guarantee data security unless a large portion of nodes is colluding to
subvert the blockchain. Permissioned systems –where each node is accountable–
may relax consensus constraints in order to reduce the computational load, by
selecting simpler algorithms. State-machine replication protocols such as Byzan-
tine Fault Tolerance (BFT) variants [13] are often adopted. The Iroha framework
3
Ethereum Project: https://www.ethereum.org/
4
Hyperledger: https://www.hyperledger.org/
4 M. Ruta et al.
adopted for the experiments of this paper uses a BFT consensus algorithm named
Sumeragi.
– Transaction model. In typical blockchain systems, assets are registered and
exchanged, so that at any time each node owns some assets in a given quan-
tity. In the Unspent Transaction Outputs (UTXO) model, a transfer from A to
B consumes (i.e., deletes) records for A’s spent assets and produces (i.e., adds)
new ones for B’s received assets. In the account-based model, instead, every node
has an account reporting all its assets, which is updated by transactions.
– Smart contract language. Blockchains can adopt any formalism for SC
specification and execution, such as procedural (imperative) languages or log-
ical (declarative) languages or automata [4]. Current proposals mostly adopt
computationally complete programming languages, either existing (e.g., Java in
the Iroha framework of Hyperledger, exploited in this work) or created for the
purpose (e.g., Ethereum’s Solidity).
Internet of Things (IoT) scenarios are expected to be among the main ap-
plication areas in the near future. Blockchain applications in IoT contexts are
mostly focused on supply chain [7] and Industry 4.0 [3]. The integration of Se-
mantic Web technologies in blockchain has been discussed e.g., in [3, 6], while [4]
proposed defeasible logics for SC definition and implementation. Nevertheless, to
the best of our knowledge this paper presents the first full blockchain framework
integrating logic-based resource discovery.
2.2 Semantic Web of Things
Knowledge representation and reasoning in the SWoT are formally grounded on
Description Logics (DLs), a family of logic languages in a decidable fragment of
First Order Logic [1]. Basic DL syntax elements are: concepts (a.k.a. classes),
standing for sets of objects; roles (a.k.a. object properties), linking pairs of ob-
jects in different concepts; individuals (a.k.a. instances), special named elements
belonging to concepts. Each DL has a specific set of logical constructors for com-
bining the above elements in concept and role expressions. Concept expressions
can be used in inclusion (a.k.a. subsumption) and definition (a.k.a. equivalence)
axioms, which model knowledge elicited for a given domain. A set of such axioms
is called Terminological Box (TBox), a.k.a. ontology. A set of individual axioms
(a.k.a. facts) constitutes an Assertion Box (ABox). TBox and ABox together
make up a Knowledge Base (KB).
The approach proposed in this paper leverages semantic matchmaking, i.e.,
the retrieval of the most relevant resources for a given request, where both re-
sources and requests are annotated with concept expressions w.r.t. a common
ontology T . Given a request R and a resource S, subsumption checks whether all
features in R are included in S; satisfiability checks whether any constraint in R
contradicts some specification in S. Unfortunately these classic inference services
enable only a Boolean “full match or no match” approach. This is inadequate in
complex scenarios, because full matches are rare and incompatibility is frequent
when dealing with detailed resource descriptions. Therefore, non-standard infer-
ences in [11] help to determine a semantic ranking of resources w.r.t. a request
A Blockchain Infrastructure for the Semantic Web of Things 5
and a logic-based explanation of outcomes:
– Concept Contraction: if request R and resource S are not compatible, Con-
traction determines which part of R is conflicting with S. If one retracts con-
flicting requirements in R, G (for Give up), a concept K (for Keep) is obtained,
representing a contracted version of the original request, such that K and S are
compatible w.r.t. T . G represents “why” R and S clash.
– Concept Abduction: when R and S are compatible but S is not a full match
for R, Abduction determines what should be hypothesized in S in order to com-
pletely satisfy R. The solution H (for Hypothesis) to Abduction is a concept
representing “why” the subsumption relation does not hold. H can be inter-
preted as what is requested in R and not specified in S.
If R and S are incompatible, the matchmaking process uses Contraction to ex-
tract the compatible part K and then Abduction to obtain the required HK
for reaching a full match. Furthermore, penalty functions are computed based
on the structure and number of elements in G and HK [11], defining a well-
founded semantic distance metric, which can be used to rank a set of resources
by relevance (i.e., semantic affinity) w.r.t. the request.
In SWoT contexts, computation resources of devices are strictly constrained
and require careful software design. Adding more constructors makes DL lan-
guages more expressive, but leads to an increase in computational complexity of
inference services, hence a tradeoff is needed. This paper refers to the moderately
expressive Attributive Language with unqualified Number restrictions (ALN )
DL, which grants polynomial complexity to all the above standard and non-
standard inferences.
3 Semantic-enhanced Blockchain for unpredictable
scenarios
The proposed framework defines a semantic-based discovery layer built upon a
standard blockchain system, retaining full backward compatibility.
3.1 Framework architecture
Figure 2 sketches the overall architecture, whose main features are reported
hereafter.
– Peer agents registered in the blockchain are identified by their public keys
and associated with accounts. Each agent can perform a semantic-based re-
source discovery in order to take ownership and transfer assets between ac-
counts.
– Assets are annotated w.r.t. a domain ontology. They can represent physical
or digital resources, as well as service instances. Peers can register assets
through specific transactions, and annotations are stored in the blockchain.
– Smart contracts are enhanced by exploiting non-standard inference ser-
vices described in Section 2.2. Particularly, each peer locally integrates an
6 M. Ruta et al.
Peer
Semantic Application Programming Interface
Blockchain
Smart Contracts
Peer Reasoning engine
Peer 1 Processor
N
Peer 2
Consensus
Storage
engine
Fig. 2. Framework architecture
embedded matchmaking and reasoning engine, allowing semantic-based re-
source discovery [11].
– Consensus engine tracks and validates semantic-based transactions in a
standard and transparent way.
– Storage for all transactions, including semantic ones, exploits Merkle trees,
a data structure commonly adopted in blockchains for efficient detection of
erroneous or malicious changes on a transaction block.
3.2 How to make smarter contracts: put semantics in the consensus
protocol
The SWoT blockchain proposal is intended as a SOA enabling fundamental
operations as registration, discovery, selection and final execution (payment).
Such tasks are implemented as smart contracts, i.e., their accomplishment is
distributed and validation is reached by consensus. The distinguishing feature of
the proposed approach w.r.t. basic blockchain infrastructures is the integration of
advanced resource/service discovery grounded on semantic matchmaking. It al-
lows computing a score measuring the semantic distance between the request and
each available chain resource, both described w.r.t. the same domain ontology.
This logic-based metric induces a relevance-ranked list of annotated elements
w.r.t. the request. The non-standard inference services also provide a formal
explanation of discovery outcomes, reinforcing user confidence in the discovery
process. SOA primitives and corresponding SCs are outlined hereafter.
A. Resource registration. Multiple resource domains can coexist and be
tracked in the same blockchain. Each domain is associated to a different ontology,
providing the reference conceptual vocabulary for annotating resources. Resource
instances are characterized by the following attributes:
– a Uniform Resource Identifier (URI) featuring the resource unambiguously
(and possibly denoting its fruition endpoint, although this is not mandatory);
A Blockchain Infrastructure for the Semantic Web of Things 7
– a semantic annotation in Web Ontology Language (OWL 2)5 describing the
resource;
– the URI of the reference ontology, in order to cope with the co-existence of
several resource domains in the same blockchain;
– a resource price: the framework supports any type of currency unit, pe-
cuniary or otherwise (e.g., in SWoT applications energy or time could be
chosen).
By means of the Registration SC the owner node will register a resource as an
asset on the blockchain storage, in order to make it available for discovery and
usage.
B. Resource discovery. In order to search for a (set of) item(s), the re-
quester randomly selects n peers and sends a multicast request specifying:
– URI of the reference ontology: this determines the resource domain, i.e.,
the vocabulary used to express annotations of request and resources to be
retrieved; the nodes receiving a request will not process resources annotated
w.r.t. other ontologies in the semantic matchmaking;
– OWL semantic annotation of the request, specifying desired resource features
and constraints;
– maximum price pmax the requester is willing to pay; resources with a price
higher than this threshold will be skipped from matchmaking in order to
reduce computational overhead;
– minimum semantic relevance threshold smin , as a floating-point number in
the [0, 1] range, where 1 corresponds to a full match and 0 to a complete
mismatch (both rare situations in realistic scenarios); after matchmaking,
resources with a relevance score below this threshold will not be returned,
as deemed irrelevant to the requester;
– maximum number of results rmax to be returned;
– requester’s address.
The proposal adopts a gossip-based (a.k.a. epidemic) approach [5] to dissemi-
nate discovery requests and aggregate results as in Figure 3. This grants protocol
simplicity and low computational overhead, which is a primary requirement in
SWoT contexts. Nodes receiving the request execute semantic matchmaking of
it with their own resources through an on-board matchmaking engine [11], im-
plementing the non-standard inference services in Section 2.2. A list of at most
rmax results satisfying both semantic relevance and price constraints is returned,
ranked by relevance. Nodes also select other n random peers and forward the
request. Nodes receiving forwarded requests behave in the same way, up to a
search depth threshold m. Each queried node returns results directly to the
original requester at the specified address.
C. Explanation. This is an optional step in a typical discovery process, in-
voked when a requester needs a justification of the matchmaking outcome. This
5
OWL 2 Web Ontology Language Document Overview (Second Edition), W3C Rec-
ommendation 11 December 2012, http://www.w3.org/TR/owl2-overview/
8 M. Ruta et al.
RequesterNode ReceiverNodeN ReceiverNodeN+1
:BlockchainNode :BlockchainNode :BlockchainNode
towards n peers
if m is not reached
discovery (ontologyURI,
semanticDescription, forward discovery (ontologyURI,
maxPrice, semanticDescription,
minSemanticRelevance, maxPrice,
maxResult, , minSemanticRelevance,
matchmaking on maxResult,
requesterNodeIP)
owned resources requesterNodeIP) matchmaking on
return resources ranked list owned resources
return resources ranked list
optional
explanation (resourceURI,
semanticDescription)
return semantic explanation
selection (resourceURI)
ACK
Fig. 3. Resource discovery and retrieval flow
can be useful, e.g., to trigger a request refinement process [9], as it can make the
requester aware of further features it did not include in its original request but it
may be interested in. The requester node sends a unicast request containing: (i)
the semantic annotation of the request; (ii) the URI of the discovered resource.
The receiving node replies with the matchmaking outcome explanation, struc-
tured as: (i) semantic affinity score in the [0, 1] interval; (ii) concept expressions
of G and K from Concept Contraction and of H from Concept Abduction. G, K
and H can be cached from the previous discovery step, if storage of the resource
owner allows it.
D. Resource selection. After receiving all results –or just a subset, if the
response delay of some nodes is greater than a fixed timeout– the requester selects
the best resource(s) by means of select SC. A unicast message is delivered to
the resource owner specifying the resource URI and a currency payment. The
recipient answers with a properly usable resource representation: this depends on
the actual kind of resource and meaning of the URI, e.g., an interface endpoint
to access a networked device or a further SC to be invoked. The proposal does
not constrain resource fruition in any way, leaving application-specific details to
the semantic annotation of the resources themselves.
The resource discovery and retrieval interaction sequence is shown in Figure
3. Each associated transaction is recorded on the blockchain for robustness,
traceability and accountability purposes.
4 Performance assessment
In order to assess effectiveness and scalability of the proposed approach, an ex-
perimental evaluation campaign has been carried out starting from the Iroha
framework from Hyperledger. The implemented prototype enhanced Iroha as in
A Blockchain Infrastructure for the Semantic Web of Things 9
what follows:
– the server API has been extended with support for semantic matchmaking;
– the SCs described in Section 3.2 have been implemented and the Mini-ME
reasoning and matchmaking engine [11] has been integrated;
– zlib 6 compression library has been exploited to cope with the well-known ver-
bosity of ontology languages as OWL.
In order to reach a quantitative performance analysis, small, medium and
large scale chain scenarios have been considered, respectively with 50, 150 and
500 nodes. In each of them nodes have been split in two sets materially executing
SC transactions with semantic-based discovery (see Section 3.2): producers, i.e.,
providers of annotated resources, registered in the blockchain; consumers, i.e.,
resource requesters.
The following experiment parameters have been set: (i) duration of 300 s;
(ii) consumer/producer ratio of 0.1; (iii) 20 randomly-generated annotations per
producer; (iv) each consumer sends a new randomly-generated request every 10 s;
(v) each request can be forwarded to 4 nodes at most; (vi) a request is aborted if
no match is found after 2 hops; (vii) the minimum threshold of semantic affinity is
0.9. Each scenario is executed several times by varying the following parameters:
(i) the discovery timeout has been set to 2, 6 and 10 s; (ii) the explanation SC,
described in Section 3.2, has been either enabled or disabled.
The experimental campaign has leveraged the adoption of Docker platform
to deploy the testbed, by performing the following steps:
– the prototype has been compiled as a Docker image, to create all the scenarios;
– each node has been executed as a container instance of the compiled image;
– a Docker Swarm mode cluster has been deployed on 6 VirtualBox virtual
machines running on a workstation7 , with an overlay network configured to
allow communications among the Iroha nodes;
– the execution of experiments has been managed via the Docker API SDK8 .
The following performance metrics have been calculated: (i) average request
processing time, both as all-out and split by task; (ii) average hit ratio per node,
i.e., percentage of requests with at least one resource satisfying the constraints
within the given timeout. Experimental results are reported later on.
Time. Average turnaround times can be deemed as very low in experiments
involving 50 and 150 nodes, as Figure 4 and Figure 5 show. the growth exhibits
a linear trend, suggesting proper scaling. With 500 nodes, instead, absolute time
reach the timeouts as depicted in Figure 6, due to the needed consensus about
among a larger number of entities. Furthermore, in all the experiments the time
of discovery process dominates the ones of explanation and selection phases.
This could be due to the fact that matchmaking is the most computationally
intensive task, despite the optimization of the adopted reasoning engine.
6
http://zlib.net/
7
Intel Xeon E5-2643 CPU at 3.30 GHz, 48 GB of RAM and Ubuntu 16.04 (64bit)
operating system.
8
https://github.com/spotify/docker-client
10 M. Ruta et al.
Fig. 4. Processing time for tasks on 50 Fig. 5. Processing time for tasks on 150
nodes nodes
Fig. 6. Processing time for tasks on 500 Fig. 7. Average hit ratio depending on
nodes timeout
Hit ratio. Figure 7 shows average results are closely related to the number
of nodes. The best outcomes have been obtained in the small and medium sce-
narios with timeouts high enough. Conversely, in the 500 nodes case the average
hit ratio is noticeably lower. The increased resource miss ratio is partially due to
Docker Swarm deployment on a single host instead of adopting a more proper
cluster computing environment: the large number of containers on the same host
led to Docker resource contention issues affecting the CPU, file system and net-
work load. Furthermore, the inherent complexity of consensus algorithms tends
to increase at higher scales, leading to higher processing times and consequent
increased probability of timeout expiration.
Early results supported the feasibility of the proposal, as performance is ba-
sically satisfactory for small-to-medium permissioned blockchains. Larger-scale
scenarios could not be setup on the reference testbed due to the above limits
with Docker Swarm deployment in a single-host environment. Testbed migration
toward a computer cluster will be performed to re-evaluate semantic-enhanced
blockchain performance in the same scenarios as well as to allow larger-scale
simulations.
A Blockchain Infrastructure for the Semantic Web of Things 11
5 Applications
Semantic-enhanced blockchain systems enable discovery infrastructures for general-
purpose machine-to-machine trustless marketplaces with minimal or no human
intervention across multiple DAOs. This has several possible applications with
potential transformation impact on relevant sectors.
– Logistics. Asset tracking and supply chain are among the most popular
blockchain applications, due to the easy fit with existing industry standards.
The simplest approaches rely on transactional ledgers for asset transfer [2].
Semantic-enhanced SC-based blockchains further allow any application logic to
be implemented, and also support discoverable, composable and verifiable multi-
step business processes in multi-party SOAs [8].
– Industry 4.0. IoT-based manufacturing benefits from blockchain technolo-
gies [2], granting not only a decentralized collaboration infrastructure, but also
a ledger for process traceability of production and quality assurance. Semantic-
based blockchain evolution can provide greater composition flexibility and rig-
orous process formalization.
– Utility markets. Energy, water and natural gas provisioning are increasingly
relying on sensor networks, low-level digital control and high-level decision sup-
port. Semantic-enhanced blockchains can strongly support the Smart Grid, by
providing both resource discovery and a robust ledger for contracts and pay-
ments, which are needed in large-scale peer-to-peer decentralized marketplaces.
– Public sector. Many public services can be made faster, cheaper and less
error prone through process and data dematerialization. Blockchain technology
can assist in the interfacing of the information systems of several independent
branches and levels of the public administration. Furthermore, it plays the role
of verifiable registry in property transfers as well as authentication and notary
services. Semantic-based querying capabilities make information and function-
alities more accessible to both citizens and decision-makers.
– Financial services. Traditional banks and finance institutions, private and
public alike, are experimenting with blockchain technology to reduce operating
costs of financial transactions management. Semantic-based approaches enable a
marketplace of financial services, where atomic building blocks can be automat-
ically discovered, compared and composed in order to provide the most suitable
personalized solutions.
6 Conclusion
The paper proposed a framework redesigning resource discovery for SWoT sce-
narios thanks to an underlying blockchain infrastructure. Registration, discovery,
selection and finalization tasks have been revisited as smart contracts with op-
portunistic and distributed execution, exploiting validation by consensus. Logic-
based explanation of discovery outcomes is an important feature of the proposal,
granted by non-standard inferences for request-resource matchmaking.
Future aims are basically directed to migrate the testbed toward a cluster
of physical nodes, in order to remove bottlenecks biasing results and increase
12 M. Ruta et al.
simulation scale. Development of case studies within the envisioned application
areas is at an early stage and will be completed to fully validate benefits and
possible limitations of the proposal in realistic settings.
Acknowledgements
This work was supported in part by Italian PON project ERHA (Enhanced
Radiotherapy with HAdrons).
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