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robust infrastructure for traffic monitoring.
We aim to define a model for the smart surveil-
lance of traffic and identify possible issues that might
occur inside the system. We propose a framework
for monitoring the availability of the sensors, named
throughout the paper as nodes. We are interested also
in collecting the data offered by the nodes and inter-
pret it to understand the current situation of the traffic.
The monitoring framework ensures the availability of
the smart traffic services.
Monitoring is essential for understanding the per-
formance and evolution of the system. Tradition-
ally, it implies collecting data from running services
and assessing system performance in terms of service
availability, failures and anomalies. The heterogene-
ity and distribution of the sensors of a traffic system
over a wide geographical area has introduced a high
complexity for the monitoring solutions. The use of
a formal model covers the system analysis and design
phases of software development and leads to a pro-
posal which can be verified for its intended properties.
Model-driven engineering allows the stake-
holders to contribute at defining specific concepts
and entities. Natural language requirements, Unified
Modeling Language (UML) use cases, agile user sto-
ries are captured in models who are part of the soft-
ware development process. In the design phase func-
tional and non-functional properties are proposed and
validated. Spotting errors later in the development
process leads to higher costs for software projects.
The specification needs to encompass the behavior
of the monitors and abstract away from complex de-
tails. We favored the use of the ASM method in front
of other modeling techniques as, UML or Business
Process Model and Notation (BPMN), due to its abil-
ity to model multi-agent systems and to easily refine
specifications by replacing an action through multi-
ple parallel actions. In comparison with UML, ASMs
provide a more rigorous abstraction, that allows veri-
fication through model checking.
3 SYSTEM OVERVIEW
Traffic surveillance solutions consist of a large num-
ber of sensors deployed and communicating across
an LDS. The proposed monitoring framework is part
of an architecture model concerned with coordinating
numerous heterogeneous components. The architec-
ture of the whole system is expressed as an abstract
machine model as depicted in Fig. 1. The moni-
toring component is closely related to the execution
layer from where it extracts information and the adap-
tation layer, which uses information from it to bring
the system to a proper state. The diagnosis established
by the monitor focuses on three main aspects: failure
detection, assessment of availability and diagnosis of
network problems (failure of the communication pro-
cesses).
The ASM relies also on local storage for saving
important events and data. Monitoring information
is saved in terms of low- and high-level metrics in
the data storage, while meaningful operations (adap-
tation events, identification of problems) are stored in
the event database. A meta storage is used for sav-
ing additional information as for instance functions to
aggregate low-level metrics.
Robustness of the proposal is achieved by employ-
ing redundant monitors that can take over the tasks in
case of the misbehavior of running elements. There-
fore, each traffic sensor is assigned a set of monitors
to assess its status. The evaluation is carried out in a
collaborative way. When one of the monitors exhibits
a random behavior, it is stopped by the middleware
and replaced.
Moreover, the interaction of the monitoring and
adaptation layers enables the system to perform re-
configuration plans whenever any of the sensors faces
a problem. The monitoring framework submits the
collected data to the adapter whenever a problem oc-
curs. Afterwards, a plan to restore the system to a
normal working state is proposed and the monitors
perform a new evaluation that can indicate if the adap-
tation processes have been efficient.
3.1 Background on ASM
Our research focused on elaborating formal models
for monitoring the smart traffic solutions in terms of
ASMs, which allow capturing the requirements in ab-
stract specifications that can further be implemented.
The method offers system descriptions that can
be easily understood by the clients, as well as de-
velopers. ASMs have already been used in indus-
trial projects, in proofs of correctness of programming
languages (B
¨
orger and Stark, 2003) and in modeling
client-cloud interaction (Arcaini et al., 2016).
One of the main artefacts of ASMs is the ground
model, which reflects system’s requirements. It is ad-
vanced, through incremental refinements, to a written
specification, that can be simulated and validated be-
fore deployment.
Basic ASMs contain transition rules expressed as
if Condition then Updates, where the Condition is an
arbitrary predicate logic formula and the Updates are
defined as a set of assignments f(t
1
, ..., t
n
) := t. For
an update to be carried out successfully, consistency
must be ensured, meaning that for each location only
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