D.3.3 ALGORITHMS FOR INCREMENTAL ... - SecureChange

4 F. Massacci and L.M.S. Tran
quantitative metrics, max belief and residual risk (§6), to support decision makers
in selecting optimal configuration for the enterprise model in practical scenarios
of evolution (§7). For our concrete instantiation with goal models we also present
an incremental algorithm to calculate the two proposed metrics (§8). Finally, we
discuss related works (§9) and conclude the paper (§10).
2 Air Traffic Management Case Study
In order to make the discussion more concrete we present here a case study on the
Air Traffic Management (ATM) system that we have carried out in the framework
of the EU project SECURECHANGE [28]. The focus in this paper is the deployment
of the Arrival Manager (AMAN) into an air traffic control room. AMAN,
DMAN (Departure Manager), and SMAN (Surface Manager) are queue management
tools, introduced by the SESAR Open Sky initiative. These support tools will
substitute some human activities to deal with increasingly traffic loads at terminals,
while still guaranteeing better performance, efficiency, and safety.
As the name suggests, the AMAN is a ground-based tool suggesting to the Air
Traffic Controllers (ATCO) an optimal arrival sequence of aircrafts and providing
support in estimating the optimal aircraft approach route. The critical mission of
AMAN is to support ATCO to manage the arrival traffic safely and efficiency, i.e.
by maintaining an appropriate separation between aircrafts. To achieve this high
level objective, several processes and activities are performed. Here we only focus
on two of them, applying an optimal arrival sequence, and collaborating with
other units.
AMAN calculates an optimum arrival sequence considering many constraints
such as flight profiles, aircraft types, airspace and runway condition, inbound flow
rate, as well as meteorological conditions. The final sequence is approved by AT-
COs. Then, the AMAN generates various kinds of advisories for ATCO to send to
pilots e.g., time to lose or gain, top-of-descent, track extension, while their execution
is continuously monitored to react to eventual violations. The sequence and
other relevant data are exchanged with adjacent sectors to improve collaboration
and reduce conflicts.
Fig. 1 summarizes these objectives in a goal model. We will define it formally
in later section but the intuitive understanding is that in order to achieve the goal
depicted in a node we need to achieve the goals described in its children. In the
ATM domain they are sometimes called influence diagrams.
The ATM operational environment, however, is continuously evolving due to
numerous causes such as the rapidly increasing of traffic load, the new performance
and safety standards, as well as the need of tighter collaboration between
ATM systems. To efficiently cope with evolutions, potential changes must be foreseen,
for instance:
– AMAN should support what-if-probing and online simulation.
– AMAN should support trajectory prediction, i.e. Expected Time of Arrival
– AMAN should support advanced advisories generation: heading, speed instructions.