Autonomic Aspects of IoT Based Systems: A Logistics Domain Scheduling Example - Interoperability and Open-Source Solutions for the Internet of Things - page 150

develop applications that can optimize the use of existing trac resources and
infrastructure. More and more trac monitoring and control devices (such as
loop detectors, radars, trac surveillance cameras, smart trac lights, dynamic
signs, weather sensors, etc.) are connected to the Internet and in this way they
form a rich Internet of Things (IoT) environment. But such a vast amount of
devices (sensors and actuators) doing real-time data reporting pose a new range
of problems regarding:
- data collecting, filtering and processing, in other words transforming data into
useful information;
- decision making about the actions that must be performed using the derived
information;
- actions execution.
The overall paradigm that is able to cover the needs of such environment
is inspired by the research area of Autonomic Computing, which has greatly
increased over the course of the last ten years the common understanding on
how to realize systems with self-managing capabilities. The main steps of such
feature pack are inspired in its high-level design by the MAPE-K (Monitor,
Analyse, Plan, Execute - Knowledge) loop, which is one key conceptual aspect
of the Autonomic Computing field [ 6 ].
A logistics domain application based on IoT paradigm and autonomic com-
puting is presented in this paper. We have considered a logistics scenario, where
at the outskirts of a city several depots are located which provide construction
materials and equipments for several construction sites located inside the city.
The transport company minivans are responsible for delivering the construction
materials. The application dynamically reconfigures the routes of the minivans
based on the trac conditions. In order to generate data from the smart city
IoT environment, we have used CoReMo (Constraints Responsive Mobility) soft-
ware, which is based on Repast Symphony multi-agent system [ 7 ]. The dynamic
reconfiguration system is based on the MAPE-K autonomic loop. For the analy-
sis phase we have used Esper complex event processing engine [ 8 ] and the planing
phase is ensured by CHOCO constraints solver [ 9 ].
Presented experimental work is based entirely on open source platforms.
There is no dependency regarding any particular technological option and each
individual step in the autonomic loop can be easily made interoperable with
equivalent implementation.
In the following part of this section are briefly presented the main concepts
and paradigms used in this paper, as follows: autonomic computing, complex
event processing and constrains satisfaction problem. The Chap. 2 presents the
scenario used to test the proposed solution. The architecture description and
the implementation details are included in Chap. 3. The paper conclusions are
documented in Sect. 5 .