
fuel level, and diagnostic fault codes, is collected via
the OBD port, supplemented by GPS-based location
tracking. Cabin environment data can also be inte-
grated for enhanced analysis. In our setup, an OBD-
CAN device paired with a GPS receiver was used to
collect these parameters. While dynamic metrics like
speed and RPM reflect vehicle status, fault codes pro-
vide critical insights for predictive maintenance. By
connecting the OBD-CAN device and GPS module
to a mobile phone (Mobile Computing) and establish-
ing a Vehicle Gateway (VG1/VG2) connected to the
cloud, a complete IoT system was created. Using the
CCP-IoT-RA framework, this system was further ex-
tended to support advanced telematics use cases. The
experimental setup is illustrated in Figure 4.
5.2 Application of Framework and
Systematic Approach
In this section we describe the the application of
RDCT approach which is derived from the Frame-
work for System Design.
• System Level Use Cases: We design the follow-
ing system level use cases for our experiment
(a) Driver Behaviour: All over-speeding, over-
acceleration, hard-braking, and engine revving
events are to be computed from the data from
OBD and reported to Fleet Manager and notified
to the Driver in real-time
(b) Vehicle Behaviour: All fault codes broad
casted on the OBD are to be collected, parsed and
the diagnostics information must be indicated to
the driver and the fleet manager. Abnormality in
coolant temperature, rate or fuel consumption is
also to be computed and indicated.
(c) Fleet Manager: Primary aim of the fleet man-
ager is to track fleets, connect with the driver at re-
quired intervals, monitor the driver behaviour and
vehicle behaviour.
• Computation Requirements: As per the use
cases for the system, the vehicle speed, engine
RPM, coolant temperature, fuel level, and faults
reported by the system are to be detected. Based
on the use case, computation is to be completed
example - rate of change of speed can indicate
over-acceleration, or hard-braking - which are to
be notified to the driver and the fleet manager.
These computations will need to be made in driver
cabin and/or at the cloud. In case of connectivity
issues (wireless connectivity to the cloud is lost),
all computation is to be achieved on Mobile or Ve-
hicle Gateway (Use of CCP).
• Data Processing Needs: Per the use case and
compurgation for vehicle and driver behaviour,
the data mat be processed on mobile, vehicle gate-
way or cloud.
• Data Storage Demands: In all cases, local stor-
age is required and further, opportunistically, the
data must be transmitted to a central store on the
cloud.
• Outcome Considerations: With the increased
need for safety and a need to keep the driver in-
formed, the processing on Mobile and Vehicle
Gateway will form the key to ensure that driver
is notified at all times.
• Quality Attribute Focus: A real-time perfor-
mance of the system to assist the driver is essential
in this case.
5.3 System Design Considerations
As outlined in (Joshi and Deshpande, 2025) and
(Joshi and Deshpande, 2024c), the authors have pre-
sented a comprehensive framework design along with
the software technologies utilized. Our implemen-
tation builds upon this foundation, adhering to the
same architectural principles and methodologies for
IoT system development.
5.4 Conclusion of the Experiment
Based on the validation criteria, the assessment is pro-
vided in Tables 3 to 8. (NT: Not Tested; NA: Not
Applicable)
By following the complete process, we establish
a structured mechanism for implementing an IoT sys-
tem using CCP while also enabling a systematic eval-
uation of the enhancements introduced by CCP. This
approach ensures that every stage of IoT system de-
velopment—from design to deployment—leverages
the benefits of collaborative computing. It becomes
evident that CCP serves as a foundational enhance-
ment to modern IoT architectures, providing greater
computational efficiency, scalability, and adaptability
in dynamic environments.
6 CONCLUSION
Building on the foundations of CCP, we established
key design considerations and core principles to
guide computation-centric, data-driven, and outcome-
focused system development.
Coupled with a standardized RDCT (Require-
ments, Design, Construction, Testing) methodology,
ICSOFT 2025 - 20th International Conference on Software Technologies
374