CONSENSUS-BASED DATA MANAGEMENT WITHIN FOG COMPUTING FOR THE INTERNET OF THINGS

Description

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected
and interacting devices. This infrastructure involves a new generation
of service delivery models, more advanced data management and policy schemes,
sophisticated data analytics tools, and eective decision making applications. IoT
technology brings automation to a new level wherein nodes can communicate and
make autonomous decisions in the absence of human interventions. IoT enabled
solutions generate and process enormous volumes of heterogeneous data exchanged
among billions of nodes. This results in Big Data congestion, data management,
storage issues and various ineciencies. Fog Computing aims at solving the issues
with data management as it includes intelligent computational components and
storage closer to the data sources. Often, an IoT-enabled infrastructure is shared
among many users with various requirements. Sharing resources, sharing operational
costs and collective decision making (consensus) among many stakeholders
is frequently neglected. This research addresses an essential requirement for adaptive,
autonomous and consensus-based Fog computational solutions which are able
to support distributed and in-network schemes and policies. These network schemes
and policies need to meet the requirements of many users. In this work, innovative
consensus-based computational solutions are investigated. These proposed solutions
aim to correlate and organise data for eective management and decision making in
Fog. Instead of individual decision making, the algorithms aim to aggregate several
decisions into a consensus decision representing a collective agreement, beneting
from the individuals variant knowledge and meeting multiple stakeholders requirements.
In order to validate the proposed solutions, hybrid research methodology is
involved that includes the design of a test-bed and the execution of several experiments.
In order to investigate the eectiveness of the paradigm, three experiments
were designed and validated. Real-life sensor data and synthetic statistical data was
collected, processed and analised. Bayesian Machine Learning models and Analytics
were used to consolidate the design and evaluate the performance of the algorithms.
In the Fog environment, the rst scenario tests the Aggregation by Distribution
algorithm. The solution contribute in achieving a notable eciency of data delivery
obtained with a minimal loss in precision. The second senario validates the merits of
the approach in predictng the activities of high mobility IoT applications. The third
scenario tests the applications related to smart home IoT. All proposed Consensus
algorithms use statistical analysis to support eective decision making in Fog and
enable data aggregation for optimal storage, data transmission, processing and analytics.
The nal results of all experiments showed that all the implemented consensus
approaches surpass the individual ones in dierent performance terms. Formal results
also showed that the paradigm is a good t in many IoT environments and
can be suitable for dierent scenarios when applying data analysis to correlate data
with the design. Finally, the design demonstrates that Fog Computing can compete
with Cloud Computing in terms of accuracy with an added preference of locality

Period	5 Sep 2019 - 17 Sep 2019
Examinee
Degree of Recognition	National