doctoralThesis
Edge-distributed stream processing for video analytics in smart city applications
Fecha
2021-03-31Registro en:
ROCHA NETO, Aluízio Ferreira da. Edge-distributed stream processing for video analytics in smart city applications. 2021. 118f. Tese (Doutorado em Ciência da Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2021.
Autor
Rocha Neto, Aluízio Ferreira da
Resumen
Emerging Internet of Things (IoT) applications based on distributed sensors and machine
intelligence, especially in smart cities, present many challenges for network and processing infrastructure. For example, a single system with a few dozen monitoring cameras is
sufficient to saturate the city’s backbone. Such a system generates massive data streams
for event-based applications that require rapid processing for immediate actions. Finding a missing person using facial recognition technology is one of those applications that
require immediate action at the location where that person is since this location is perishable information. An encouraging plan to support the computational demand for widely
geographically distributed systems is to integrate edge computing with machine intelligence to interpret massive data near the sensor and reduce end-to-end latency in event
processing. However, due to the limited capacity and heterogeneity of the edge devices,
distributed processing is not trivial, especially when applications have different Quality
of Service (QoS) requirements. This work presents an edge-distributed system framework
that supports stream processing for video analytics. We investigate recent researches regarding massive IoT data stream processing, primarily focusing on the division of this
processing in multiple types of tasks. Then, we propose an architecture to organize edge
and cloud nodes for running various functions in a collaboration schema to process multimedia data streams. A method of distributing workload on edge nodes for event-based
processing is also proposed, along with a scheme for reusing nodes that perform tasks of
interest to various applications, such as a facial recognition task, for example. We have also
developed an algorithm to allocate nodes with sufficient processing capacity to process the
flows demand while meeting the applications’ QoS requirements. Finally, the simulations showed that the distribution of processing across multiple edge nodes reduces latency and
energy consumption and further improves availability compared to centralized processing
in the cloud.