A Model To Manage Smart Devices In Mobile Sensing Applications

Abstract

The growth in the number and complexity of new smart devices has been exponential in recent years. With the increasing understanding and application of artificial intelligence and machine learning, smart devices have been used in creating new opportunities for intelligent solutions that can enable services suited for smart cities, autonomous systems and ubiquitous systems monitoring and control.
Smart devices, including mobile devices, usually have a small-scale factor and have limited space for batteries, computing, and memory resources. This places a demand for such devices to strictly manage the use of resources to remain in operation for a longer period. In current and upcoming applications of smart devices, such as in the IoT, a network of devices, commonly referred to as a wireless sensor network, needs to gather data by sensing, computing the data, and reporting the information to a base station. Often these data is huge in size and transmitting all the data to the base station would drain the devices of their limited resources. However, the consumption of resources within the device is directly related to the communication and routing algorithm used across the network by each device. Thus, to improve the network’s performance through extending its lifetime and addressing more applications than it was specifically built for, the network needs to be sensitive to changes in the context of the application and be able to dynamically select the appropriate routing algorithm to apply based on various performance objectives.
The aim of this research involved the investigation and analysis of the problem, including a study of relevant literature and supporting theory, and culminated in the development of such an adaptive model that can dynamically manage a set of smart mobile devices. It included the investigation of the behaviour of a set of smart devices and their data management approach, while identifying the factors that determined their performance metrics. Metrics considered included energy consumption, bandwidth, and latency. With this knowledge as foundation, an adaptive model with capability to dynamically determine the optimal data management approach in a collection of devices was designed, developed, and evaluated. Various unique single and complex scenarios (scenarios with more than one application running) were used in an evaluation of the model and the results of this process proved that the model outperformed the current state of the art.

Date of Award	9 Nov 2021
Original language	English
Awarding Institution	Edge Hill University
Supervisor	Nik Bessis (Director of Studies) & YANNIS KORKONTZELOS (Supervisor)