Context computing for internet of things
Journal of Ambient Intelligence and Humanized Computing
Context-aware computing is an ambient-intelligence environment for adapting to the situations around humans, to their surroundings, and their use of software and hardware. Through IoT-based sensing, context-aware computing connects a variety of information found in the real world to ambient intelligence. A variety of IoT devices, such as smartphones, tablet PCs, wearable devices, smart bands, smart sensors, cameras, microphones, and GPS devices, can be connected with each other to collect
... t-aware data of the user's surroundings in real time. Ambient intelligence turns real situations into information, and provides a user-friendly intelligence service using the information. The information from a real situation makes it possible to realize human-oriented decision making through the application of a variety of machine learning techniques, such as feature extraction, learning, and inference. Along with the information collected in real-time, from the underlying data such as user preferences or behavior pattern data are analyzed and learned to achieve adaptive decision-making in consideration of personal situations. The main issues of context-aware computing are the integration of the data collected from multiple data sources and the protection of personal information about the end-users. To address these issues, various studies have been conducted in the computer science area. The central issue is to introduce selected research papers which includes trends in topics like context computing for the internet of things, context computing for networks, ambient embedded systems, context software, ambient context computing, adaptive knowledge base systems, advancement in wireless technologies, ambient IoT contexts, hybrid networking systems, artificial intelligence, innovative applications of semantic computing, knowledge mining, big data analysis, and ambient intelligence. The first paper by Hakim et al. (2018) suggests a nonintrusive contextual dynamic reconfiguration process of an ambient-intelligence IoT system. They propose contextual dynamic reconstruction using an ambient architecture-level IoT framework with more flexibility and comfort of use. This study focuses on reconfiguring IoT systems using an evolution manager's structure, and focuses on reconfiguration steps using processing-context data and a decision-making process. In addition, it develops a smart home implementation based on ambient devices and a home gateway using the autonomic computing MAPE/K loop and the cisco packet tracer simulator. It monitors the progress of context data, analyzing, planning, and executing contextual dynamic reconfigurations. The second paper by develops a context-aware adaptive algorithm using ambient-intelligence, dynamic adaptive streaming over HTTP (DASH) in mobile edge computing (MEC). The developed algorithm aims to select the optimal streaming segment, and reduces network latency in adaptive MEC streaming. This study applies an MLP deep learning algorithm and the multilayer perceptron classifier in the intermediate roles between a core server and a client application. It guarantees high quality of service as well as consistent network connections by improving quality streaming and reducing network latency.