Analysis of the Impact of Digital Technologies on Chinese Economic Growth

This study aims to analyze the impact of digital technologies on the Chinese economic growth from 2013 to 2018. The research takes into account five digital technologies namely cloud computing, artificial intelligence, robotics, big data and the internet of things. In order to evaluate the role of each of the five digital technologies on china economy, we run a linear regression model of each of them with the country GDP. the results reveal that despite difference in their level of significance, Cloud Computing, Artificial Intelligence, Robotics, Internet of Things and Big Data have a positive significant impact on the country GDP. Keywords: Digital technologies, cloud computing, artificial intelligence, robotics, big data, internet of things, Chinese economic growth. DOI: 10.7176/JESD/10-8-15 Publication date: April 30th 2019

IoT devices controlled using mobile apps

Internet of Things (IoT) shows no sign of slowing down, particularly in the field of mobile applications because many IoT devices can be controlled through an application on a smartphone. There is a clear intersection between the Internet of Things (IoT) and artificial intelligence (AI). IoT allows you to connect machines and use the data generated from these machines. Artificial intelligence is the simulation of intelligent behavior in different kinds of machines. Leading manufacturers like Samsung and Apple obviously participate in the rise of artificial intelligence. The implementation of artificial intelligence (AI) and Internet of Things within terminals with touch screen is spreading at lightning speed in smartphones. With the advantage of detecting objects in front of the camera of lowering energy consumption and better guaranteeing data security than the traditional approach in the cloud. In this paper, authors proposed and present a home automation system to connect artificial intelligence (AI) and internet of things (IoT) controlled with a smartphone. The IoT system proposed allow any user to manage his house on site or remotely to fight against any intrusion or other natural disasters (Wind, Erosion, etc ...) that can cause considerable damage. This solution based Raspberry Pi technology, consist to manage and monitor a home remotely without human intervention by automating the entire house

Context-aware Dynamic Discovery and Configuration of 'Things' in Smart Environments

The Internet of Things (IoT) is a dynamic global information network consisting of Internet-connected objects, such as RFIDs, sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future Internet. Currently, such Internet-connected objects or `things' outnumber both people and computers connected to the Internet and their population is expected to grow to 50 billion in the next 5 to 10 years. To be able to develop IoT applications, such `things' must become dynamically integrated into emerging information networks supported by architecturally scalable and economically feasible Internet service delivery models, such as cloud computing. Achieving such integration through discovery and configuration of `things' is a challenging task. Towards this end, we propose a Context-Aware Dynamic Discovery of {Things} (CADDOT) model. We have developed a tool SmartLink, that is capable of discovering sensors deployed in a particular location despite their heterogeneity. SmartLink helps to establish the direct communication between sensor hardware and cloud-based IoT middleware platforms. We address the challenge of heterogeneity using a plug in architecture. Our prototype tool is developed on an Android platform. Further, we employ the Global Sensor Network (GSN) as the IoT middleware for the proof of concept validation. The significance of the proposed solution is validated using a test-bed that comprises 52 Arduino-based Libelium sensors.Comment: Big Data and Internet of Things: A Roadmap for Smart Environments, Studies in Computational Intelligence book series, Springer Berlin Heidelberg, 201

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it

MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices

The Internet of Things (IoT) is part of Future Internet and will comprise many billions of Internet Connected Objects (ICO) or `things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. Collecting data from these objects is an important task as it allows software systems to understand the environment better. Many different hardware devices may involve in the process of collecting and uploading sensor data to the cloud where complex processing can occur. Further, we cannot expect all these objects to be connected to the computers due to technical and economical reasons. Therefore, we should be able to utilize resource constrained devices to collect data from these ICOs. On the other hand, it is critical to process the collected sensor data before sending them to the cloud to make sure the sustainability of the infrastructure due to energy constraints. This requires to move the sensor data processing tasks towards the resource constrained computational devices (e.g. mobile phones). In this paper, we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT middleware for mobile devices, that allows to collect and process sensor data without programming efforts. Our architecture also supports sensing as a service model. We present the results of the evaluations that demonstrate its suitability towards real world deployments. Our proposed middleware is built on Android platform

Privacy Aware Offloading of Deep Neural Networks

Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy. We propose a technique that obfuscates the data before sending it to the remote computation node. The obfuscated data is unintelligible for a human eavesdropper but can still be classified with a high accuracy by a neural network trained on unobfuscated images.Comment: ICML 2018 Privacy in Machine Learning and Artificial Intelligence worksho

Building microclouds at the network edge with the Cloudy platform

Edge computing enables new types of services which operate at the network edge. There are important use cases in pervasive computing, ambient intelligence and the Internet of Things (IoT) for edge computing. In this demo paper we present microclouds deployed at the networks edge in the Guifi.net community network leveraging an open extensible platform called Cloudy. The demonstration focuses on the following aspects: The usage of Cloudy for end users, the services of Cloudy to build microclouds, and the application scenarios of IoT data management within microclouds.Peer ReviewedPostprint (author's final draft

AIoT-based Sustainable Smart Supply Chain Framework

Purpose: Supply chains in today's global environment operate in a market that is increasingly complex and dynamic in nature. In such an environment, a stable supply chain to respond to drastic changes in customer needs becomes inevitable. Based on these studies, it is obvious that organizations operating in the field of supply chain should accelerate their focus on sustainability and use technologies such as "Internet of Things" (IoT) and artificial intelligence to achieve the organization's goal of creating sustainable processes. Methodology: The presence of the Internet of Things, along with artificial intelligence technology, has created Artificial intelligence of things (AIoT) technology, which gives the big data from the Internet of Things great power. In this research, an attempt has been made to study and analyze the key dimensions, components and indicators of the AIoT-based sustainable supply chain. Also, a conceptual framework for Padidar intelligent supply chain based on these evolving technologies is presented, which can help to understand the elements of this intelligent supply chain in order to optimize. Findings: A study of the literature shows that investing in this technology to achieve sustainable benefits is inevitable. In addition, the use of this technology due to networking and the presence of the Internet requires appropriate security solutions for information technology, a workforce with the required set of skills, sharing information in an integrated environment with business partners. Originality/Value: The generalities of AIoT-based sustainable supply chain components in general, and things can be added to different industries and behaviors. Understanding the dimensions of this framework can always help to implement it effectively