https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1007&context=aii_fac_pub

Machine Learning for Internet of Things Data Analysis: A Survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.

Machine learning for Internet of Things data analysis: A survey

With the widespread adoption of the Internet of Things (IoT), the number of connected devices is growing at an exponential rate, which is contributing to ever-increasing, massive data volumes. Real-time analytics on the massive IoT data, referred to as the “real-time IoT analytics” in this paper, is becoming the mainstream with an aim to provide an immediate or non-immediate actionable insights and business intelligence. However, the analytics network of the existing IoT systems does not adequately consider the requirements of the real-time IoT analytics. In fact, most researchers overlooked an appropriate design of the IoT analytics network while focusing much on the sensing and delivery networks of the IoT system. Since much of the IoT analytics network has often been taken as granted, the survey, in this paper, we aim to review the state-of-the-art of the analytics network methodologies, which are suitable for real-time IoT analytics. In this vein, we first describe the basics of the real-time IoT analytics, use cases, and software platforms, and then explain the shortcomings of the network methodologies to support them. To address those shortcomings, we then discuss the relevant network methodologies which may support the real-time IoT analytics. Also, we present a number of prospective research problems and future research directions focusing on the network methodologies for the real-time IoT analytics.

A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues

Understanding 'smart cities': Intertwining development drivers with desired outcomes in a multidimensional framework

The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people’s everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Robust camera pose and scene structure analysis for service robotics

The paper presents a joints trajectories planning strategy for a redundant manipulator. The control system modifies, in real-time, the robot manipulator joints references positions, during a transition, when an unexpected moving obstacle is getting closer to one of the robot components (links and joints). The position of the moving objects is determined using a digital image processing system. The control system determines if the moving obstacle and the arm robot are in collision using an interference detection method based on oriented bounding boxes. When the arm robot control system detects a collision, it modifies the arm robot joints trajectories in order to increase the distance between the robot and the obstacle and, in the same time, maintain the initial trajectory of the end-effecter. Computer simulation and experimental results are given for a 7 DOF redundant arm and demonstrate the collision avoidance capability.

Real-time collision avoidance for redundant manipulators

Using the CityPulse framework we developed a proof of concept application for Brasov public transportation company. The application provides route recommendations and incident notifications for the citizens who travel by bus. This is achieved by processing in real-time the data streams about bus arrivals in stations and the incidents reported by citizens. The proof of concept application was presented to the representatives of Brasov municipality and public transport company.

A public transportation journey planner enabled by IoT data analytics

Reducing the amount of lost and damaged perishable goods (food and medicines) during transportation and storage represents a substantial global challenge, which imply the implementation of cold chain monitoring at all levels of the supply chains. The main objective of this work is to develop an IoT enabled infrastructure, which supports the development of smart transportation specific applications. The proposed infrastructure contains an emulator, which can be used for emulating the conditions inside a truck during the transportation process and some CEP based tools for processing the data generated by the sensors.

Dan Puiu

Papers

CityPulse: Large Scale Data Analytics Framework for Smart Cities

Robust camera pose and scene structure analysis for service robotics

Real-time collision avoidance for redundant manipulators

A public transportation journey planner enabled by IoT data analytics

Predictive analytics based on CEP for logistic of sensitive goods