Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.

Fog Computing: A Taxonomy, Survey and Future Directions

Survey on fog computing

Human emotion recognition using deep belief network architecture

In recent years, in order to provide a better quality of service (QoS) to Internet of Things (IoT) devices, the cloud computing paradigm has shifted toward the edge. However, the resource capacity (e.g., bandwidth) in fog network technology is limited and it is essential to efficiently bind the IoT applications with stringent QoS requirements with the available network infrastructure. In this paper, we formulate a joint user association and resource allocation problem in the downlink of the fog network, considering the evergrowing demand of QoS requirements imposed by the ultra-reliable low latency communications and enhanced mobile broadband services. First, we determine the priority of different QoS requirements of heterogeneous IoT applications at the fog network by enforcing the analytical framework using an analytic hierarchy process (AHP). Using the AHP, we then formulate a two-sided matching game to initiate stable association between the fog network infrastructure (i.e., fog devices) and IoT devices. Subsequently, we consider the externalities in the matching game that occurs due to job delay and solve the network resource allocation problem by applying the “best-fit” resource allocation strategy during matching. The simulation results illustrate the stability of the user association and efficiency of resource allocation with higher utility gain.

/pdf/resource-allocation-for-ultra-reliable-and-enhanced-mobile-3k7exi7726.pdf

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

Large-scale Internet applications, such as content distribution networks, are deployed in a geographically distributed manner and emit massive amounts of carbon footprint at the data center. To provide uniform low access latencies, Cisco has introduced Fog computing as a new paradigm which can transform the network edge into a distributed computing infrastructure for applications. Fog nodes are geographically distributed and the deployment size at each location reflects the regional demand for the application. Thus, we need to control the fraction of user traffic to data center to maximize the social welfare. In this paper, we consider the emerging problem of joint resource allocation and minimizing carbon footprint problem for video streaming service in Fog computing. To solve the largescale optimization, we develop a distributed algorithm based on the proximal algorithm and alternating direction method of multipliers (ADMM). The numerical results show that our algorithm converges to near optimum within fifteen iterations, and is insensitive to step sizes.

http://networking.khu.ac.kr/layouts/net/publications/data/A%20Proximal%20Algorithm%20for%20Joint%20Resource%20Allocation%20and%20Minimizing%20Carbon%20Footprint%20in%20Geo-distributed%20Fog%20Computing.pdf

A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing

The notion of IoT has ignited umpteen possibilities of different heterogeneous devices to blend within a network. In IoT, especially sensor devices are mostly deployed in an extremely resource constrained environment and thus pose the necessity to extend the capability and life expectancy of these kind of devices in terms of energy consumption. Since, the devices in IoT have limited energy sources they often run on battery with a certain energy capability, the deployment of green communication and system model in IoT has been a core challenging issue. This paper addresses the energy efficiency issues across diverse IoT driven networks by proposing a system model for G-IoT and energy efficient scheme for the IoT devices to extend the life expectancy of the whole IoT network.

http://networking.khu.ac.kr/layouts/net/publications/data/A%20System%20Model%20for%20Energy%20Efficient%20Green-IoT%20Network.pdf

A system model for energy efficient green-IoT network

Fog computing, which performs on network edges, is a front-end distributed computing archetype of centralized cloud computing. Mobile Fog is a special purpose computing prototype, which leverages the mobile computing to deliver seamless and latency-aware mobile services. Offloading computation in mobile Fog is challenging because of the spatiotemporal resource requirements of heterogeneous mobile devices. In this paper, we propose reinforcement learning based code offloading mechanism to ensure low-latency service delivery towards mobile service consumers. We use the distributed reinforcement learning algorithm to offload basic blocks in a decentralized fashion to deploy mobile codes on geographically distributed mobile Fogs. We simulate the proposed prototype using OMNeT++ considering fluctuated resources of mobile Fog and varied service demands of mobile users. The proposed method significantly reduces the execution time and latency of accessing mobile services while ensuring lower energy consumption of mobile devices.

http://networking.khu.ac.kr/layouts/net/publications/data/ICOIN%206.pdf

Md. Golam Rabiul Alam

Papers

Human emotion recognition using deep belief network architecture

Resource Allocation for Ultra-Reliable and Enhanced Mobile Broadband IoT Applications in Fog Network

A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing

A system model for energy efficient green-IoT network

Multi-agent and reinforcement learning based code offloading in mobile fog