In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.

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Windows Azure Storage: a highly available cloud storage service with strong consistency

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

All one needs to know about fog computing and related edge computing paradigms: A complete survey

https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1069&context=ele_facpubs

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

The ubiquitous deployment of various kinds of sensors in smart cities requires a new computing paradigm to support Internet of Things (IoT) services and applications, and big data analysis Fog Computing, which extends Cloud Computing to the edge of network, fits this need In this paper, we present a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities To secure future communities, it is necessary to build large-scale, geospatial sensing networks, perform big data analysis, identify anomalous and hazardous events, and offer optimal responses in real-time We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events These experimental results demonstrate the feasibility of the system's city-wide implementation in the future

A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities

A tutorial is provided on the performance evaluation of multiprocessor interconnection networks, to guide system designers in their design process. A classification of parallel/distributed systems is followed by a classification of multiprocessor interconnection networks. Basic terminology for performance evaluation is presented. The performance of crossbar interconnection networks, multistage interconnection networks, and multiple-bus systems is then addressed, and a comparison is made along them. >

Performance of multiprocessor interconnection networks

This paper presents a new disk I/O architecture composed of an array of a flash memory SSD (solid state disk) and a hard disk drive (HDD) that are intelligently coupled by a special algorithm. We call this architecture I-CASH: Intelligently Coupled Array of SSD and HDD. The SSD stores seldom-changed and mostly read reference data blocks whereas the HDD stores a log of deltas between currently accessed I/O blocks and their corresponding reference blocks in the SSD so that random writes are not performed in SSD during online I/O operations. High speed delta compression and similarity detection algorithms are developed to control the pair of SSD and HDD. The idea is to exploit the fast read performance of SSDs and the high speed computation of modern multi-core CPUs to replace and substitute, to a great extent, the mechanical operations of HDDs. At the same time, we avoid runtime SSD writes that are slow and wearing. An experimental prototype I-CASH has been implemented and is used to evaluate I-CASH performance as compared to existing SSD/HDD I/O architectures. Numerical results on standard benchmarks show that I-CASH reduces the average I/O response time by an order of magnitude compared to existing disk I/O architectures such as RAID and SSD/HDD storage hierarchy, and provides up to 2.8 speedup over state-of-the-art pure SSD storage. Furthermore, I-CASH reduces random writes to SSD implying reduced wearing and prolonged life time of the SSD.

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I-CASH: Intelligently Coupled Array of SSD and HDD

This paper presents a novel disk storage architecture called DCD, Disk Caching Disk, for the purpose of optimizing I/O performance. The main idea of the DCD is to use a small log disk, referred to as cache-disk, as a secondary disk cache to optimize write performance. While the cache-disk and the normal data disk have the same physical properties, the access speed of the former differs dramatically from the latter because of different data units and different ways in which data are accessed. Our objective is to exploit this speed difference by using the log disk as a cache to build a reliable and smooth disk hierarchy. A small RAM buffer is used to collect small write requests to form a log which is transferred onto the cache-disk whenever the cache-disk is idle. Because of the temporal locality that exists in office/engineering work-load environments, the DCD system shows write performance close to the same size RAM (i.e. solid-state disk) for the cost of a disk. Moreover, the cache-disk can also be implemented as a logical disk in which case a small portion of the normal data disk is used as the log disk. Trace-driven simulation experiments are carried out to evaluate the performance of the proposed disk architecture. Under the office/engineering work-load environment, the DCD shows superb disk performance for writes as compared to existing disk systems. Performance improvements of up to two orders of magnitude are observed in terms of average response time for write operations. Furthermore, DCD is very reliable and works at the device or device driver level. As a result, it can be applied directly to current file systems without the need of changing the operating system.

Qing Yang

Papers

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities

Performance of multiprocessor interconnection networks

I-CASH: Intelligently Coupled Array of SSD and HDD

DCD --- Disk Caching Disk: A New Approach for Boosting I/O Performance