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

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

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

/pdf/a-comprehensive-survey-on-fog-computing-state-of-the-art-and-4fbh4oy5fy.pdf

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

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

This Letter reports on an ultraweak intrinsic Fabry-Perot interferometer (IFPI) array fabricated by a femtosecond (fs) laser for distributed sensing applications. Ultralow reflectors (<-60  dB) were obtained. IFPIs with different physical lengths showed identical temperature sensitivity (-1.5  GHz/°C). A distributed temperature sensing test was conducted. No crosstalk between IFPI elements in the array was observed, implying the device's utility as a distributed sensing system. The possibility of using smaller bandwidths for sensor interrogation was experimentally proven. A small-scale temperature distribution test was conducted on a continuously cascaded ultraweak IFPI array, demonstrating its high spatial resolution. The temperature detection limit of this system was measured to be less than 0.0667°C.

https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1009&context=ele_facpubs

Ultraweak intrinsic Fabry-Perot cavity array for distributed sensing.

To investigate the outcome and safety data of chemosaturation with percutaneous hepatic perfusion (CS-PHP) of melphalan in patients with liver-dominant metastatic uveal melanoma. This is a HIPAA compliant, IRB approved, retrospective study. A total of 28 CS-PHPs were performed in 16 individual patients (six men and ten women, median age 63.1 years [range 49.1 to 78.7 years], one to six CS-PHP procedures per patient) for treatment of liver-dominant metastatic uveal melanoma between June, 2015 and December, 2018. All patients received cross-sectional imaging at baseline and during follow-up. CS-PHP was performed with the Hepatic CHEMOSAT® Delivery System (Delcath Systems, Inc., NY, USA) facilitating extracorporeal filtration of hepatic blood for melphalan removal. Ideal body weight-adjusted melphalan doses were administered into the hepatic arteries. Serious adverse events (SAE), progression-free survival based on response criteria in solid tumors, and overall survival were noted. Survival data were analyzed using Kaplan-Meier estimates. Partial response after first CS-PHP was observed in nine patients (60%), stable disease in five patients (33%) and progressive disease in one patient (7%). Median overall survival was 27.4 months (95% CI 4.1 to 35.4 month) after first CS-PHP. Median progression-free survival was 11.1 months after first CS-PHP (95% CI 4.9 to 23.6 months). SAEs were observed in the majority of patients with most SAEs limited to grades one and two. Thirteen SAEs of grades three and four were observed in seven individual patients. No grade five SAE was observed. CS-PHP is an efficacious and safe treatment for patients presenting with liver-dominant metastatic uveal melanoma.

/pdf/chemosaturation-with-percutaneous-hepatic-perfusion-of-27nvwuud2r.pdf

Chemosaturation with percutaneous hepatic perfusion of melphalan for liver-dominant metastatic uveal melanoma: a single center experience.

This letter reports a fiber Bragg grating (FBG) for distributed sensing applications fabricated using single-mode optical fiber and a femtosecond laser and interrogated in the terahertz (THz) range. A theoretical model of device behavior was derived, which agreed well with experimentally observed device behavior. In order to investigate the utility of THZ FBGs (THz FBGs) as a sensing modality, temperature tests were conducted. The results demonstrated a sensitivity of −1.32 GHz/°C and a detection resolution of <0.0017 °C. A temperature distribution test was also conducted using a THz FBG, demonstrating its potential as a distributed sensing platform with high spatial resolution. The feasibility of interrogating THz FBGs using narrow interrogation bandwidths was also experimentally shown.

/pdf/terahertz-fiber-bragg-grating-for-distributed-sensing-4u3l1tbs0s.pdf

Gerald Hefferman

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

Ultraweak intrinsic Fabry-Perot cavity array for distributed sensing.

Chemosaturation with percutaneous hepatic perfusion of melphalan for liver-dominant metastatic uveal melanoma: a single center experience.

Terahertz Fiber Bragg Grating for Distributed Sensing