There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments Because of its multilayer structure, deep learning is also appropriate for the edge computing environment Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

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Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

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

Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in real time using deep learning or used to train deep learning models. However, deep learning inference and training require substantial computation resources to run quickly. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices and also provides additional benefits in terms of privacy, bandwidth efficiency, and scalability. This paper aims to provide a comprehensive review of the current state of the art at the intersection of deep learning and edge computing. Specifically, it will provide an overview of applications where deep learning is used at the network edge, discuss various approaches for quickly executing deep learning inference across a combination of end devices, edge servers, and the cloud, and describe the methods for training deep learning models across multiple edge devices. It will also discuss open challenges in terms of systems performance, network technologies and management, benchmarks, and privacy. The reader will take away the following concepts from this paper: understanding scenarios where deep learning at the network edge can be useful, understanding common techniques for speeding up deep learning inference and performing distributed training on edge devices, and understanding recent trends and opportunities.

https://ieeexplore.ieee.org/ielaam/5/8789751/8763885-aam.pdf

Deep Learning With Edge Computing: A Review

Burst segmentation : an Approach for Reducing Packet Loss in Optical Burst Switched Networks

We address the issue of contention resolution in optical burst switched networks, and we introduce an approach for reducing packet losses which is based on the concept of burst segmentation In burst segmentation, rather than dropping the entire burst during contention, the burst may be broken into multiple segments, and only the overlapping segments are dropped The segmentation scheme is investigated by simulation in conjunction with a deflection scheme, and it is shown that segmentation with deflection can achieve a significantly reduced packet loss rate

/pdf/burst-segmentation-an-approach-for-reducing-packet-loss-in-10lwl6zswe.pdf

Burst segmentation: an approach for reducing packet loss in optical burst switched networks

Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.

A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices e.g., smartphone. The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information e.g., food type and portion size from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network CNN-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets UEC-256 and Food-101 and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

We address the issue of providing quality-of-service (QoS) in an optical burst-switched network. QoS is provided by introducing prioritized contention resolution policies in the network core and a composite burst-assembly technique at the network edge. In the core, contention is resolved through prioritized burst segmentation and prioritized deflection. The burst segmentation scheme allows high-priority bursts to preempt low-priority bursts and enables full class isolation between bursts of different priorities. At the edge of the network, a composite burst-assembly technique combines packets of different classes into the same burst, placing lower class packets toward the tail of the burst. By implementing burst segmentation in the core, packets that are placed at the tail of the burst are more likely to be dropped than packets that are placed at the head of the burst. The proposed schemes are evaluated through analysis and simulation, and it is shown that significant differentiation with regard to packet loss and delay can be achieved.

/pdf/prioritized-burst-segmentation-and-composite-burst-assembly-1lr43635ru.pdf

Vinod M. Vokkarane

Papers

Burst segmentation : an Approach for Reducing Packet Loss in Optical Burst Switched Networks

Burst segmentation: an approach for reducing packet loss in optical burst switched networks

A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

Prioritized burst segmentation and composite burst-assembly techniques for QoS support in optical burst-switched networks