Edge Computing for the Internet of Things: A Case Study

Premsankar, Gopika; Francesco, Mario Di; Taleb, Tarik

doi:10.1109/jiot.2018.2805263

Cited by 490 publications

(229 citation statements)

References 46 publications

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“…In IoT applications like virtual and augmented reality based games which require real-time behavior with almost zero-delay can benefit from edge computing [157]. A distributed edge computing mechanism divides the whole task among different devices of the network and ICN instance name function networking (NFN) can improve the working of many ICN-IoT applications including smart-home and health, VANETs and smart grid [158].…”

Section: F Edge Computing (In-network Computation) and Cloud Computingmentioning

confidence: 99%

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Arshad

Azam

Rehmani

et al. 2019

IEEE Internet Things J.

180

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Information-Centric Networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current IP-address based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, caching the content at intermediate nodes to provide reliable, efficient data delivery and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoTs) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoTs attracts both industry and research communities. IoTs is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoTs by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN based caching, naming, security and mobility approaches for IoTs with appropriate classification. Furthermore, we present operating systems (OS) and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoTs.

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Section: F Edge Computing (In-network Computation) and Cloud Computingmentioning

confidence: 99%

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Arshad

Azam

Rehmani

et al. 2019

IEEE Internet Things J.

180

View full text Add to dashboard Cite

show abstract

“…However, the inability to linearly scale power with existing CMOS technology prompts us to look at neuromorphic computing architectures that can be used in edge devices and possibly useful for replacing hardware in cloud computing platforms. It is expected that in 2-5 years the edge computing technologies will be in the main stream [3], along with machine learning, Internet of Things (IoT) and smart electronics, mutually contributing to each other areas growth [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Neuromemristive Circuits for Edge Computing: A Review

Krestinskaya

James

Chua

2020

IEEE Trans. Neural Netw. Learning Syst.

207

109

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The volume, veracity, variability and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing.

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“…However, the number of IoT devices is expanding rapidly, and the massive amount of collected data is hard to manage by central clouds. The reasons are the massive workload on the IoT network, the cost of the communication infrastructure, the required energy for data transmission, and, more generally, reliability, latency, and privacy concerns [10]. The new trend of IoT devices is to be "smart" to make decisions on their own, without streaming all the raw data to the cloud [11].…”

Section: Introductionmentioning

confidence: 99%

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Wang

Magno

Cavigelli

et al. 2020

IEEE Internet Things J.

119

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The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-Vbased Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This paper also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

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Edge Computing for the Internet of Things: A Case Study

Cited by 490 publications

References 46 publications

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Neuromemristive Circuits for Edge Computing: A Review

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

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