The Future of Low-End Motes in the Internet of Things: A Prospective Paper

Oliveira, Daniel; Costa, Miguel; Pinto, Sandro; Gomes, Tiago

doi:10.3390/electronics9010111

Cited by 20 publications

(11 citation statements)

References 74 publications

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“…In recent times, FPGA integrated embedded (hard or soft) processors cores into their existing architecture known as Field-Programmable Systems-on-Chips (FPSoCs). FPSoC are suitable implementation alternative to low-end IoT sensor nodes, as they offer advantages like scalability, flexibility, versatility, higher performance, low power solutions and secure infrastructure [172].…”

Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

et al. 2021

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Internet of things (IoT) is one of key pillars in fifth generation (5G) and beyond 5G (B5G) networks. It is estimated to have 42 billion IoT devices by the year 2025. Currently, carbon emissions and electronic waste (e-waste) are significant challenges in the information & communication technologies (ICT) sector. The aim of this article is to provide insights on green IoT (GIoT) applications, practices, awareness, and challenges to a generalist of wireless communications. We garner various efficient enablers, architectures, environmental impacts, technologies, energy models, and strategies, so that a reader can find a wider range of GIoT knowledge. In this article, various energy efficient hardware design principles, data-centers, and software based data traffic management techniques are discussed as enablers of GIoTs. Energy models of IoT devices are presented in terms of data communication, actuation process, static power dissipation and generated power by harvesting techniques for optimal power budgeting. In addition, this article presents various effective behavioral change models and strategies to create awareness about energy conservation among users and service providers of IoTs. Fog/Edge computing offers a platform that extends cloud services at the edge of network and hence reduces latency, alleviates power consumption, offers improved mobility, bandwidth, data privacy, and security. Therefore, we present the energy consumption model of a fog-based service under various scenarios. Problems related to ever increasing data in IoT networks can be solved by integrating artificial intelligence (AI) along with machine learning (ML) models in IoT networks. Therefore, this article provides insights on role of the ML in the GIoT. We also present how legislative policies support adoption of recycling process by various stakeholders. In addition, this article is presenting future research goals towards energy efficient hardware design principles and a need of coordination between policy makers, IoT devices manufacturers along with service providers.INDEX TERMS Fifth generation, Internet of Things, green Internet of Things, fog, machine learning.

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Section: Wireless Sensor Network (Wsn)mentioning

confidence: 99%

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

et al. 2021

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“…Some technologies are available to implement smart objects [5]. Microcontrollers (MCUs) are more flexible when it comes to application development.…”

Section: Smart Objectsmentioning

confidence: 99%

“…These intelligent objects are connected to embedded systems, equipped with microcontrollers or microprocessors, sensors and actuators, and communication units. The hardware that makes up a smart object is called a node, mote or end device [5] [6].…”

mentioning

confidence: 99%

“…Therefore, the ecosystem of pervasive applications depends on infrastructures and technologies capable of guaranteeing quality of service [3]. The devices have limited resources, mainly due to communication technologies used [5]. In addition, the IoT solutions proposed in a more comprehensive way are built with several different technologies making their design and management relatively complex tasks [6] [2].…”

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confidence: 99%

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Probabilistic Model for the Lifetime Prediction of IoT Devices

Correia

Alencar

Assis

2021

Preprint

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The development of new electronics and communications technologies allows the Internet of Things (IoT) concepts to be put into practice. A large amount of information is expected to travel over the internet from intelligent objects to many different kinds of software and cloud computing applications. However, there are still several challenges to be overcome. Among them, management of devices energy consumption is intriguing. Therefore, a model to estimate the range of probabilities of the energy spent by IoT equipment is presented in this work. The results include the formulation of probability distributions and discrete event simulations. The proposed method can be applied to design and evaluate IoT applications.

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“…As observed, the original floating-point CapsNet requires 27.217 MiB of memory only for storing its weights. Besides the known overhead imposed by floating-point operations in low-end MCUs, the memory footprint of the original CapsNet is prohibited for this class of devices, which typically feature memories in the order of hundreds of KiB or a few MiB [Oliveira et al, 2020, Silva et al, 2019. In contrast to the original CapsNet, which requires 4 bytes per parameter, the quantized version only requires 1 byte.…”

Section: Quantizationmentioning

confidence: 99%

Shifting Capsule Networks from the Cloud to the Deep Edge

Costa¹,

Costa²,

Gomes³

et al. 2021

Preprint

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Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects is preserved across the network. However, their complexity is mainly related with the capsule structure and the dynamic routing mechanism, which makes it almost unreasonable to deploy a CapsNet, in its original form, in a resource-constrained device powered by a small microcontroller (MCU). In an era where intelligence is rapidly shifting from the cloud to the edge, this high complexity imposes serious challenges to the adoption of CapsNets at the very edge. To tackle this issue, we present an API for the execution of quantized CapsNets in Cortex-M and RISC-V MCUs. Our software kernels extend the Arm CMSIS-NN and RISC-V PULP-NN, to support capsule operations with 8-bit integers as operands. Along with it, we propose a framework to perform post-training quantization of a CapsNet. Results show a reduction in memory footprint of almost 75%, with a maximum accuracy loss of 1%. In terms of throughput, our software kernels for the Arm Cortex-M are, at least, 5.70x faster than a pre-quantized CapsNet running on an NVIDIA GTX 980 Ti graphics card. For RISC-V, the throughout gain increases to 26.28x and 56.91x for a single-and octa-core configuration, respectively.

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The Future of Low-End Motes in the Internet of Things: A Prospective Paper

Cited by 20 publications

References 74 publications

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

Probabilistic Model for the Lifetime Prediction of IoT Devices

Shifting Capsule Networks from the Cloud to the Deep Edge

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